Based on the data of GF-1 and GF-2 in China, the freeze-thaw disaster distribution data of Qinghai Tibet project corridor is produced by using the deep learning classification method and manual visual interpretation and correction. The geographical range of the data is 40km along the Xidatan Anduo section of Qinghai Tibet highway. The data include the distribution data of thermokast lakes and the distribution data of thermal melting landslides. The dataset can provide data basis for the research of freeze-thaw disaster and engineering disaster prevention and reduction in Qinghai Tibet engineering corridor. The spatial distribution of freezing and thawing disasters within 40km along the Xidatan-Anduo section of Qinghai Tibet highway is self-made based on the domestic GF-2 image data. Firstly, the deep learning method is used to extract the mud flow terrace block from GF-2 data; Then, ArcGIS is used for manual editing.
NIU Fujun, LUO Jing LUO Jing
This phenological data is based on the MOD13A2 data of the Qinghai Tibet Plateau from 2000 to 2015 (with a temporal resolution of 16 days and a spatial resolution of 1km). The NDVI curve is fitted using the segmented Gaussian function in the TIMESAT software. The spring phenology, autumn phenology and the length of the growth season are extracted using the dynamic threshold method. The thresholds of spring phenology and autumn phenology are set to 0.2 and 0.7 respectively. The phenological data were masked. Among them, the mask rules are: 1) The maximum value of NDVI must be met between June and September; 2) The average value of NDVI from June to September shall not be less than 0.2; 3) The average NDVI in winter shall not exceed 0.3.
ZU Jiaxing , ZHANG Yangjian
The dataset is the remote sensing image data ofGF-1 satellite in the Qinghai-Tibet engineering corridor obtained by China High Resolution Earth Observation Center. After the fusion processing of multispectral and panchromatic bands, the image data with a spatial resolution of 2 m is obtained. In the process of obtaining ground vegetation information, the classification technology of combining object-oriented computer automatic interpretation and manual interpretation is adopted, The object-oriented classification technology is to collect adjacent pixels as objects to identify the spectral elements of interest, make full use of high-resolution panchromatic and multispectral data space, texture and spectral information to segment and classify, and output high-precision classification results or vectors. In actual operation, the image is automatically extracted by eCognition software. The main processes are image segmentation, information extraction and accuracy evaluation. After verification with the field survey, the overall extraction accuracy is more than 90%.
NIU Fujun
Data content: this data set is the historical archived satellite data of the domestic high score series (GF1 / 2 / 3 / 4) in the key river and lake research areas of the Qinghai Tibet Plateau from 2015 to 2020, which can cover the typical river and lake areas for effective monitoring. The time range of the data is from 2015 to 2020. Data source and processing method: the data are level 1 products. After equalizing radiation correction, the changes affecting the sensors are corrected by the equalizing functions of different detectors. Some data are based on the Landsat 8 images in the same period as the base map, and control points are selected for geometric correction of the images. Then, orthophoto correction is carried out based on DEM data, and band fusion processing is carried out for the corresponding data. Data quality description: the Gaofen series satellites are processed by the China Resources Satellite Application Center. There are raw data received by the satellite ground receiving station of the Chinese Academy of Sciences and processed products at all levels. Among them, level 1a (pre-processing level radiometric correction image product): image data processed by data analysis, uniform radiometric correction, noise removal, MTFC, CCD splicing, band registration, etc; And provide RPC files for satellite direct attitude orbit data production. Refer to the data website of China Resources Satellite Application Center for details. Data application achievements and prospects: the data are domestic high-resolution data with high resolution, which can be used to monitor the changes of the Qinghai Tibet Plateau as a water tower in Asia and the generated images, and test the accuracy of other data in the region
QIU Yubao
The normalized difference vegetation index (NDVI) can accurately reflect the surface vegetation coverage. At present, NDVI time series data based on spot / vegetation and MODIS satellite remote sensing images have been widely used in the research of vegetation dynamic change monitoring, land use / cover change detection, macro vegetation cover classification and net primary productivity estimation at various scales. Evi is similar to the normalized difference vegetation index (NDVI) and can be used to quantify vegetation greenness. However, evi corrects for some atmospheric conditions and canopy background noise and is more sensitive in areas with dense vegetation. It contains an "L" value to adjust the canopy background, a "C" value as the atmospheric drag coefficient, and a value from the blue band (b). These enhancements allow the ratio between R and NIR values to be calculated exponentially while reducing background noise, atmospheric noise and saturation in most cases. This research work mainly focuses on post-processing NDVI and evi data, and gives a more reliable vegetation situation of the Qinghai Tibet Plateau in 2013 and 2018 through transformation of projection coordinate system, data fusion, maximum value synthesis method, elimination of outliers and clipping. The spatial resolution of the data is 0.05 °, and the temporal resolution is month.
YE Aizhong
Fractional Vegetation Cover (FVC) refers to the percentage of the vertical projected area of vegetation to the total area of the study area. It is an important indicator to measure the effectiveness of ecological protection and ecological restoration. It is widely used in the fields of climate, ecology, soil erosion and so on. FVC is not only an ideal parameter to reflect the productivity of vegetation, but also can play a good role in evaluating topographic differences, climate change and regional ecological environment quality. This research work is mainly to post process two sets of glass FVC data, and give a more reliable vegetation coverage of the circumpolar Arctic Circle (north of 66 ° n) and the Qinghai Tibet Plateau (north of 26 ° n to 39.85 °, east longitude 73.45 ° to 104.65 °) in 2013 and 2018 through data fusion, elimination of outliers and clipping.
YE Aizhong
NDVI reflects the background effects of plant canopy, such as soil, wet ground, snow, dead leaves, roughness, etc., and is related to vegetation cover. It is one of the important parameters to reflect the crop growth and nutrient information. According to this parameter, the N demand of crops in different seasons can be known, which is an important guide to the reasonable application of N fertilizer. Correct NDVI (C-NDVI) is the value of NDVI after excluding the influence of climate elements (temperature, precipitation, etc.) on NDVI. Taking precipitation as an example, studies on the lag effect of precipitation on vegetation growth show that the lag time of precipitation effects varies in different regions due to differences in vegetation composition and soil types. In this study, we post-processed the MODIS NDVI data and firstly correlated the NDVI value of the current month with the precipitation of the current month, the average value of the precipitation of the current month with that of the previous month, and the average value of the precipitation of the current month with that of the previous two months to determine the optimal lag time. The NDVI was regressed on precipitation and air temperature to obtain the correlation coefficients, and then the corrected NDVI values were calculated by the difference between the MODIS NDVI and the NDVI regressed on climate factors. We corrected NDVI using climate data to give reliable vegetation correction indices for the circum-Arctic Circle (range north of 66°N) and the Tibetan Plateau (range 26°N to 39.85°N and 73.45°E to 104.65°E) for 2013 and 2018. The spatial resolution of the data is 0.5 degrees and the temporal resolution is monthly values.
YE Aizhong
Soil moisture is an important boundary condition of earth-atmosphere exchanges, and it has been defined as an essential climate variable by GCOS. Vegetation optical depth is a physical variable to measure the attenuation of vegetation in microwave radiative transfer model, and it has been proved to be a good indicator of vegetation water content and biomass. This dataset uses the multi-channel collaborative algorithm (MCCA) to retrieve both soil moisture and polarized vegetation optical depth with SMAP brightness temperature. The algorithm uses a self-constraint relationship between land parameters and an analytical relationship between brightness temperature at different channels to perform the retrieval process. The MCCA does not depend on other auxiliary data on vegetation properties and can be applied to a variety of satellites. The soil moisture product from this dataset includes the soil moisture content in the unfrozen period and the liquid water content in the frozen period. Both horizontal- and vertical-polarization vegetation optical depth are retrieved. So far as we know, it is the first polarization-dependent vegetation optical depth product at L-band. This dataset was validated by 19 dense soil moisture observation networks (9 core validation sites used by SMAP team and 13 sites not used by them), and the widely used soil climate analysis network (SCAN). It was found that ubRMSE (unbiased root mean square error) of MCCA retrieved soil moisture is generally smaller than that of other SMAP products.
ZHAO Tianjie, PENG Zhiqing , YAO Panpan, SHI Jiancheng
This data set is daily surface albedo product over Tibet plateau region from 2002 to 2020 with a spatial resolution of 0.00425°. The MODIS reflectance data product was used to retrieve the Extended Multi-Sensor Combined BRDF Inversion (EMCBI) Model which has coupled with topographic effects with assistance of a BRDF priori-knowledge. The daily BRDF was retrieved in a 5-day period to collect multi-angular information from MODIS observations. And then the daily albedo is estimated, where the black sky albedo was calculated at local noon. MODIS surface reflectance data (MOD09GA and MYD09GA) are downloaded from the official website. The albedo product is quality-controlled with better temporal and spatial continuity in Tibet plateau area. The validation results show that it meets the accuracy requirements of albedo application with higher precisions comparing to the other similar products. And thus, this product is useful for the long-term environmental monitoring and radiation energy budget research study.
YOU Dongqin, YOU Dongqin, TANG Yong, TANG Yong, TANG Yong, HAN Yuan HAN Yuan
Photosynthetically active radiation (PAR) is fundamental physiological variable driving the process of material and energy exchange, and is indispensable for researches in ecological and agricultural fields. In this study, we produced a 35-year (1984-2018) high-resolution (3 h, 10 km) global grided PAR dataset with an effective physical-based PAR model. The main inputs were cloud optical depth from the latest International Satellite Cloud Climatology Project (ISCCP) H-series cloud products, the routine variables (water vapor, surface pressure and ozone) from the ERA5 reanalysis data, aerosol from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) products and albedo from Moderate Resolution Imaging Spectroradiometer (MODIS) product after 2000 and CLARRA-2 product before 2000. The grided PAR products were evaluated against surface observations measured at seven experimental stations of the SURFace RADiation budget network (SURFRAD), 42 experimental stations of the National Ecological Observatory Network (NEON), and 38 experimental stations of the Chinese Ecosystem Research Network (CERN). The instantaneous PAR was validated at the SURFRAD and NEON, and the mean bias errors (MBEs) and root mean square errors (RMSEs) are 5.6 W m-2 and 44.3 W m-2, and 5.9 W m-2 and 45.5 W m-2, respectively, and correlation coefficients (R) are both 0.94 at 10 km scale. When averaged to 30 km, the errors were obviously reduced with RMSEs decreasing to 36.3 W m-2 and 36.3 W m-2 and R both increasing to 0.96. The daily PAR was validated at the SURFRAD, NEON and CERN, and the RMSEs were 13.2 W m-2, 13.1 W m-2 and 19.6 W m-2, respectively at 10 km scale. The RMSEs were slightly reduced to 11.2 W m-2, 11.6 W m-2, and 18.6 W m-2 when upscaled to 30 km. Comparison with the other well-known global satellite-based PAR product of the Earth's Radiant Energy System (CERES) reveals that our PAR product was a more accurate dataset with higher resolution than the CRERS. Our grided PAR dataset would contribute to the ecological simulation and food yield assessment in the future.
TANG Wenjun
Based on 11 well-acknowledged global-scale microwave remote sensing-based surface soil moisture products, and with 9 main quality impact factors of microwave-based soil moisture retrieval incorporated, we developed the Remote Sensing-based global Surface Soil Moisture dataset (RSSSM, 2003~2020) through a complicated neural network approach. The spatial resolution of RSSSM is 0.1°, while the temporal resolution is approximately 10 days. The original dataset covered 2003~2018, but now it has been updated to 2020. RSSSM dataset is outstanding in terms of temporal continuity, and has full spatial coverage except for snow, ice and water bodies. The comparison against the global-scale in-situ soil moisture measurements indicates that RSSSM has a higher spatial and temporal accuracy than most of the frequently-used global/regional long-term surface soil moisture datasets. In addition, although RSSSM is remote sensing based, without the incorporation of any precipitation data or records, its interannual variation generally conforms with that of precipitation (e.g., the GPM IMERG precipitation data) and Standardized Precipitation Evapotranspiration Index (SPEI). Moreover, RSSSM can also reflect the impact of human activities, e.g., urbanization, cropland irrigation and afforestation on soil moisture changes to some degree. The data is in ‘Tiff’ format, and the size after compression is 2.48 GB. The relevant data describing paper has been published in the Journal ‘Earth System Science Data’ in 2021.
CHEN Yongzhe, FENG Xiaoming, FU Bojie
Surface soil moisture (SSM) is a crucial parameter for understanding the hydrological process of our earth surface. Passive microwave (PM) technique has long been the primary choice for estimating SSM at satellite remote sensing scales, while on the other hand, the coarse resolution (usually >~10 km) of PM observations hampers its applications at finer scales. Although quantitative studies have been proposed for downscaling satellite PM-based SSM, very few products have been available to public that meet the qualification of 1-km resolution and daily revisit cycles under all-weather conditions. In this study, therefore, we have developed one such SSM product in China with all these characteristics. The product was generated through downscaling of AMSR-E and AMSR-2 based SSM at 36-km, covering all on-orbit time of the two radiometers during 2003-2019. MODIS optical reflectance data and daily thermal infrared land surface temperature (LST) that have been gap-filled for cloudy conditions were the primary data inputs of the downscaling model, in order to achieve the “all-weather” quality for the SSM downscaling outcome. Daily images from this developed SSM product have achieved quasi-complete coverage over the country during April-September. For other months, the national coverage percentage of the developed product is also greatly improved against the original daily PM observations. We evaluated the product against in situ soil moisture measurements from over 2000 professional meteorological and soil moisture observation stations, and found the accuracy of the product is stable for all weathers from clear sky to cloudy conditions, with station averages of the unbiased RMSE ranging from 0.053 vol to 0.056 vol. Moreover, the evaluation results also show that the developed product distinctly outperforms the widely known SMAP-Sentinel (Active-Passive microwave) combined SSM product at 1-km resolution. This indicates potential important benefits that can be brought by our developed product, on improvement of futural investigations related to hydrological processes, agricultural industry, water resource and environment management.
SONG Peilin, ZHANG Yongqiang
The data set mainly includes the ice observation frequency (ICO) of north temperate lakes in four periods from 1985 to 2020, as well as the location, area and elevation of the lakes. Among them, the four time periods are 1985-1998 (P1), 1999-2006 (P2), 2007-2014 (P3) and 2015-2020 (P4) respectively, in order to improve the "valid observation" times in the calculation period and improve the accuracy. The ICO of the four periods is calculated by the ratio of "icing" times and "valid observation" times counted by all Landsat images in each period. Other lake information corresponds to the HydroLAKEs data set through the "hylak_id" column in the table. In addition, the data only retains about 30000 lakes with an area of more than 1 square kilometer, which are valid for P1-P4 observation. The data set can reflect the response of Lake icing to climate change in recent decades.
WANG Xinchi
Land surface temperature (LST) is a key parameter in the study of surface energy balance. It is widely used in the fields of meteorology, climate, hydrology, agriculture and ecology. As an important means to obtain global and regional scale LST information, satellite (thermal infrared) remote sensing is vulnerable to the influence of cloud cover and other atmospheric conditions, resulting in temporal and spatial discontinuity of LST remote sensing products, which greatly limits the application of LST remote sensing products in related research fields. The preparation of this data set is based on the empirical orthogonal function interpolation method, using Terra / Aqua MODIS surface temperature products to reconstruct the lst under ideal clear sky conditions, and then using the cumulative distribution function matching method to fuse era5 land reanalysis data to obtain the lst under all-weather conditions. This method makes full use of the spatio-temporal information of the original MODIS remote sensing products and the cloud impact information in the reanalysis data, alleviates the impact of cloud cover on LST estimation, and finally reconstructs the high-quality global 0.05 ° spatio-temporal continuous ideal clear sky and all-weather LST data set. This data set not only realizes the seamless coverage of space-time, but also has good verification accuracy. The reconstructed ideal clear sky LST data in the experimental areas of 17 land cover types in the world, the average correlation coefficient (R) is 0.971, the bias (bias) is -0.001 K to 0.049 K, and the root mean square error (RMSE) is 1.436 K to 2.688 K. The verification results of the reconstructed all-weather LST data and the measured data of ground stations: the average R is 0.895, the bias is 0.025 K to 2.599 K, and the RMSE is 4.503 K to 7.299 K. The time resolution of this data set is 4 times a day, the spatial resolution is 0.05 °, the time span is 2002-2020, and the spatial range covers the world.
ZHAO Tianjie, YU Pei
This dataset contains the glacier outlines in Qilian Mountain Area in 2020. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2020 were used as basic data for glacier extraction. Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2020, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.
Li Jia Li Jia LI Jia LI Jia
Based on AVHRR-CDR SR products, a daily cloud-free snow cover extent dataset with a spatial resolution of 5 km from 1981 to 2019 was prepared by using decision tree classification method. Each HDF4 file contains 18 data elements, including data value, data start date, longitude and latitude, etc. At the same time, to quickly preview the snow distribution, the daily file contains the snow area thumbnail, which is stored in JPG format. This data set will be continuously supplemented and improved according to the real-time satellite remote sensing data and algorithm update (up to may 2019), and will be fully open and shared.
HAO Xiaohua
Agricultural irrigation consumes a large amount of available freshwater resources and is the most immediate human disturbance to the natural water cycle process, with accelerated regional water cycles accompanied by cooling effects. Therefore, estimating irrigation water use (IWU) is important for exploring the impact of human activities on the natural water cycle, quantifying water resources budget, and optimizing agricultural water management. However, the current irrigation data are mainly based on the survey statistics, which is scattered and lacks uniformity, and cannot meet the demand for estimating the spatial and temporal changes of IWU. The Global Irrigation Water Use Estimation Dataset (2011-2018) is calculated by the satellite soil moisture, precipitation, vegetation index, and meteorological data (such as incoming radiation and temperature) based on the principle of soil water balance. The framework of IWU estimation in this study coupled the remotely sensed evapotranspiration process module and the data-model fusion algorithm based on differential evolution. The IWU estimates provided from this dataset have small bias at different spatial scales (e.g., regional, state/province and national) compared to traditional discrete survey statistics, such as at Chinese provinces for 2015 (bias = −3.10 km^3), at U.S. states for 2013 (bias = −0.42 km^3), and at various FAO countries (bias = −10.84 km^3). Also, the ensemble IWU estimates show lower uncertainty compared to the results derived from individual precipitation and soil moisture satellite products. The dataset is unified using a global geographic latitude and longitude grid, with associated metadata stored in corresponding NetCDF file. The spatial resolution is about 25 km, the time resolution is monthly, and the time span is 2011-2018. This dataset will help to quantitatively assess the spatial and temporal patterns of agricultural irrigation water use during the historical period and support scientific agricultural water management.
ZHANG Kun, LI Xin, ZHENG Donghai, ZHANG Ling, ZHU Gaofeng
This daily land surface kernel-driven BRDF model's coeciffients proudct is with a spatl resolution of 0.02 ° x 0.02 ° over the Tibet Plateau in 2016. Multi-sensory data is used to retrieve the the kernel-driven BRDF model and coupled with topographic effects, and prior knowledge is introduced for quality control inversion. The high-precision BRDF of good spatial-temporal continiuty is retrieved by combining MODIS reflectance data (a polar orbiting satellite) and himawari-8 AHI land surface reflectance (a geostationary satellite ). MODIS lans surface reflectance data and AHI TOA reflectance data are downloaded from the official websites. After registration, atmospheric correction and other processing, the daily resolution BRDF is synthesized with a period of 5 days. Compared with similar products, it has more advantages in capturing rapidly changing surface features, and has better temporal and spatial continuity with the shortest composition period. It can effectively support angular effects correction and the BRDF-releated parameters' retrieval.
WEN Jianguang , TANG Yong, TANG Yong, TANG Yong, YOU Dongqin YOU Dongqin
This dataset includes Fraction Vegetation Coverage (FVC) data for five countries in Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan) during 2010, 2015 and 2020. The data is calculated from the MODIS-NDVI data set (product number MOD13A2.006) based on the empirical relationship between FVC in arid areas and NDVI. The product has a time resolution of 1 year and a spatial resolution of 1 km. The algorithm selects the best available pixel value based on low cloud, low detection angle and highest NDVI value from all the observation data of the year, and performs conversion.
XU Xiaofan, TAN Minghong
The global monthly all-sky land surface temperature (2000-2020) is produced by the method from Chen et al. 2017 JHM.
CHEN Xuelong, BOB Su, MA Yaoming
Based on a large number of measured aboveground biomass data of grassland, the temperate grassland types were divided according to the vegetation type map of China in 1980s Based on the Landsat remote sensing data of engine platform, the random forest model of grassland aboveground biomass and remote sensing data was constructed for different grassland types. On the basis of reliable verification, the annual aboveground biomass of grassland from 1993 to 2019 was estimated, and the annual spatial data set of aboveground biomass of temperate grassland in Northern China from 1993 to 2019 was formed. Aboveground biomass is defined as the total amount of organic matter of vegetation living above the ground in unit area. The original grid value has been multiplied by a factor of 100, unit: 0.01 g / m2 (g / m2). This data set can provide a scientific basis for the dynamic monitoring and evaluation of temperate grassland resources and ecological environment in northern China.
ZHANG Na
Based on a large number of measured aboveground biomass data of grassland, the temperate grassland types were divided according to the vegetation type map of China in 1980s Based on the Landsat remote sensing data of engine platform, the random forest model of grassland aboveground biomass and remote sensing data was constructed for different grassland types. On the basis of reliable verification, the annual aboveground biomass of grassland from 1993 to 2019 was estimated, and the annual spatial data set of aboveground biomass of temperate grassland in Northern China from 1993 to 2019 was formed. Aboveground biomass is defined as the total amount of organic matter of vegetation living above the ground in unit area. The original grid value has been multiplied by a factor of 100, unit: 0.01 g / m2 (g / m2). This data set can provide a scientific basis for the dynamic monitoring and evaluation of temperate grassland resources and ecological environment in northern China.
ZHANG Na
Based on the medium resolution long time series remote sensing image Landsat, the data set obtained six periods of ecosystem type distribution maps of the Qinghai Tibet Plateau in 1990 / 1995 / 2002 / 2005 / 2010 / 2015 through image fusion, remote sensing interpretation and data inversion, and made the original ecological base map of the Qinghai Tibet Plateau in 25 years (1990-2015). According to the area statistics of various ecosystems in the Qinghai Tibet Plateau, the area of woodland and grassland decreased slightly, the area of urban land, rural residential areas and other construction land increased, the area of rivers, lakes and other water bodies increased, and the area of permanent glacier snow decreased from 1990 to 2015. The atlas can be used for the planning, design and management of ecological projects in the Qinghai Tibet Plateau, and can be used as a benchmark for the current situation of the ecosystem, to clarify the temporal and spatial pattern of major ecological projects in the Qinghai Tibet Plateau, and to reveal the change rules and regional differences of the pattern and function of the ecosystem in the Qinghai Tibet Plateau.
ZHAO Hui, WANG Xiaodan
Evapotranspiration over the Qinghai Tibet Plateau is calculated by etwatch, a land surface evapotranspiration remote sensing model based on multi-scale and multi-source data. Etwatch adopts the method of combining the residual term method with P-M formula to calculate evapotranspiration. Firstly, according to the characteristics of the data image, the suitable model is selected to retrieve the evapotranspiration on a sunny day; the remote sensing model is often lack of data because the weather conditions can not obtain a clear image. In order to obtain the daily continuous evapotranspiration, the penman Monteith formula is introduced, and the evapotranspiration results on a sunny day are regarded as the "key frame", and the surface impedance information of the key frame is used as the basis to construct the surface impedance Based on the daily meteorological data, the time series data of evapotranspiration are reconstructed. Through the data fusion model, the high spatial and temporal resolution evapotranspiration data set is constructed by combining the low and medium resolution evapotranspiration temporal variation information with the high resolution evapotranspiration spatial difference information, so as to generate the 8 km resolution evapotranspiration of the Qinghai Tibet Plateau Data sets (1990-2015).
WANG Xiaofeng
The data set is based on a series of microwave remote sensing data, including Special Sensor Microwave Imager (SSM/I), Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E), etc., which can be used as a reference for primary productivity. The data is from Liu et al. (2015), and the specific calculation method is shown in the article. The source data range is global, and Tibetan Plateau region is selected in this data set. This data set is often used to evaluate the temporal and spatial patterns of vegetation greenness and primary productivity, which has practical significance and theoretical value.
LIU Yi
This data set includes the monthly average actual evapotranspiration of the Tibet Plateau from 2001 to 2018. The data set is based on the satellite remote sensing data (MODIS) and reanalysis meteorological data (CMFD), and is calculated by the surface energy balance system model (SEBS). In the process of calculating the turbulent flux, the sub-grid scale topography drag parameterization scheme is introduced to improve the simulation of sensible and latent heat fluxes. In addition, the evapotranspiration of the model is verified by the observation data of six turbulence flux stations on the Tibetan Plateau, which shows high accuracy. The data set can be used to study the characteristics of land-atmosphere interaction and the water cycle in the Tibetan Plateau.
HAN Cunbo, MA Yaoming, WANG Binbin, ZHONG Lei, MA Weiqiang*, CHEN Xuelong, SU Zhongbo
The land cover dataset of Pan third pole major cities contains 14 cities (Urumqi, Xining, Lanzhou, Dhaka, Kathmandu, Lucknow, Delhi, Lahore, Islamabad, Kabul, Dushanbe, Tashkent, Bishkek and Almaty) in 2000 / 2010 / 2017, the spatial resolution of this dataset is 30 m. It includes vegetation, cultivated land, artificial surface, water body and others. Based on globeland30, mcd12q1 and globcover2009, the consistent regions were identified and retained. The inconsistent regions were reclassified by deep learning method, and the final classification results were obtained by fusing the above regions. The data has been verified by visual interpretation. The data are applied to the study of construction land dynamics and anthropogenic influence in Pan-Third Pole cities. Data type: grid. Projection mode: UTM projection.
Xin LI, LI Xin
The dataset include ground-based passive microwave brightness temperature, multi-angle brightness temperature, ten-minute 4-component radiation and snow temperature, daily snow pit data and hourly meteorological data observed at Altay base station（lon：88.07、lat: 44.73）from November 27, 2015 to March 26, 2016. Daily snow pit parameters include: snow stratification, stratification thickness, density, particle size, temperature. These data are stored in five NetCDF files: TBdata. nc, TBdata-multiangle. nc, ten-minute 4 component radiation and snow temperature. nc, hourly meteorological and soil data. nc and daily snow pit data.nc. TBdata. nc is brightness temperature at 3 channels for both polarizations automatically collected by a six-channel dual polarized microwave radiometer RPG-6CH-DP. The contents include Year, month, day, hour, minute, second, Tb1h, Tb1v, Tb18h, Tb18v, Tb36h, Tb36v, incidence angle, azimuth angle. TBdata-multiangle.nc is 7 groups of multi-angle brightness temperatures at 3 channels for both polarizations. The contents include Year, month, day, hour, minute, second, Tb1h, Tb1v, Tb18h, Tb18v, Tb36h, Tb36v, incidence angle, azimuth angle. The ten-minute 4 component radiation and snow temperature.nc contains 4 component radiation and layered snow temperatures. The contents include Year, month, day, hour, minute, SR_DOWN, SR_UP, LR_DOWN, LR_UP, T_Sensor, ST_0cm, ST_5cm, ST_15cm, ST_25cm, ST_35cm, ST_45cm, ST_55cm. The hourly meteorological and soil data.nc contains hourly weather data and layered soil data. The contents include Year, month, day, hour, Tair, Wair, Pair, Win, SM_10cm, SM_20cm, Tsoil_5cm, Tsoil_10cm, Tsoil_15 cm, Tsoil_20cm. The daily snow pit data.nc. is manual snow pit data. The observation time was 8:00-10:100 am local time. The contents include Year, month, day, snow depth, thickness_layer1, thickness_layer2, thickness_layer3, thickness_layer4, thickness_layer5, thickness_layer6, Long_layer1, Short_layer1, Long_layer2, Short_layer2, Long_layer3, Short_layer3, Long_layer 4, Short_layer4, Long_layer5, Short_layer5, Long_layer6, Short_layer 6, Stube, Snow shovel_0-10, Snow shovel _10-20, Snow shovel _20-30, Snow shovel _30-40, Snow shovel _40-50, Snow fork_5, Snow fork _10, Snow fork _15, Snow fork_20, Snow fork_25, Snow fork_30, Snow fork_35, Snow fork_40, Snow fork_45, Snow fork_50, shape1, shape2, shape3, shape4, shape5,
DAI Liyun
Thematic data on desertification in Western Asia, includes two parts: Distribution Map of Sandy Land in Western Asia, Distribution Map of Grassland Degradation in Western Asia. The spatial resolution of the data is 30m. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences, the spatial resolution of data is 30 m. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101. The map of artificial oasis pattern in Amu river basin is based on Landsat TM and ETM image data in 2015. Firstly, with the help of eCognition software, the object-oriented classification is carried out. Secondly, the classification results are checked and corrected manually.
The long-term evolution of lakes on the Tibetan Plateau (TP) could be observed from Landsat series of satellite data since the 1970s. However, the seasonal cycles of lakes on the TP have received little attention due to high cloud contamination of the commonly-used optical images. In this study, for the first time, the seasonal cycle of lakes on the TP were detected using Sentinel-1 Synthetic Aperture Radar (SAR) data with a high repeat cycle. A total of approximately 6000 Level-1 scenes were obtained that covered all large lakes (> 50 km2) in the study area. The images were extracted from stripmap (SM) and interferometric wide swath (IW) modes that had a pixel spacing of 40 m in the range and azimuth directions. The lake boundaries extracted from Sentinel-1 data using the algorithm developed in this study were in good agreement with in-situ measurements of lake shoreline, lake outlines delineated from the corresponding Landsat images in 2015 and lake levels for Qinghai Lake. Upon analysis, it was found that the seasonal cycles of lakes exhibited drastically different patterns across the TP. For example, large size lakes (> 100 km2) reached their peaks in August−September while lakes with areas of 50−100 km2 reached their peaks in early June−July. The peaks of seasonal cycles for endorheic lakes were more pronounced than those for exorheic lakes with flat peaks, and glacier-fed lakes with additional supplies of water exhibited delayed peaks in their seasonal cycles relative to those of non-glacier-fed lakes. Large-scale atmospheric circulation systems, such as the westerlies, Indian summer monsoon, transition in between, and East Asian summer monsoon, were also found to affect the seasonal cycles of lakes. The results of this study suggest that Sentinel-1 SAR data are a powerful tool that can be used to fill gaps in intra-annual lake observations.
ZHANG Yu, ZHANG Guoqing
Global land cover data are key sources of information for understanding the complex interactions between human activities and global change. FROM-GLC (Finer Resolution Observation and Monitoring of Global Land Cover) from Tsinghua is the 30 m resolution global land cover maps produced. The Global land cover data of all 34 key nodes of pan-third pole region are produced through analyse by argis. The classfication system is crop(10), forest(20), grass(30), shrbu(40), wetland(50), water(60), tundra(70), impervious(80), Bareland(90), snow/ice(100), cloud(120). Finally, This data set serves as the research basis for all remote sensing data and provides baseline data for the project.
GE Yong, LING Feng, ZHANG Yihang
This dataset includes component temperatures measured by the thermal imager at the Mixed Forest and Sidaoqiao stations between 23 July and 18 August, 2014. The Mixed Forest (101.1335 °E, 41.9903 °N, 874 m.a.s.l.) and Sidaoqiao (101.1374 °E, 42.0012 °N, 873 m.a.s.l.) stations were located in the downstream of the Heihe River basin, Dalaihubu Town, Ejin Banner, Inner Mongolia. At the Mixed Forest station, a Testo 890-2 thermal imager (Testo Inc., Germany) with a resolution of 640 × 480 pixels was employed to acquire brightness temperature images. The imager was manually operated from a 10-m height platform of the tower between 10:00-16:00 (China Standard Time, CST) with an observation interval of 1-h on cloudless days. On August 4th observations were acquired between 11:00 and 17:00 at an interval of 10-min to match observations acquired with an airborne TIR imager. The ground based imager was pointed to five viewing directions (southeast-SE, east-E, northeast-NE, northwest-NW, and southwest-SW) and was inclined 25°–45° below the horizon depending on viewing direction. At Sidaoqiao station, a Testo 875-2i imager (Testo Inc., Germany) with a resolution of 160 × 120 pixels was manually operated from a 10-m high platform to acquire brightness temperature images in directions SW, SE, NE, and NW. Depending on the targets in each viewing direction, the imager was inclined to 30°–45° below the horizon. Observations at Sidaoqiao and Mixed Forest stations were almost synchronous. Furthermore, visible images were taken simultaneously with the aforementioned two TIR imagers (2048 × 1536 pixels for Testo 890-2 and 640 × 480 pixels for Testo 875-2i).
LI Mingsong , MA Jin
This dataset includes component temperatures measured by the thermal infrared (TIR) radiometers at the Mixed Forest and Sidaoqiao stations between 22 July, 2014 and 19 July, 2016. The Mixed Forest (101.1335 °E, 41.9903 °N, 874 m.a.s.l.) and Sidaoqiao (101.1374 °E, 42.0012 °N, 873 m.a.s.l.) stations were located in the downstream of the Heihe River basin, Dalaihubu Town, Ejin Banner, Inner Mongolia. At the Mixed Forest station, two TIR radiometers (SI-111, Apogee Instruments Inc., USA) connected to a data logger (CR800, Campbell Scientific Inc., USA) measured component temperatures of the sunlit canopy and shaded canopy. TIR radiometers were mounted horizontally at 5 m height on iron rods just south and north of a tree and pointed to its canopy. The distance from the sensor to the canopy was ~1 m. At the Sidaoqiao station, two SI-111 TIR radiometers connected to a CR800 data logger measured component temperatures of the soil and shrub. The first sensor pointed from 2 m height under a viewing zenith angle of 45° to bare soil; the second sensor was mounted at 1-m height and pointed horizontally into the shrub canopy.
ZHOU Ji, LI Mingsong , MA Jin
Sentine-1 SAR data were used to monitor the permafrost of Biuniugou in Heihe River Basin of Qinghai-Tibet Plateau. Based on the Sentine-1 SAR image of Bison Valley from 2014 to 2018, the active layer thickness in the study area was estimated by using the small baseline set time series InSAR (DSs-SBAS) frozen soil deformation monitoring method based on distributed radar target, combined with SAR backscattering coefficient, MODIS surface temperature and Stefan model. The results show that the thickness of active layer is between 0.8 m and 6.6 m, with an average of about 3.3 M. It is of great significance to carry out large-scale and high-resolution monitoring.
JIANG Liming
Global warming and human activities have led to the degradation of permafrost and the collapse of permafrost, which have seriously affected the construction of permafrost projects and the ecological environment. Based on high-resolution satellite images, the permafrost of oboling in Heihe River Basin of Qinghai Tibet Plateau is taken as the research area, and the object-oriented classification technology of machine learning is used to extract the thermal collapse information in the research area. The results show that from 2009 to 2019, the number of thermal collapse increased from 12 to 16, and the total area increased from 14718.9 square meters to 28579.5 square meters, nearly twice. The combination of high spatial resolution remote sensing and object-oriented classification method has a broad application prospect in the monitoring of thermal thawing and collapse of frozen soil.
JIANG Liming
The distribution of lakes in space and its change over time are closely related to agricultural, environmental and ecological issues, and are critical factors for human socio-economic development. In the past decades, satellite based remote sensing has been developed rapidly to provide essential data sources for monitoring temporal lakes dynamics with its advantage of rapidness, wide coverage, and lower cost. This dataset was produced from Landsat images using the automated water detection method (Feng et al, 2015). We collected 96,278 Landsat images (about 25 terabytes) that acquired since 2000 with less than 80% cloud contamination in the arid region of central Asia and Tibetan Plateau. Water is detected in each of the image and then aggregated to monthly temporal resolution by taking advantage of the high-performance processing capability and large data storage provided by Global Land Cover Facility (GLCF) at University of Maryland. The results are validated systematically and quantitatively using manually interpreted dataset, which consists of a set of locations collected by a stratified random sampling strategy to effectively represent different spatial-temporal distributions in the region. The validation suggests high accuracy of the results (overall accuracy: 99.45(±0.59); user accuracy: 85.37%±(3.74); produce accuracy: 98.17(±1.05)).
FENG Min, CHE Xianghong
This dataset is a pixel-based maximum fractional vegetation cover map within the Yellow River source region on the Qinghai-Tibet Plateau, with an area of about 44,000 square kilometers. Based on the time series images acquired from MODIS with a resolution of 250 m and Landsat-8 with a resolution of 30 m in 2015 during the vegetation growing season, the data are derived using dimidiate pixel model and time interpolation. The spatial resolution of the image is 30 m, using the WGS 1984 UTM projected coordinate system, and the data is in the format of grid.
WANG Guangjun
Thematic data on desertification (land desertification, salinization and vegetation degradation) in Central Asia, includes three parts: Distribution Map of Sandy Land in Central Asia, Distribution Map of Salinized Land in Central Asia and Distribution Map of Land Vegetation Degradation in Central Asia. The spatial resolution of the data is 1km, the time resolution is in 2015. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101.
XU Wenqiang
Data Set of Key Elements of Desertification in Typical Watershed of Central and Western Asia includes four parts: distribution and change of agricultural land of Amu River Basin, distribution and change of grassland of Amu River Basin, distribution and change of shrub land of Amu River Basin, distribution and change of forests of Amu River Basin. the spatial resolution of data is 30 m. All the data is based on Landsat TM/ETM image data in 1990, 2000 and 2010. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101.
The data was obtained from the 30-second global elevation dataset developed by the US Geological Survey (USGS) and completed in 1996. Downloaded the data from the NCAR and UCAR Joint Data Download Center (https://rda.ucar.edu/datasets/ds758.0/) and redistributed it through this data center. GTOPO30 divides the world into 33 blocks. The sampling interval is 30 arc seconds, which is 0.00833333333333333 degrees. The coordinate reference is WGS84. The DEM is the distance from the sea level in the vertical direction, ie the altitude, in m, the altitude range from -407 to 8752, the ocean depth information is not included here, the negative value is the altitude of the continental shelf; the ocean is marked as -9999, the elevation above the coastline is at least 1; the island less than 1 square kilometer is not considered. In order to facilitate the user's convenience, on the basis of the block data, splice 10 blocks in -10S-90N and 20W-180E without any resampling processing. This data file is DEM_ptpe_Gtopo30.nc
HE Yongli
The basic data set of remote sensing for ecological assets assessment of the Qinghai-Tibet Plateau includes the annual Fraction Vegetation Coverage (FVC), Net Primary Productivity (NPP) and Leaf Area Index (LAI) of the Qinghai-Tibet Plateau since 2000, and other ecological parameters based on remote sensing inversion. The FVC data are mainly developed from MODIS NDVI data. Based on pixel dichotomy model, the vegetation coverage model is developed by using multi-scale remote sensing images, combining with high precision remote sensing parameters such as vegetation community type and distribution characteristics, and the mixed pixel decomposition method is used to construct the vegetation coverage model. All data could be used only after the permission of the data distributor.
LIU Wenjun
The distribution data of Central Asia desert oil and gas fields are in the form of vector data in ". SHP". Including the distribution of oil and gas fields and major urban settlements in the five Central Asian countries. The data is extracted and cut from modis-mcd12q product. The spatial resolution of the product is 500 m, and the time resolution is 1 year. IGBP global vegetation classification scheme is adopted as the classification standard. The scheme is divided into 17 land cover types, among which the urban data uses the construction and urban land in the scheme. The data can provide data support for the assessment and prevention of sandstorm disasters in Central Asia desert oil and gas fields and green town.
GAO Xin
The data set includes estimated data on the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on the MODIS 16-day synthetic NDVI product (MOD13A2 collection 6). Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage ranges from 2001 to 2014, and the spatial resolution is 1 km.
WANG Xufeng
The data set includes the estimated data of the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on 10-day synthetic NDVI products from the SPOT satellite. Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage is from 1999 to 2013, and the spatial resolution is 1 km.
WANG Xufeng
The data set includes the estimated data on the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on GIMMS3g version 1.0, the latest version of the GIMMS NDVI data set. Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage ranges from 1982 to 2015, and the spatial resolution is 8 km.
WANG Xufeng
These data contain two data files: GLOBELAND30 TILES (raw data) and TIBET_ GLOBELAND30_MOSAIC (mosaic data). The raw data were downloaded from the Global Land Cover Data website (GlobalLand3) (http://www.globallandcover.com) and cover the Tibetan Plateau and surrounding areas. The raw data were stored in frames, and for the convenience of using the data, we use Erdas software to splice and mosaic the raw data. The Global Land Cover Data (GlobalLand30) is the result of the “Global Land Cover Remote Sensing Mapping and Key Technology Research”, which is a key project of the National 863 Program. Using the American Landsat images (TM5, ETM+) and Chinese Environmental Disaster Reduction Satellite images (HJ-1), the data were extracted by a comprehensive method based on pixel classification-object extraction-knowledge checks. The data include 10 primary land cover types—cultivated land, forest, grassland, shrub, wetland, water body, tundra, man-made cover, bare land, glacier and permanent snow—without extracting secondary types. In terms of accuracy assessment, nine types and more than 150,000 test samples were evaluated. The overall accuracy of the GlobeLand30-2010 data is 80.33%. The Kappa indicator is 0.75. The GlobeLand30 data use the WGS84 coordinate system, UTM projection, and 6-degree banding, and the reference ellipsoid is the WGS 84 ellipsoid. According to different latitudes, the data are organized into two types of framing. In the regions of 60° north and south latitudes, the framing is carried out according to a size of 5° (latitude) × 6° (longitude); in the regions of 60° to 80° north and south latitudes, the framing is carried out according to a size of 5° (latitude) × 12° (longitude). The framing is projected according to the central meridian of the odd 6° band. GLOBELAND30 TILES: The original, unprocessed raw data are retained. TIBET_ GLOBELAND30_MOSAIC: The Erdas software is used to mosaic the raw data. The parameter settings use the default value of the raw data to retain the original, and the accuracy is consistent with that of the downloading site.
CHEN Jun
There are three types of glacial lakes: supraglacial lakes, lakes attached to the end of the glacier and lakes not attached to the end of the glacier. Based on this classification, the following properties are studied: the variation in the number and area of glacial lakes in different basins in the Third Pole region, the changes in extent in terms of size and area, distance from glaciers, the differences in area changes between lakes with and without the supply of glacial melt water runoff, the characteristics of changes in the glacial lake area with respect to elevation, etc. Data source: Landsat TM/ETM+ 1990, 2000, 2010. The data were visually interpreted, which included checking and editing by comparing the original image with Google Earth images when the area was greater than 0.003 square kilometres. The data were applied to glacial lake changes and glacial lake outburst flood assessments in the Third Pole region. Data type: Vector data. Projected Coordinate System: Albers Conical Equal Area.
ZHANG Guoqing
This is the vegetation index (NDVI) for Maduo County in July, August and September of 2016. It is obtained through calculation based on the multispectral data of GF-1. The spatial resolution is 16 m. The GF-1 data are processed by mosaicking, projection coordinating, data subsetting and other methods. The maximum synthesis is then conducted every month in July, August, and September.
LI Fei, Fei Li, Zhijun Zhang
The NDVI data set is the sixth version of the MODIS Normalized Difference Vegetation Index product (2001-2016) jointly released by NASA EOSDIS LP DAAC and the US Geological Survey (USGS EROS). The product has a temporal resolution of 16 days and a spatial resolution of 0.05 degrees. This version is a Climate Modeling Grid (CMG) data product generated from the original NDVI product (MYD13A2) with a resolution of 1 kilometer. Please indicate the source of these data as follows in acknowledgments: The MOD13C NDVI product was retrieved online courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, The [PRODUCT] was (were) retrieved from the online [TOOL], courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota.
NASA
The NDVI data set is the latest release of the long sequence (1981-2015) normalized difference vegetation index product of NOAA Global Inventory Monitoring and Modeling System (GIMMS), version number 3g.v1. The temporal resolution of the product is twice a month, while the spatial resolution is 1/12 of a degree. The temporal coverage is from July 1981 to December 2015. This product is a shared data product and can be downloaded directly from ecocast.arc.nasa.gov. For details, please refer to https://nex.nasa.gov/nex/projects/1349/.
The National Center for Atmospheric Research
The continuous advancement of SAR interferometry technology makes it possible to obtain multitemporal DEMs with high precision in the glacial area. In particular, in 2000, the Shuttle Radar Topography Mission (SRTM) led by NASA provided DEM data covering the area from 56ºS to 60ºN; the TanDEM-X bistatic SAR interferometry system of DLR could provide the global DEM data with high resolution and precision. These high-quality, large-coverage SAR interferometry data, as well as published DEM data products, provided valuable information for using the multitemporal DEMs to detect changes in ice thickness. The temporal coverage of the ice thickness variation data of typical glaciers on the Tibetan Plateau was from 2000 to 2013, covering Puruogangri and the west Qilian Mountains with a spatial resolution of 30 meters. Using TanDEM-X bistatic InSAR data and a C-band SRTM DEM, the differential radar interferometry method was first used to generate a TanDEM-X DEM with high precision. Then, based on the precise registration of DEM, the DEM data obtained in different periods were compared. Lastly, the ice thickness changes were estimated. The format of the data set was GeoTIFF, and each typical glacier ice thickness change was stored in a folder. For details of the data, please refer to the Ice elevation changes for typical glaciers on the Tibetan Plateau - Data Description.
JIANG Liming
This data set contains the wide swath mode Level 1B SAR data acquired over Greenland in 2005 by the ASAR sensor of the ENVISAT-1 satellite. The width is 400 km, the spatial resolution is 75 m, and the absolute positioning accuracy is approximately 200 m. The SAR data are stored in a time-growth order, which causes the images of the descending track to be left-right mirror images and the images of the ascending track to be up-down images. The naming scheme for these data is as follows: ASA_IMS_1PPIPA 20050402_095556_000000162036_00065_16151_0388.N1 ASA: Product identification, ASAR Sensor IMS: Reception and processing information of the data (imaging modes, such as WS, WSS, IM, ...) 1PPIPA: Customized number 20050402: Acquisition time of the data (UTC time) 095556: Geographic location (start, end) 000000162036: Information on the satellite orbit 00065: Product trust data 16151: Size and structure information of the product 0388 => Check code
HUI Fengming
The 1 km / 5-day Lai data set of Heihe River basin provides the 5-day Lai synthesis results of 2010-2014. The data uses Terra / MODIS, Aqua / MODIS, as well as domestic satellites fy3a / MERSI and fy3b / MERSI sensor data to build a multi-source remote sensing data set with a spatial resolution of 1 km and a time resolution of 5 days. Multi-source remote sensing data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm first classifies the quality of multi-source data sets, which can be divided into first level data, second level data and third level data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. The purpose of quality evaluation and classification is to provide the basis for the selection of the optimal data set and the design of inversion algorithm flow. Leaf area index product inversion algorithm is designed to distinguish mountain land and vegetation type, using different neural network inversion model. Based on global DEM map and surface classification map, PROSAIL model is used for continuous vegetation such as grassland and crops, and gost model is used for forest and mountain vegetation. Using the reference map generated by the measured ground data of the forests in the upper reaches of Heihe River and the oasis in the middle reaches, and scaling up the corresponding high-resolution reference map to 1km resolution, compared with the Lai product, the product has a good correlation between the farmland and the forest area and the reference value, and the overall accuracy basically meets the accuracy threshold of 0.5%, 20% specified by GCOS. By cross comparing this product with Lais products such as MODIS, geov1 and glass, the accuracy of this Lai product is better than that of similar products compared with reference value. In a word, the synthetic Lai data set of 1km / 5 days in Heihe River Basin comprehensively uses multi-source remote sensing data to improve the estimation accuracy and time resolution of Lai parameter products, so as to better serve the application of remote sensing data products.
LI Jing, Yin Gaofei, YIN Gaofei, ZHONG Bo, WU Junjun, WU Shanlong
On June 15, 2012, the satellite transit ground synchronous observation was carried out in the TerraSAR-X sample near the super station in the dense observation area of Daman. TerraSAR-X satellite carries X-band synthetic aperture radar (SAR). The daily transit image is HH / VV polarized, with a nominal resolution of 3 m, an incidence angle of 22-24 ° and a transit time of 19:03 (Beijing time), which mainly covers the ecological and hydrological experimental area of the middle reaches artificial oasis. The local synchronous data set can provide the basic ground data set for the development and verification of active microwave remote sensing soil moisture retrieval algorithm. Quadrat and sampling strategy: Six natural blocks are selected in the southeast of the super station, with an area of about 100 m × 100 m. One plot in the northwest corner of the sample plot is watermelon field, others are corn. The basis of sample selection is: (1) considering different vegetation types, i.e. watermelon and corn; (2) considering the visible light pixel, the sample size of 100m square can guarantee at least 4 30 M-pixel is located in the sample; (3) the location of the sample is near the super station, with convenient transportation. The observation of the super station is in the north, and there is a water net node on both sides of the East and the west, which makes it possible to integrate these observations in the future; (4) in addition, there are some obvious points around the sample, which can ensure that the geometric correction of the SAR image is more accurate in the future. Considering the resolution of the image, 21 splines (distributed from east to West) are collected at 5 m intervals. Each line has 23 points (north-south direction) at 5 m intervals. Four hydroprobe data acquisition systems (HDAS, reference 2) are used to measure at the same time. The sampling interval is controlled by the scale and moving splines on the measuring line to make up for the lack of using hand-held GPS. Measurement content: About 500 points on the quadrat were obtained, and each point was observed twice, i.e. in each sampling point, once in the film (marked a in the data record) and once out of the film (marked b in the data record); although the watermelon land was also covered with film, considering that it was not laid horizontally, only the soil moisture at the non covered position was measured (marked b in both data records). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and imaginary part of soil complex dielectric are observed. The vegetation team completed the measurement of biomass, Lai, vegetation water content, plant height, row ridge distance, chlorophyll, etc. Data: This data set includes two parts: soil moisture observation and vegetation observation. The former saves the data format as a vector file, the spatial location is the location of each sampling point (WGS84 + UTM 47N), and the measurement information of soil moisture is recorded in the attribute file; the vegetation sampling information is recorded in the excel table.
WANG Shuguo, MA Mingguo, LI Xin
The aim of the simultaneous observation of river surface temperature is obtaining the river surface temperature of different places, while the sensor of thermal infrared go into the experimental areas of artificial oases eco-hydrology on the middle stream. All the river surface temperature data will be used for validation of the retrieved river surface temperature from thermal infrared sensor and the analysis of the scale effect of the river surface temperature, and finally serve for the validation of the plausibility checks of the surface temperature product from remote sensing. 1. Observation sites and other details Ten river sections were chosen to observe surface temperature simultaneously in the midstream of Heihe River Basin on 3 July and 4 July, 2012, including Sunan Bridge, Binhe new area, Heihe Bridge, Railway Bridge, Wujiang Bridge, Gaoya Hydrologic Station, Banqiao, Pingchuan Bridge, Yi’s Village, Liu’s Bridge. Self-recording point thermometers (observed once every 6 seconds) were used in Railway Bridge and Gaoya Hydrologic Station while handheld infrared thermometers (observed once of the river section temperature for every 15 minutes) were used in other eight places. 2. Instrument parameters and calibration The field of view of the self-recording point thermometer and the handheld infrared thermometer are 10 and 1 degree, respectively. The emissivity of the latter was assumed to be 0.95. All instruments were calibrated on 6 July, 2012 using black body during observation. 3. Data storage All the observation data were stored in excel.
HE Xiaobo, Jia Shuzhen
This dataset contains the spectra of white cloth and black cloth obtained in the simultaneous time during the airborn remote sensing which supports the airboren data preprocessing as CASI, SASI and TASI , and the spetra of the typical targets in the middle reaches of the Heihe River Basin. Instruments: SVC-HR1024 from IRSA, ASD Field Spec 3 from CEODE, Reference board Measurement method: the spectra radiance of the targets are vertically measured by the SVC or ASD; before and after the target, the spectra radiance of the reference board is measured as the reference. This dataset contains the spectra recorded by the SVC-HR1024 ( in the format of .sig which can be opened by the SVC-HR1024 software or by the notepad ) and the ASD (in the format of .asd), the observation log (in the format of word or excel), and the photos of the measured targets. Observation time: 15-6-2012, the spectra of typical targets in the EC matrix using SVC 16-6-2012, the spectra of typical targets in the wetland by SVC 29-6-2012, the spectra of typical vegetation and soil in Daman site and Gobi site by ASD 29-6-2012, the spectra of white cloth and black cloth by ASD which is simultaneous with the airborne CASI data 30-6-2012, the spectra of vegetation and soil in the desert by ASD 5-7-2012, the spectra of white cloth and black cloth by ASD which is simultaneous with the airborne CASI data 7-7-2012, the spectra of corn in the Daman site for the research of daily speral variation. 8-7-2012, the spectra of white cloth and black cloth by ASD which is simultaneous with the airborne CASI data 8-7-2012, the spectra of corn in the Daman site by ASD for the research of daily speral variation 9-7-2012, the spectra of corn in the Daman site by ASD for the research of daily speral variation 10-7-2012, the spectra of corn in the Daman site by ASD for the research of daily speral variation 11-7-2012, the spectra of corn in the Daman site by ASD for the research of daily speral variation. The time used in this dataset is in UTC+8 Time.
XIAO Qing, MA Mingguo
Biological productivity refers to the material production capacity of organisms and their groups or even larger scale (including ecosystem and biosphere). It changes with the environment. Therefore, it becomes an indicator of environmental change and the health of the earth system. Net primary productivity (NPP) of vegetation refers to the remaining part of total organic matter (GPP) produced by photosynthesis of green plants in unit time unit area after deducting autotrophic respiration (RA). The NPP products in Heihe River Basin mainly focus on the important parameters par and FPAR of the model of light energy utilization, and improve the algorithm and product production. The FPAR inversion model that distinguishes the direct radiation from the scattered radiation and the par inversion method based on the combination of static and polar orbit satellites are proposed. Finally, the net primary productivity data set of Heihe River Basin is produced by using the light utilization model. The algorithm improves the temporal and spatial resolution of data products, and the accuracy of products is also significantly improved.
LI Li, ZHONG Bo, WU Junjun, WU Shanlong, XIN Xiaozhou
The purpose of differential GPS positioning survey is to unify multiple survey areas into the same coordinate system and realize accurate absolute positioning through joint survey with national high-level control point coordinates. Under the national geodetic coordinate system of 2000, the accurate positioning of flux observation matrix, hulugou small watershed, tianmuchi small watershed and dayokou watershed and target is completed. In order to realize the geometric correction and absolute positioning of optical image, SAR image and airborne lidar data, the layout of ground control points and high-precision measurement are completed. In the middle reaches of the area, one national high-level control point is jointly surveyed in the five directions of East, South, West, North and middle. Measuring instrument: There are 3 sets of triple R8 GNSS system. Measurement principle: For the control network encryption point, it is connected with the high-level known points in four quadrants around the survey area and distributed evenly in the survey area. For the ground control point (GCP), the obvious characteristic points (such as house corner, road intersection, inflection point, etc.) of the ground layout target and the independent ground objects are adopted and evenly distributed in the survey area. For the ground points with high accuracy requirements, the principle of average value of multiple (at least three) measurements is adopted. Measurement method: In the test area, the control network is encrypted, and GPS static measurement and national high-level control network are used for joint measurement and calculation. During measurement, multiple GPS receivers conduct static synchronous observation at different stations, and the observation time is strictly in accordance with the control network measurement specifications. The ground points in the test area are accurately located. GPS-RTK positioning technology is used and the national high-level control points are used to calibrate to the local coordinate system. When the mobile station obtains the fixed solution during the coordinate acquisition, the measurement is carried out again and the single measurement lasts for 5S. Measuring position: (1) Flux observation matrix 17 stations, Las tower, waternet, soilnet and bnunet nodes in the core area of flux observation matrix; ground control points in CASI flight area; ground corner reflector positions in radar coverage area; ground target positions in lidar flight area. (2) Hulugou small watershed Ground target location of lidar flight area. (3) Tianmuchi small watershed Ground target location of lidar flight area. (4) Dayokou Basin Satellite image geometric correction ground control point. Data format: GPS static survey, the original data format is ". Dat" and ". T01" (or ". T02") files (or converted renix data) and "field record". GPS-RTK survey, the original project is ". Job" file (or converted ". DC" file). The test results are submitted in the format of exported ". CSV" data, which can be viewed and edited by Excel software. Measurement time: June 19, 2012 to July 30, 2012
LIU Xiangfeng, MA Mingguo
On 29 June 2012, CASI sensor carried by the Harbin Y-12 aircraft was used in a visible near Infrared hyperspectral airborne remote sensing experiment, which is located in the observation experimental area (30×30 km). The land cover pattern product in the middle reaches of the Heihe River Basin were obtained at a spatial resolution of 1 m, using CASI aerial data with high spatial and spectral resolution．A hierarchical classification structure integrated by pixel-based classification and object-based classification is used to obtain production．According to surveyed reference data about land cover and visual interpretation from high resolution imagery，the accuracy of the classification result of land cover was evaluated，and the result showed that overall accuracy was 84.61 %，Kappa coefficient was 0.8262．
XIAO Qing, Liu Liangyun
During lidar and widas flight in summer 2012, the ground synchronously carried out the continuous observation of differential GPS of ground base station, and obtained the synchronous GPS static observation data, which is used to support the synchronous solution of aviation flight data. Measuring instrument: Two sets of triple R8 GNSS system. Zgp8001 sets Time and place of measurement: On July 19, 2012, EC matrix lidar flew and observed at mjwxb (northwest of Maojiawan) and sbmz (shibamin) two base stations at the same time On July 25, 2012, lidar of hulugou small watershed and tianmuchi small watershed in the upper reaches flew, observed in XT Xiatang, lidar of Zhangye City calibration field in the middle reaches, and observed in mjwxb (northwest of Maojiawan) On July 26, 2012, lidar flight of hulugou small watershed and tianmuchi small watershed in the upper reaches was observed in XT Xiatang, lidar flight of Zhangye City calibration field in the middle reaches was observed in HCZ (railway station) On August 1, 2012, the upper east and West branches of widas flew and observed in yng (yeniugou) On August 2, 2012, the midstream EC matrix test area widas flew and observed in HCZ (railway station) On August 3, 2012, the midstream EC matrix test area widas flew and observed in mjwxb (northwest Maojiawan) Data format: Original data format before differential preprocessing.
LIU Xiangfeng, MA Mingguo
250m/1km month compositing Fraction Vegetation Cover (FVC) data set of Heihe River Basin provides the results of monthly FVC synthesis in 2011-2014. The data is produced by using MODIS vegetation index products MOD13A2 and MOD13Q1 based on dimidiate pixel model.
ZHONG Bo, WU Junjun
On 10 July 2012 (UTC+8), TASI sensor carried by the Harbin Y-12 aircraft was used in a visible near Infrared hyperspectral airborne remote sensing experiment, which is located in the observation experimental area (30×30 km), Linze region and Heihe riverway. The relative flight altitude is 2500 meters. The wavelength of TASI is 8-11.5 μm with a spatial resolution of 3 meters. Through the ground sample points and atmospheric data, the data are recorded in surface radiance processed by geometric correction and atmospheric correction.
XIAO Qing, Wen Jianguang
The albedo product was obtained based on the visible and near-infrared hyperspectral radiometer (29 June, 2012) which covered the artificial oasis eco-hydrology experimental area (5.5 km*5.5 km)with a 5 m spatial resolution.
XIAO Qing, Wen Jianguang
On 19 August 2012, a Leica ALS70 airborne laser scanner boarded by the Y-12 aircraft was used to obtain the point cloud data. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 2900 m with the point cloud density 1 point per square meter. Aerial LiDAR-DSM was obtained through parameter calibration, automatic classification of point cloud density and manual editing.
XIAO Qing, Wen Jianguang
On 19 August 2012, a Leica ALS70 airborne laser scanner boarded on the Y-12 aircraft was used to obtain the Lidar point cloud data. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 2900 m with the point cloud density 1 point per square meter. Aerial LiDAR-DEM was obtained through parameter calibration, automatic classification of point cloud density and manual editing.
XIAO Qing, Wen Jianguang
This dataset includes the retrieved soil moisture products from the airborne PLMR microwave radiometer on 30 June, 7 July, 10 July, 26 July and 2 August, 2012 (UTC+8), in the HiWATER artificial oasis eco-hydrology experimental area of Heihe river basin. The soil moisture (SM), vegetation water content (VWC) and surface roughness (Hr) are simultaneously retrieved based on six brightness temperatures at three incidence angles (7°, 21.5°, 38.5°) and with dual polarization (H and V), by using the Levenberg-Marquardt optimization algorithm. The spatial resolution of the soil moisture products is 700 m, which represent the ~5 cm surface soil moisture according to the L-band observation wavelength. This dataset is in the format of asc, and with UTM projection (47°N). The validation against the eco-hydrological wireless sensor network observations and artificial synchronized observation shows that the total accuracy of this dataset can achieve 0.05 cm^3/cm^3, and that of the products on 7 July and 10 July even less than 0.04 cm^3/cm^3. This dataset can be helpful for the land surface process/hydrological process simulation and data assimilation, surface flux estimation, artificial irrigation management and spatial scaling research.
Li Dazhi, KANG Jian, LI Xin
The dataset contains vegetation type in the middle reaches of the Heihe River Basin, which was used to validate products from remote sensing. It was generated from investigating the land cover strips of CASI during 2012. Instruments: High-precision handheld GPS (2-3 m) and digital camera were used as main tools in the survey. Measurement method: Hierarchical classification is applied based on CASI data. According to various land types, pixel classifications is used for forest, grassland, bare land and building lands; in-situ observations and investigations are used for different crops. Dataset contains: land types, including maize, leek, poplar trees, cauliflower, bell pepper, potatoes, endive sprout, orchard, watermelon, kidney bean, pear orchard, shadow, and non-vegetation, except for 14 others which are not classified. Observation site: core experimental areas with 5*5 matrix structure in the middle reaches of the Heihe river basin Date: From 25 June in 2012 (UTC+8) on.
Zhang Miao
On 19 July 2012 (UTC+8), Leica ALS70 airborne laser scanner carried by the Harbin Y-12 aircraft was used in a LiDAR airborne optical remote sensing experiment. The relative flight altitude is 1500 m (the elevation of 2700 m). Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm with the point cloud density 4 points per square meter. Based on the original Airborne LiDAR-DEM data production were obtained through parameter calibration, automatic classification of point cloud density and manual editing.
XIAO Qing, Wen Jianguang
On June 26, 2012, the satellite transit ground synchronous observation was carried out in the TerraSAR-X sample near the super station in the dense observation area of Daman. TerraSAR-X satellite carries X-band synthetic aperture radar (SAR). The daily transit image is HH / VV polarized, with a nominal resolution of 3 m, an incidence angle of 22-24 ° and a transit time of 19:03 (Beijing time), which mainly covers the ecological and hydrological experimental area of the middle reaches artificial oasis. The local synchronous data set can provide the basic ground data set for the development and verification of active microwave remote sensing soil moisture retrieval algorithm. Quadrat and sampling strategy: Six natural blocks are selected in the southeast of the super station, with an area of about 100 m × 100 m. One plot in the northwest corner of the sample plot is watermelon field, others are corn. The basis of sample selection is: (1) considering different vegetation types, i.e. watermelon and corn; (2) considering the visible light pixel, the sample size of 100m square can guarantee at least 4 30 M-pixel is located in the sample; (3) the location of the sample is near the super station, with convenient transportation. The observation of the super station is in the north, and there is a water net node on both sides of the East and the west, which makes it possible to integrate these observations in the future; (4) in addition, there are some obvious points around the sample, which can ensure that the geometric correction of the SAR image is more accurate in the future. Considering the resolution of the image, 21 splines (distributed from east to West) are collected at 5m intervals. Each line has 21 points (north-south direction) at 5m intervals. Three hydroprobe data acquisition systems (HDAS, reference 2) are used to measure at the same time. The sampling interval is controlled by the scale and moving splines on the measuring line to make up for the lack of using hand-held GPS. Measurement content: About 440 points on the quadrat were obtained, and each point was observed twice, i.e. two times in each sampling point, one time inside the film (marked as a in the data record) and one time outside the film (marked as B in the data record); although the watermelon land was also covered with film, considering that it was not laid horizontally, only the soil moisture at the non covered position was measured (marked as B in the two data records). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and imaginary part of soil complex dielectric are observed. Because the vegetation in this area has been sampled and observed once every five days, no special vegetation synchronous sampling has been carried out on that day. Data: The data format of this data set is vector file, the spatial location is the location of each sampling point (WGS84 + UTM 47N), and the measurement information of soil moisture is recorded in the attribute file.
WANG Shuguo, MA Mingguo, LI Xin
The first dataset of ground truth measurements synchronizing with TerraSAR-X was obtained in the Daman foci experimental area on 4 June, 2012. The satellite image was in StripMap mode and HH/VV polarization with an incidence angle of 22-24°, and the overpass time was approximately at 19:00 UTC+8. The second dataset of ground truth measurements synchronizing with TerraSAR-X was obtained in the Daman foci experimental area on 15 June, 2012. The satellite image was in StripMap mode and HH/VV polarization with an incidence angle of 22-24°, and the overpass time was approximately at 19:00 UTC+8. The third dataset of ground truth measurements synchronizing with TerraSAR-X was obtained in the Daman foci experimental area on 26 June, 2012. The satellite image was in StripMap mode and HH/VV polarization with an incidence angle of 22-24°, and the overpass time was approximately at 19:00 UTC+8. The measurements were conducted at a sampling plot southeast to the Daman Superstation with an area of around 100 m × 100 m, which was dominantly planted with maize. Steven Hydro probes were used to collect soil moisture and other measurements with an interval of 5 m. For each sampling point, two measurements were acquired within an area of 1 m2, with one for the soil covered by plastic film (point name was tagged as LXPXXA) and the other for exposed soil (point name was tagged as LXPXXB). Concurrently with soil moisture sampling, vegetation properties were measured at around 10 locations within this sampling plot. Observation items included: Soil parameters: volumetric soil moisture (inherently converted from measured soil dielectric constant), soil temperature, soil dielectric constant, soil electric conductivity. Vegetation parameters: biomass, LAI, vegetation water content, canopy height, row distance and leaf chlorophyll content. Data and data format: This dataset includes two parts of measurements, i.e. soil and vegetation parameters. The former is as shapefile, with measured items stored in its attribute table. The measured vegetation parameters are recorded in an Excel file.
WANG Shuguo, LI Xin
In July 19, 2012 (UTC+8), the airborne LIDAR data is acquired in the foci area in the Heihe,middle reaches, which can provide high spatial resolution (m) and high precision (20 cm) of the surface elevation information. Based on airborne LIDAR data processing, the land surface DEM, DSM and point cloud density map were generated. By subtracting DSM and DEM directly, a Vegetation height product in the middle reaches of the Heihe River Basin was obtained. The product overall accuracy is 88%.
XIAO Qing, Wen Jianguang
On 30 June 2012 (UTC+8), TASI sensor carried by the Harbin Y-12 aircraft was used in a visible near Infrared hyperspectral airborne remote sensing experiment, which is located in the observation experimental area (30×30 km), Linze region and Heihe riverway. The relative flight altitude is 2500 meters. The wavelength of TASI is 8-11.5 μm with a spatial resolution of 3 meters. Through the ground sample points and atmospheric data, the data are recorded in surface radiance processed by geometric correction and atmospheric correction. Land surface temperature (LST) data was retrieved by temperature/emissivity separation algorithm.
XIAO Qing, Wen Jianguang
The aim of the simultaneous observation of land surface temperature is obtaining the land surface temperature of different kinds of underlying surface, including greenhouse film, the roof, road, ditch, concrete floor and so on, while the sensor of thermal infrared go into the experimental areas of artificial oases eco-hydrology on the middle stream. All the land surface temperature data will be used for validation of the retrieved land surface temperature from thermal infrared sensor and the analysis of the scale effect of the land surface temperature, and finally serve for the validation of the plausibility checks of the surface temperature product from remote sensing. 1. Observation time and other details On 25 June, 2012, ditch and asphalt road surface temperatures were observed once every five minutes using handheld infrared thermometers recorded. On 26 June, 2012, ditch and asphalt road surface temperatures were observed once every five minutes using handheld infrared thermometers while greenhouse film and concrete floor surface temperatures were observed once every one second using self-recording point thermometer. On 29 June, 2012, concrete floor surface temperatures were observed continuously using handheld infrared thermometers during the sensor of TASI go into the region. At the same time, greenhouse film and concrete floor surface temperatures were observed once every one second using self-recording point thermometer. On 30 June, 2012, asphalt road, ditch, bare soil, melonry and ridge of field surface temperatures were observed continuously using handheld infrared thermometers during the sensor of TASI go into the region. At the same time, greenhouse film and concrete floor surface temperatures were observed once every one second using self-recording point thermometer. On 10 July, 2012, asphalt road, ditch, bare soil, melonry and ridge of field surface temperatures were observed once every one minute using handheld infrared thermometers during the sensor of TASI go into the region. At the same time, concrete floor surface temperatures were observed once every six second using self-recording point thermometer. On 26 July, 2012, asphalt road, concrete floor, bare soil and melonry surface temperatures were observed once every one minute using handheld infrared thermometers during the sensor of WiDAS go into the region. At the same time, greenhouse film and concrete floor surface temperatures were observed once every six second using self-recording point thermometer. On 2 August, 2012, corn field and concrete floor surface temperatures were observed using handheld infrared thermometers. At the same time, greenhouse film and concrete floor surface temperatures were observed once every six second using self-recording point thermometer. For corn field, twelve sites were selected according to the flight strip of the WiDAS sensor, and for each site one plot surface temperatures were recorded continuously during the sensor of WiDAS go into the region. On 3 August, 2012, corn field and concrete floor surface temperatures were observed using handheld infrared thermometers. At the same time, greenhouse film and concrete floor surface temperatures were observed once every six second using self-recording point thermometer. For corn field, fourteen sites were selected according to the flight strip of the WiDAS sensor, and for each site three plots surface temperatures were recorded continuously during the sensor of WiDAS go into the region. 2. Instrument parameters and calibration The field of view of the self-recording point thermometer and the handheld infrared thermometer are 10 and 1 degree, respectively. The emissivity of the latter was assumed to be 0.95. The observation heights of the self-recording point thermometer for the greenhouse film and the concrete floor were 0.5 m and 1 m, respectively. All instruments were calibrated three times (on 6 July, 5 August and 20 September, 2012) using black body during observation. 3. Data storage All the observation data were stored in excel.
GENG Liying, Jia Shuzhen, WANG Haibo, PENG Li, Dong Cunhui
Data content: precipitation data of the Aral Sea basin from 2015 to 2018. Data sources and processing methods: from the new generation of global precipitation measurement (GPM) of NASA (version 06, global precipitation observation program), the daily rainfall can be obtained by adding the three-hour rainfall data, and then the eight day rainfall can be obtained. Data quality: the spatial resolution is 0.1 ° x 0.1 ° and the temporal resolution is 8 days. The value of each pixel is the sum of rainfall in 8 days. Data application results: under the background of climate change, it can be used to analyze the correlation between meteorological elements and vegetation characteristics.
XIAO Qing, Wen Jianguang
On 4 July 2012 (UTC+8), a TASI sensor boarded on the Y-12 aircraft was used to obtain the thermal-infrared hyperspectral image, which is located in the observation experimental area, Linze region and Heihe riverway. The relative flight altitude is 1000 meters. The wavelength of TASI is 8-11.5 μm with a spatial resolution of 3 meters. Through the ground sample points and atmospheric data, the data are recorded in surface radiance processed by geometric correction and atmospheric correction. Land surface temperature (LST) data was retrieved by temperature/emissivity separation algorithm.
XIAO Qing, Wen Jianguang
Trough the select tasking, we obtained the WorldView-2 stereo image data in Dayekou Basin production in mid-May 2012. In the same year from July to August, 27 GPS ground control points (GCP) and checkpoints were measured based on the watershed differential GPS control network. Based on the full-field GCPs, the rational polynomial coefficients (RPC) files of WorldView-2 images were corrected in the digital photogrammetry software system. In the stereo model, 60 high-precision tie points evenly distributed were got through image matching technology, and the 1-m and 2-m resolution digital elevation model (DEM) were rapid extracted. Moreover, the DEM was edited in some key areas, such as the shady forest coverage and Dayekou reservoir. The terrain feature points and line data were added to improve the accuracy of the results in large variation of terrain feature. Check points were composed of GPS points and model confidential points, which used for quantitative validation. And they root mean square errors RMSE were 1.9 meters and 1.2 meters respectively, which achieve the requirements of two degree accuracy of 2.0 m at a scale of 1:2000 in high mountains.
Zhang Yanli, MA Mingguo
Trough the select tasking, we obtained the WorldView-2 stereo image data in Dayekou Watershed production in mid-May 2012. In the same year from July to August, 27 GPS ground control points (GCP) and checkpoints were measured based on the watershed differential GPS control network. Based on the full-field GCPs, the rational polynomial coefficients (RPC) files of WorldView-2 images were corrected in the digital photogrammetry software system. In the stereo model, 60 high-precision tie points evenly distributed were got through image matching technology, and the 1-m and 2-m resolution digital elevation model (DEM) were rapid extracted. Based on collinearity equations, images at nadir were corrected to adjust relief displacements and geometric errors, and the 0.5-m resolution digital orthorectified images DOM were obtained with the principle of digital differential rectification in Dayekou Basin.
Zhang Yanli, MA Mingguo
Using ETWatch model with the system complete the heihe river basin scale 1 km resolution 2014 surface evaporation data with middle oasis 30 meters resolution on scale data set, the surface evaporation raster image data of the data sets, it is the time resolution of scale from month to month, the spatial resolution of 1 km scale (covering the whole basin) and 30 meters scale (middle oasis area), the unit is mm.Data types include monthly, quarterly, and annual data. The projection information of the data is as follows: Albers equal-area cone projection, Central longitude: 110 degrees, First secant: 25 degrees, Second secant: 47 degrees, Coordinates by west: 4000000 meter. File naming rules are as follows: 1) 1 km resolution remote sensing data set Monthly cumulative ET value file name: heihe-1km_2014m01_eta.tif Heihe refers to heihe river basin, 1km means the resolution is 1km, 2014 means the year of 2014, m01 means the month of January, eta means the actual evapotranspiration data, and tif means the data is tif format. Name of quarterly cumulative ET value file: heihe-1km_2014s01_eta.tif Heihe represents the heihe river basin, 1km represents the resolution of 1km, 2014 represents the year of 2014, s01 represents the period from January to march, and the first quarter, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Annual cumulative value file name: heihe-1km_2014y_eta.tif Heihe represents the heihe river basin, 1km represents the resolution of 1km, 2014 represents the year of 2014, y represents the year, eta represents the actual evapotranspiration data, and tif represents the data in tif format. 2) remote sensing data set with a resolution of 30 meters Monthly cumulative ET value file name: heihe-midoasa-30m_2014m01_eta.tif Heihe represents the heihe river basin, midoasis represents the mid-range oasis area, 30m represents the resolution of 30 meters, 2014 represents 2014, m01 represents January, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Name of quarterly cumulative ET value file: heihe-midoasa-30m_2014s01_eta.tif Heihe represents the heihe river basin, midoasis represents the mid-range oasis area, 30m represents the resolution of 30 meters, 2014 represents 2014, s01 represents january-march, and the first quarter, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Annual cumulative value file name: heihe-midoasa-30m_2014y_eta.tif Heihe represents the heihe river basin, midoasis represents the mid-range oasis area, 30m represents the resolution of 30 meters, 2014 represents the year of 2014, y represents the year, eta represents the actual evapotranspiration data, and tif represents the data in tif format.
WU Bingfang
The 1km / 5day vegetation index (NDVI / EVI) data set of Heihe River basin provides a 5-day resolution NDVI / EVI composite product in 2015. The data uses the characteristics of China's domestic FY-3 satellite data with high time resolution (1 day) and spatial resolution (1km) to construct a multi angle observation data set. Based on the analysis of the multi-source data set and the existing composite vegetation index products and algorithms A global synthetic vegetation index product algorithm system based on multi-source data set is proposed. The vegetation index synthesis algorithm of MODIS is basically adopted, that is, the algorithm system of BRDF angle normalization method, cv-mvc method and MVC method based on the semi empirical walthal model. Using the algorithm system, the composite vegetation index is calculated for the first level data and the second level data, and the quality is identified. Multi-source data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm system first classifies the quality of multi-source data sets, which can be divided into primary data, secondary data and tertiary data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. In the middle reaches of Heihe River, the verification results of farmland and forest areas show that the NDVI / EVI composite results of combined multi temporal and multi angle observation data are in good agreement with the ground measured data (RMSE = 0.105). Compared with the time series of MODIS mod13a2 product, it fully shows that when the time resolution is increased from 16 days to 5 days, a stable and high-precision vegetation index can describe the details of vegetation growth in detail. In a word, the NDVI / EVI data set of Heihe River Basin, which is 1km / 5day, comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products and better serves the application of remote sensing data products.
LI Jing, LIU Qinhuo, ZHONG Bo, YANG Aixia
The 5-day Lai synthesis results in 2015 are provided by the 1 km / 5-day Lai data set of Heihe River Basin. The data set is constructed by using the data of Terra / MODIS, Aqua / MODIS, as well as the domestic satellites fy3a / MERSI and fy3b / MERSI to construct the multi-source remote sensing data set with a spatial resolution of 1 km and a time resolution of 5 days. Multi-source remote sensing data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm first classifies the quality of multi-source data sets, which can be divided into first level data, second level data and third level data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. The purpose of quality evaluation and classification is to provide the basis for the selection of the optimal data set and the design of inversion algorithm flow. Leaf area index product inversion algorithm is designed to distinguish mountain land and vegetation type, using different neural network inversion model. Based on global DEM map and surface classification map, PROSAIL model is used for continuous vegetation such as grassland and crops, and gost model is used for forest and mountain vegetation. Using the reference map generated by the measured ground data of the forests in the upper reaches of Heihe River and the oasis in the middle reaches, and scaling up the corresponding high-resolution reference map to 1km resolution, compared with the Lai product, the product has a good correlation between the farmland and the forest area and the reference value, and the overall accuracy basically meets the accuracy threshold of 0.5%, 20% specified by GCOS. By cross comparing this product with Lais products such as MODIS, geov1 and glass, the accuracy of this Lai product is better than that of similar products compared with reference value. In a word, the synthetic Lai data set of 1km / 5 days in Heihe River Basin comprehensively uses multi-source remote sensing data to improve the estimation accuracy and time resolution of Lai parameter products, so as to better serve the application of remote sensing data products.
LI Jing, Yin Gaofei, YIN Gaofei, ZHONG Bo, WU Junjun, WU Shanlong
The 1 km / 5-day FVC data set of Heihe River basin provides the 5-day FVC synthesis results in 2015. The data uses the data of Terra / MODIS, Aqua / MODIS, and domestic satellites fy3a / MERSI and fy3b / MERSI to build a multi-source remote sensing data set with a spatial resolution of 1 km and a time resolution of 5 days. The whole country is divided into different vegetation divisions and land types, and the conversion coefficient of NDVI and FVC is calculated respectively. The conversion coefficient look-up table and 1km / 5-day synthetic NDVI product production area 1km / 5-day synthetic FVC product are used. In the Heihe River Basin, 1 km / 5-day synthetic FVC products can directly obtain vegetation coverage ratio through high-resolution data to reduce the impact of low-resolution data heterogeneity; in addition, select the typical period of vegetation growth and change, obtain the corresponding growth curve parameters of each pixel by fitting the vegetation index of each pixel time series; and then cooperate with land use map and vegetation classification map, To find the representative uniform pixel of the region to train the conversion coefficient of vegetation index. Compared with the results of high-resolution aster reference FVC in Heihe River Basin, the first step is to aggregate the aster products in Heihe River basin to 1km scale by combining the measured ground data and using the scale up method, and to obtain the aster aggregate FVC data, which is based on spot vegetation remote sensing data released by geoland 2 project (geov1 for short) The results show that the results of geov1 are higher than those of ASTER image combined with ground measurement, and the results of 1 km / 5-day synthetic FVC products in Heihe River Basin are between the two, and the results of 1 km / 5-day synthetic FVC products in Heihe River Basin in the experimental area are better than those of geov1 products. In a word, the comprehensive utilization of multi-source remote sensing data to improve the estimation accuracy and time resolution of FVC parameter products can better serve the application of remote sensing data products.
MU Xihan, RUAN Gaiyan, ZHONG Bo, LIU Qinhuo
The 30 m / month vegetation index (NDVI / EVI) data set of Heihe River basin provides the monthly NDVI / EVI composite products from 2011 to 2014. This data uses the characteristics of HJ / CCD data of China's domestic satellite, which has both high time resolution (2 days after Networking) and spatial resolution (30 m), to construct multi angle observation data set. The average composite MC method is used as the main algorithm for synthesis, and the backup algorithm uses VI method. At the same time, the main observation angles of the multi-source data set are used as part of the quality descriptor to help analyze the angle effect of the composite vegetation index residue. The remote sensing data acquired every month can provide more angles and more observations than the single day sensor data, but the quality of multi-phase and multi angle observation data is uneven due to the difference of on orbit operation time and performance of the sensor. Therefore, in order to effectively use the multi-temporal and multi angle observation data, before using the multi-source data set to synthesize the vegetation index, the algorithm designs the data quality inspection of the multi-source data set, removing the observation with large error and inconsistent observation. The verification results in the middle reaches of Heihe River show that the NDVI / EVI composite results of the combined multi temporal and multi angle observation data are in good agreement with the ground measured data (R2 = 0.89, RMSE = 0.092). In a word, the 30 m / month NDVI / EVI data set of Heihe River Basin comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products, so as to realize the stable standardized products from scratch and better serve the application of remote sensing data products.
LI Jing, LIU Qinhuo, ZHONG Bo, WU Junjun, WU Shanlong
The 1km / 5day vegetation index (NDVI / EVI) data set of Heihe River basin provides a 5-day resolution NDVI / EVI composite product from 2011 to 2014. The data uses the characteristics of FY-3 data, a domestic satellite, with high time resolution (1 day) and spatial resolution (1km), to construct a multi angle observation data set, which is the basis for analyzing multi-source data sets and existing composite vegetation index products and algorithms On the basis of this, an algorithm system of global composite vegetation index production based on multi-source data set is proposed. The vegetation index synthesis algorithm of MODIS is basically adopted, that is, the algorithm system of BRDF angle normalization method, cv-mvc method and MVC method based on the semi empirical walthal model. Using the algorithm system, the composite vegetation index is calculated for the first level data and the second level data, and the quality is identified. Multi-source data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm system first classifies the quality of multi-source data sets, which can be divided into primary data, secondary data and tertiary data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. In the middle reaches of Heihe River, the verification results of farmland and forest areas show that the NDVI / EVI composite results of combined multi temporal and multi angle observation data are in good agreement with the ground measured data (RMSE = 0.105). Compared with the time series of MODIS mod13a2 product, it fully shows that when the time resolution is increased from 16 days to 5 days, a stable and high-precision vegetation index can describe the details of vegetation growth in detail. In a word, the NDVI / EVI data set of Heihe River Basin, which is 1km / 5day, comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products and better serves the application of remote sensing data products.
LI Jing, LIU Qinhuo, ZHONG Bo, YANG Aixia
The datasets of “Land Cover Map of Heihe River Basin” provide monthly land cover classification data in 2012-2013. The HJ-1/CCD data with both high spatial resolution (30 m) and high temporal (2 days) frequency was used to construct the time series data. The NDVI curves from the time series HJ-1/CCD data can depict the variation of typical land surface. Different land use type has different NDVI curve. Rules were set to extract every land use type information. The datasets of “Land Cover Map of Heihe River Basin” hold the traditional land use types including water bodies, urban and built-up, croplands, evergreen coniferous forests, deciduous broadleaf forests and so on. Crop type classification (including maize, spring wheat, highland barely, rape and so on), snow and ice and glaciers information updates, make the datasets more detailed. Compared with previous land cover map and other products, the classification result of the datasets is visually bette. Especially in middle stream, the accuracy of crop classification is quite high compared with the data from the ground campaign. The accuracy of land cover map of the datasets in 2012 was evaluated using very high spatial resolution remote sensing data within Google Earth and data from campaign, and the overall accuracy can be as high as 92.19%. In a word, the datasets of “Land Cover Map of Heihe River Basin” is not only high in overall accuracy, but also more detailed in crop fine classification. Furthermore, it updated some new classes like glaciers and snow. The datasets of “Land Cover Map of Heihe River Basin” are consequently the classification datasets with the highest accuracy and most detailed information up to now.
ZHONG Bo, YANG Aixia
The 30 m / month synthetic photosynthetic effective radiation absorption ratio (fAPAR) data set of Heihe River basin provides the monthly Lai synthetic products from 2011 to 2014. This data uses the characteristics of HJ / CCD data of China's domestic satellite, which has both high time resolution (2 days after Networking) and spatial resolution (30 m), to construct multi angle observation data set, considering different vegetation types, based on land cover classification map, combined with 30 m /Monthly synthetic leaf area index (LAI) products were produced by fapar-p model based on energy conservation. Based on the principle of energy conservation, the algorithm considers the multiple bounces between vegetation, soil and vegetation, as well as the influence of various factors such as sky scattered light. By analyzing the process of the interaction between photons and canopy, from the point of view that the movement of photons in the canopy is equal to the probability of re collision when multiple scattering occurs, a uniform and continuous vegetation fAPAR model is established. In addition, the effects of various factors on the fAPAR model were analyzed, including soil and leaf reflectance, aggregation index, and G function. The algorithm is highly dynamic, and can get better results for different soil background, vegetation type, radiation conditions, light and observation geometry, weather conditions. Compared with the data of corn canopy par measurement in Yingke irrigation area of Zhangye City, Gansu Province on July 8, 2012, the 30 m / month fAPAR product has a high consistency with the ground observation data, and the error with the observation value is less than 5%. In a word, the 30 m / month synthetic photosynthetic effective radiation absorption ratio (fAPAR) data set of Heihe River Basin comprehensively uses the multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products, and better serves the application of remote sensing data products.
FAN Wenjie, LIU Qinhuo, ZHONG Bo, WU Junjun, WU Shanlong
The 1 km / 5-day FVC data set of Heihe River basin provides the 5-day FVC synthesis results from 2011 to 2014. The data uses the data of Terra / MODIS, Aqua / MODIS, and domestic satellites fy3a / MERSI and fy3b / MERSI to build a multi-source remote sensing data set with a spatial resolution of 1 km and a time resolution of 5 days. The whole country is divided into different vegetation divisions and land types, and the conversion coefficient of NDVI and FVC is calculated respectively. The conversion coefficient look-up table and 1km / 5-day synthetic NDVI product production area 1km / 5-day synthetic FVC product are used. In the Heihe River Basin, 1 km / 5-day synthetic FVC products can directly obtain vegetation coverage ratio through high-resolution data to reduce the impact of low-resolution data heterogeneity; in addition, select the typical period of vegetation growth and change, obtain the corresponding growth curve parameters of each pixel by fitting the vegetation index of each pixel time series; and then cooperate with land use map and vegetation classification map, To find the representative uniform pixel of the region to train the conversion coefficient of vegetation index. Compared with the results of high-resolution aster reference FVC in Heihe River Basin, the first step is to aggregate the aster products in Heihe River basin to 1km scale by combining the measured ground data and using the scale up method, and to obtain the aster aggregate FVC data, which is based on spot vegetation remote sensing data released by geoland 2 project (geov1 for short) The results show that the results of geov1 are higher than those of ASTER image combined with ground measurement, and the results of 1 km / 5-day synthetic FVC products in Heihe River Basin are between the two, and the results of 1 km / 5-day synthetic FVC products in Heihe River Basin in the experimental area are better than those of geov1 products. In a word, the comprehensive utilization of multi-source remote sensing data to improve the estimation accuracy and time resolution of FVC parameter products can better serve the application of remote sensing data products.
MU Xihan, RUAN Gaiyan, ZHONG Bo, LIU Qinhuo
30m month compositing Fraction Vegetation Cover (FVC) data set of Heihe River Basin provides the results of monthly FVC synthesis in 2011-2014. The data constructs multi-angle observation data sets by using China's domestic satellite HJ/CCD data with high temporal resolution (2 days after networking) and spatial resolution (30m) , and divides the country into different vegetation divisions and land types. The conversion coefficients of NDVI and FVC are calculated respectively, and use the calculated conversion coefficient lookup table and monthly compositing NDVI to produce the regional monthly compositing FVC products. The 30m month compositing FVC product in the Heihe River Basin can directly obtain the vegetation coverage ratio through high-resolution data, and mitigate the influence of low-resolution data heterogeneity; in addition, selecting the typical period of vegetation growth change, by fitting the vegetation index of each pixel time series to obtain the growth curve parameters that correspond to each pixel; then the land use map and the vegetation classification map are combined to find the representative uniform pixels of the region for training the conversion coefficients of the vegetation index. Compared with the ASTER reference FVC results, the 30m/month compositing FVC product in the Heihe River Basin is slightly higher than the ASTER reference result, but the overall deviation is not large, and the maximum value of the root mean square error (RMSE) of the product and the reference value is less than 0.175. In addition, compared with the ground survey data of Huailai experimental site in Hebei Province, the 30 m/month compositing FVC products generally reflect the seasonal variation of vegetation growth, and the deviation from the ground survey data is less than 0.1. At the same time, compared with the ground measurements of vegetation coverage in many watersheds in Northeast, North China and Southeast China, the overall error between the compositing FVC products and the ground measurements is less than 0.2. In all, the 30m/month compositing FVC data set of Heihe River Basin comprehensively utilizes multi-temporal and multi-angle remote sensing data to improve the estimation accuracy and time resolution of FVC parameter products, so as to better serve the application of remote sensing data products.
MU Xihan, RUAN Gaiyan, ZHONG Bo, WU Junjun, WU Shanlong, LIU Qinhuo
The 30 m / month synthetic leaf area index (LAI) data set of Heihe River basin provides the monthly Lai synthetic products from 2011 to 2014. This data uses the domestic satellite HJ / CCD data with high time resolution (2 days after Networking) and spatial resolution (30 m) to construct the multi angle observation data set. Considering the impact of surface classification and terrain fluctuation, the algorithm is selected according to the characteristics of different vegetation types Choosing a suitable parameterization scheme of integrated model, inversion Lai based on look-up table method. The remote sensing data acquired every month can provide more angles and more observations than the single day sensor data, but the quality of multi-phase and multi angle observation data is uneven due to the difference of on orbit operation time and performance of the sensor. Therefore, in order to effectively use multi temporal and multi angle observation data, a data quality inspection scheme is designed. Using the Lai ground observation data of 9 forest quadrats, 20 farmland quadrats and 14 savanna quadrats from dayokou area in the upper reaches of Heihe River and Yingke and Linze areas in the middle reaches to verify the Lai in July, the inversion results are in good agreement with the measurement results, and the average error is less than 1; in addition, the Lai inversion results of the combined multi temporal and multi angle observation data are in good agreement with the ground measurement data （R2=0.9，RMSE=0.42）。 In a word, the 30 m / month synthetic leaf area index (LAI) data set of Heihe River Basin comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products, so as to better serve the application of remote sensing data products.
LIU Qinhuo, FAN Wenjie, ZHONG Bo
The vegetation phenology data set of Heihe River basin provides remote sensing phenology products from 2012 to 2015. The spatial resolution is 1km and the projection type is sinusoidal. MODIS Lai product mod15a2 is used as the phenological remote sensing monitoring data source, and MODIS land cover classification product mcd12q1 is used as the auxiliary data set for extraction. The product algorithm first uses the time series data reconstruction method (bise method) to control the data quality of the input time series; then uses the main algorithm (logistic function fitting method) and the backup algorithm (piecewise linear fitting method) to extract the vegetation phenological parameters, realizes the complementary calculation method, guarantees the accuracy and improves the inversion rate. The algorithm can extract up to three growth cycles in a year, each growth cycle contains six data sets, including the start point of vegetation growth, the start point of growth peak, the end point of growth peak, the end point of growth, the fastest growth and the fastest decline. At the same time, it records the growth cycle type, growth season length, quality identification, etc., a total of 25 data sets. The phenology product reduces the missing rate of inversion, improves the stability of the product, and the data set is relatively reliable with rich information.
LI Jing
This data is the ASTER fractional vegetation cover in a growth cycle observed in the Yingke Oasis Crop land. Data observations began on May 30, 2012 and ended on September 12. Original data: 1.15m resolution L1B reflectivity product of ASTER 2.Vegetation coverage data set of the artificial oasis experimental area in the middle reaches Data processing: 1.Preprocessing of ASTER reflectance products to obtain ASTER NDVI; 2.Through the NDVI-FVC nonlinear transformation form, the ASTER NDVI and the ground measured FVC are used to obtain the conversion coefficients of NDVI to FVC at different ASTER scales. 3.Apply this coefficient to the ASTER image to obtain a vegetation coverage of 15m resolution; 4.Aggregate 15m resolution ASTER FVC to get 1km ASTER FVC product
HUANG Shuai, MA Mingguo
The dataset of ground truth measurement synchronizing with the airborne WiDAS mission was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on Jun. 1, 2008. WiDAS, composed of four CCD cameras, one mid-infrared thermal imager (AGEMA 550), and one infrared thermal imager (S60), can acquire CCD, MIR and TIR band data. The simultaneous ground data included: (1) The radiative temperature of maize, wheat and the bare land in Yingke oasis maize field and Huazhaizi desert No. 1 plot by ThermaCAM SC2000 (1.2m above the ground, FOV = 24°×18°). The data included raw data (read by ThermaCAM Researcher 2001), recorded data and the blackbody calibrated data (archived in Excel format). (2) The radiative temperature by the automatic thermometer (FOV: 10°; emissivity: 1.0; from Institute of Remote Sensing Applications), observing straight downwards at intervals of 1s in Yingke oasis maize field. Raw data, blackbody calibrated data and processed data were all archived in Excel format. (3) FPAR (Fraction of Photosynthetically Active Radiation) of maize and wheat by SUNSACN and the digital camera in Yingke oasis maize field. FPAR= (canopyPAR－surface transmissionPAR－canopy reflection PAR+surface reflectionPAR) /canopy PAR; APAR=FPAR* canopy PAR. Data were archived in Excel format. (4) The reflectance spectra by ASD in Yingke oasis maize field (350-2500nm , from BNU, the vertical canopy observation and the transect observation), and Huazhaizi desert No. 1 plot (350-2500nm , from Cold and Arid Regions Environmental and Engineering Research Institute, CAS, the NE-SW diagonal observation at intervals of 30m). The data included raw data (in .doc format), recorded data and the blackbody calibrated data (in Excel format). (5) Maize albedo by the shortwave radiometer in Yingke oasis maize field. R =10H (R for FOV radius; H for the probe height). Data were archived in Excel format. (6) The radiative temperature by the handheld radiometer in Yingke oasis maize field (from BNU, the vertical canopy observation, the transect observation and the diagonal observation), Yingke oasis wheat field (only for the transect temperature), and Huazhaizi desert No. 1 plot (the NE-SW diagonal observation). Besides, the maize radiative temperature and the physical temperature were also measured both by the handheld radiometer and the probe thermometer in the maize plot of 30m near the resort. The data included raw data (in .doc format), recorded data and the blackbody calibrated data (in Excel format). (7) Atmospheric parameters on the playroom roof at the resort by CE318 (produced by CIMEL in France). The underlying surface was mainly composed of crops and the forest (1526m high). The total optical depth, aerosol optical depth, Rayleigh scattering coefficient, column water vapor in 936 nm, particle size spectrum and phase function were then retrieved from these observations. The optical depth in 1020nm, 936nm, 870nm, 670nm and 440nm were all acquired by CE318. Those data include the raw data in .k7 format and can be opened by ASTPWin. ReadMe.txt is attached for detail. Processed data (after retrieval of the raw data) in Excel format are on optical depth, rayleigh scattering, aerosol optical depth, the horizontal visibility, the near surface air temperature, the solar azimuth, zenith, solar distance correlation factors, and air column mass number. (8) Narrow channel emissivity of the bare land and vegetation by the W-shaped determinator in Huazhaizi desert No. 1 plot. Four circumstances should be considered for emissivity, with the lid plus the au-plating board, the au-plating board only, the lid only and without both. Data were archived in Word.
CHEN Ling, HE Tao, REN Huazhong, REN Zhixing, YAN Guangkuo, ZHANG Wuming, XU Zhen, LI Xin, GE Yingchun, SHU Lele, JIANG Xi, HUANG Chunlin, GUANG Jie, LI Li, LIU Sihan, WANG Ying, XIN Xiaozhou, ZHANG Yang, ZHOU Chunyan, LIU Xiaocheng, TAO Xin, CHEN Shaohui, LIANG Wenguang, LI Xiaoyu, CHENG Zhanhui, Liu Liangyun, YANG Tianfu
The dataset of ground truth measurement synchronizing with the airborne WiDAS mission was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on May 30, 2008. WiDAS, composed of four CCD cameras, one mid-infrared thermal imager (AGEMA 550), and one infrared thermal imager (S60), can acquire CCD, MIR and TIR band data. The simultaneous ground data included: (1) The radiative temperature by the handheld radiometer (BNU) in Yingke oasis maize field and Huazhaizi desert maize field (the vertical canopy observation and the transect observation for both fields), and Huazhaizi desert No. 2 plot (the diagonal observation). The data included raw data (in .doc format), recorded data and the blackbody calibrated data (in Excel format). (2) The component temperature of maize and wheat by the handheld radiometer in Yingke oasis maize field, Yingke wheat field and Huazhaizi desert maize field. For maize, the component temperature included the vertical canopy temperature, the bare land temperature and the plastic film temperature; for the wheat, it included the vertical canopy temperature, the half height temperature, the lower part temperature and the bare land temperature. The data included raw data (in .doc format), recorded data and the blackbody calibrated data (in Excel format). (3) The radiative temperature of maize, wheat and the bare land in Yingke oasis maize field by ThermaCAM SC2000 (1.2m above the ground, FOV = 24°×18°), The data included raw data (read by ThermaCAM Researcher 2001), recorded data and the blackbody calibrated data (archived in Excel format). (4) The radiative temperature and the canopy multi-angle radiative temperature by the fixed automatic thermometer (FOV: 10°; emissivity: 1.0), observing straight downwards at intervals of 1s in Yingke oasis maize field (2 instruments for maize canopy), Huazhaizi desert maize field (only one for maize canopy) and Huazhaizi desert No. 2 plot (two for reaumuria soongorica canopy and the bare land). The thermal infrared remote sensing calibration was carried out in the resort plot. Raw data, blackbody calibrated data and processed data were all archived in Excel format. (5) Coverage fraction of maize and wheat by the self-made instrument and the camera (2.5m-3.5m above the ground) in Yingke oasis maize field. Based on the length of the measuring tape and the bamboo pole, the size of the photo can be decided. GPS date were also collected and the technology LAB was applied to retrieve the coverage of the green vegetation. Besides, such related information as the surrounding environment was also recorded. Data included the primarily measured image and final fraction of vegetation coverage. (6) Reflectance spectra of Yingke oasis maize field (350-2500nm, from Institute of Remote Sensing Applications) and resort calibration site (350-2500nm, from Beijing Univeristy) by ASD (Analytical Sepctral Devices); BRDF by the self-made observation platform. Raw data were binary files direct from ASD (by ViewSpecPro), and pre-processed data on reflectance were in Excel format. (7) Atmospheric parameters at the resort calibration site by CE318 (produced by CIMEL in France). The total optical depth, aerosol optical depth, Rayleigh scattering coefficient, column water vapor in 936 nm, particle size spectrum and phase function were then retrieved from these observations. The optical depth in 1020nm, 936nm, 870nm, 670nm and 440nm were all acquired by CE318. Those data include the raw data in .k7 format and can be opened by ASTPWin. ReadMe.txt is attached for detail. Processed data (after retrieval of the raw data) in Excel format are on optical depth, rayleigh scattering, aerosol optical depth, the horizontal visibility, the near surface air temperature, the solar azimuth, zenith, solar distance correlation factors, and air column mass number. (8) Soil moisture (0-40cm) by the cutting ring, the soil temperature by the thermocouple thermometer, roughness by the self-made roughness board and the camera in Huazhaizi desert No. 1 plot. Sample points were selected every 30m along the diagonals. Data were all archived in Excel format. (9) Maize albedo by the shortwave radiometer in Yingke oasis maize field. R =10H (R for FOV radius; H for the probe height). Data were archived in Excel format. (10) FPAR (Fraction of Photosynthetically Active Radiation) by SUNSACN and the digital camera in Yingke oasis maize field. FPAR= (canopyPAR－surface transmissionPAR－canopy reflection PAR+surface reflectionPAR) /canopy PAR; APAR=FPAR* canopy PAR. Data were archived in Word. LAI in Yingke oasis maize field. The maximum leaf length and width of each maize and wheat were measured. Data were archived in Excel format of May 31.
CHAI Yuan, CHEN Ling, HE Tao, KANG Guoting, QIAN Yonggang, REN Huazhong, REN Zhixing, WANG Haoxing, ZHANG Wuming, ZOU Jie, GE Yingchun, SHU Lele, WANG Jianhua, XU Zhen, GUANG Jie, LIU Sihan, XIN Xiaozhou, ZHANG Yang, ZHOU Chunyan, LIU Xiaocheng, TAO Xin, LIANG Wenguang, WANG Dacheng, LI Xiaoyu, CHENG Zhanhui, YANG Tianfu, HUANG Bo, LI Shihua, LUO Zhen
The dataset of ground truth measurement synchronizing with the airborne WiDAS mission was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on Jul. 11, 2008. WiDAS, composed of four CCD cameras, one mid-infrared thermal imager (AGEMA 550), and one infrared thermal imager (S60), can acquire CCD, MIR and TIR band data. The simultaneous ground data included: (1) Atmospheric parameters in Huazhaizi desert No. 2 plot from CE318 (produced by CIMEL in France). The total optical depth, aerosol optical depth, Rayleigh scattering coefficient, column water vapor in 936 nm, particle size spectrum and phase function were then retrieved from these observations. The optical depth in 1020nm, 936nm, 870nm, 670nm and 440nm were all acquired by CE318. Those data include the raw data in k7 format and can be opened by ASTPWin. ReadMe.txt is attached for details. Processed data (after retrieval of the raw data) in Excel format are on optical depth, Rayleigh scattering, aerosol optical depth, the horizontal visibility, the near surface air temperature, the solar azimuth, zenith, solar distance correlation factors, and air column mass number. (2) Radiative temperature of maize, wheat and the bare land (in Yingke oasis maize field), vegetation and the bare land (Huazhaizi desert No. 2 plot) by the thermal cameras at a height of 1.2m above the ground. Optical photos of the scene were also taken. Raw data (read by ThermaCAM Researcher 2001) was archived in IMG format and radiative files are stored in Excel format. . (3) Photosynthesis by LI6400 in Yingke oasis maize field, carried out according to WATER specifications. Raw data were archived in the user-defined format (by notepat.exe) and processed data were in Excel format. (4) Ground object reflectance spectra in Yingke oasis maize field, Huazhaizi maize field, Huazhaizi desert No. 1 and 2 plots, by ASD FieldSpec (350～2500 nm) from Institute of Remote Sensing Applications (IRSA), CAS. Raw data were binary files direct from ASD (by ViewSpecPro), which were recorded daily in detail, and pre-processed data on reflectance were in .txt format. (5) The radiative temperature in Huazhaizi desert No. 2 plot by the handheld infrared thermometer (BNU and IRSA). Raw data, blackbody calibrated data and processed data (in Excel format) were all archived. (6) FPAR (Fraction of Photosynthetically Active Radiation) by SUNSACN and the digital camera in Yingke oasis maize field. FPAR= (canopyPAR－surface transmissionPAR－canopy reflection PAR+surface reflectionPAR) /canopy PAR; APAR=FPAR* canopy PAR. Data were archived in Excel format. (7) The radiative temperature of the maize canopy by the automatic thermometer (FOV: 10°; emissivity: 0.95) mearsued at nadir with an time intervals of 1s in Huazhaizi desert maize field. Raw data, blackbody calibrated data and processed data were all archived as Excel files. (8) Maize albedo from two shortwave radiometer in Yingke oasis maize field. R =10H (R for FOV radius; H for the probe height). Data were archived in Excel format.
REN Huazhong, WANG Tianxing, YAN Guangkuo, LI Li, LI Hua, LIU Sihan, XIA Chuanfu, XIN Xiaozhou, ZHOU Chunyan, ZHOU Mengwei, YANG Guijun, LI Xiaoyu, CHENG Zhanhui, Liu Liangyun
The dataset of ground truth measurement synchronizing with the airborne WiDAS mission was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on Jun. 29, 2008. WiDAS, composed of four CCD cameras, one mid-infrared thermal imager (AGEMA 550), and one infrared thermal imager (S60), can acquire VNIR, MIR and TIR band data. The simultaneous ground data included: (1) Atmospheric parameters in Huazhaizi desert No. 2 plot from CE318 (produced by CIMEL in France). The total optical depth, aerosol optical depth, Rayleigh scattering coefficient, column water vapor in 936 nm, particle size spectrum and phase function were then retrieved from these observations. The optical depth in 1020nm, 936nm, 870nm, 670nm and 440nm were all acquired by CE318. Those data include the raw data in .k7 format and can be opened by ASTPWin. ReadMe.txt is attached for detail. Processed data in Excel format are on optical depth, rayleigh scattering, aerosol optical depth, the horizontal visibility, the near surface air temperature, the solar azimuth, zenith, solar distance correlation factors, and air column mass number. (2) Emissivity of maize and wheat in the Yingke oasis by portable 102F (2.0~25.0um) from BNU. Warm blackbody, cold blackbody, the target and the au-plating board of known emissivity. Raw data of those four measurements were archived in *.WBX, *.CBX, *.SAX and *.CBX Besides, the spectral radiance and emissivity calculated by 102F were archived in *.RAX and *.EMX, respectively. Meanwhile, the final spectral emissivity of targets were also calculated by TES (ISSTES). (3) LAI of mazie and wheat in Yingke oasis maize field. The maximum leaf length and width of leaves were measured. Data were archived as Excel files of Jul. 2. (4) FPAR (Fraction of Photosynthetically Active Radiation) of maize and wheat by SUNSACN and the digital camera in Yingke oasis maize field. FPAR= (canopyPAR－surface transmissionPAR－canopy reflection PAR+surface reflectionPAR) /canopy PAR; APAR=FPAR* canopy PAR. Data were archived in MS Office Word format. (5) the radiative temperature by the automatic thermometer (FOV: 10°; emissivity: 0.95), measured at nadir with time intervals of one second in Yingke oasis maize field (one from BNU and the other from Institute of Remote Sensing Applications), Huazhaizi desert maize field (only one from BNU for continuous radiative temperature of the maize canopy) and Huazhaizi desert No. 2 plot (two for reaumuria soongorica canopy and the background bare soil). Raw data, blackbody calibrated data and processed data were all archived as Excel files. (6) the component temperature in Yingke oasis maize field (by the handheld radiometer and the thermal image from BNU), Yingke oasis wheat field and Huazhaizi desert maize field. For maize, the component temperature included the vertical canopy temperature, the bare land temperature and the plastic film temperature; for the wheat, it included the vertical canopy temperature, the half height temperature, the lower part temperature and the bare land temperature. The data included raw data (in MS Office Word format), recorded data and the blackbody calibrated data (in Excel format). (7) Maize albedo by the shortwave radiometer in Yingke oasis maize field. R =10H (R for FOV radius; H for the observation height). Data were archived in MS Office Excel format. (8) the radiative temperature by the handheld radiometer in Yingke oasis maize field and Huazhaizi desert maize field (the vertical canopy observation and the transect observation for both fields), and Huazhaizi desert No. 2 plot (the NE-SW diagonal observation). The data included raw data (in .doc format), recorded data and the blackbody calibrated data (in Excel format). (9) ground object reflectance spectra in Yingke oasis maize field by ASD FieldSpec (350～2 500 nm) from BNU. The vertical canopy observation and the line-transect observation were used. The data included raw data (from ASD, read by ViewSpecPro), recorded data and processed data on reflectance (in Excel format).
CHEN Ling, GUO Xinping, REN Huazhong, WANG Tianxing, XIAO Yueting, YAN Guangkuo, CHE Tao, GE Yingchun, GAO Shuai, LI Hua, LI Li, LIU Sihan, SU Gaoli, WU Mingquan, XIN Xiaozhou, ZHOU Chunyan, ZHOU Mengwei, FAN Wenjie, SHEN Xinyi, YU Fan, YANG Guijun, Liu Liangyun
The dataset of the ground-based microwave radiometers and ground truth observations (multi-frequency, multi-polar multi-angle) for soil freeze/thaw cycle in the A'rou foci experimental area from Oct. 19 to 25, 2007, during the pre-observation period, X-band from Oct. 20 to 25, S-band from Oct. 20 to 25, K-band from Oct. 19 to 24, and Ka-band from Oct. 20 to 24, to be specific. The aims of the measurements were the effects of the soil freeze/thaw status on the microwave brightness temperatures. Those provide reliable ground data for improving and verifying microwave radiative transfer models and parameters retrieval of soil freeze/thaw status. Time-continuous ground observations synchronizing with the ground-based microwave radiometers including self-recording and manual measurements, were carried out in No. 1 quadrate of A'rou with dry natural grassland as the landscape. (1) self-recording observations: the soil temperatures at 0cm, 5cm, 10cm, 15cm and 20cm by the temperature probe from Oct. 21 to 25, 2007, and shallow layer soil moisture at 0-5cm, 5cm, 10cm, 15cm and 20cm by TDR from Oct. 19 to 21 2007. Both time interval of the observations were 5 minutes. (2) manual observations: the surface radiative temperature by the handheld infrared thermometer, the soil temperature at 0cm, 5cm, 10cm, 15cm and 20cm by the glass geothermometer, and the mean soil temperature from 0-5cm by the probe thermometer. The time interval of observations was 30 minutes from Oct. 19-21, 2007.
BAI Yunjie, CAO Yongpan, HAO Xiaohua, LI Hongyi, LI Xin, LI Zhe, QIN Chun, Wang Weizhen
The dataset of the ground-based microwave radiometers and ground truth observations for soil freeze/thaw cycle was obtained in the A'rou foci experimental area (N38º03.639'/E100º26.793'; 2998m) from May 5 to 8, 2008, S-band from Apr. 6 to 8, C-band from Apr. 7 to 8, K-band from Apr. 5 to 8, and Ka-band on Apr. 5, to be specific. The aims of the measurements were the effects of the soil freeze/thaw status on the microwave brightness temperatures. The observation site was bare land and the soil moisture was 30% after artificial irrigation. Observation items included the soil temperature at 5cm automatically (the time interval: 10m), the soil temperature at 5cm, 10cm, 20cm and 30cm by the probe thermometer (the time interval: 1h), and the soil moisture at 5cm, 10cm, 20cm and 30cm automatically (the time interval: 10m). Seven files were included, four ground-based microwave radiometers (S-band, C-band, K-band and Ka-band) observations, the automatic soil temperature, the manual soil temperature, and the automatic soil moisture, and the last three were archived in Excel format.
CAO Yongpan, CHE Tao, HAO Xiaohua, LI Zhe, Wang Weizhen, WU Yueru
The dataset of the survey at the sampling plots in the transit zone between oasis and desert was obtained in the Linze station foci experimental area. Observation items included: (1) soil moisture and temperature of the soil profiles (0-10cm, 10-20cm, 20-30cm and 30-40cm) measured by the cutting ring method (50cm^3, once each layer) and the probe thermometer (15cm, twice each layer) on May 25, 2008. Data were archived as Excel files. (2) biomass (green weight and dry weight, samples from 0.5m×0.5m) with photos measured by the plant harvesting in LY07 quadrate on Jun. 22, 2008. Data were archived as Excel files. (3) vegetation coverage measured by the diagonal method on Jun. 22, 2008. By estimating the coverage along the two diagonals, the total coverage of the plot can be developed. Data were archived as Excel files.
GAO Song, PAN Xiaoduo, Qian Jinbo, SONG Yi, WANG Yang, ZHU Shijie
The dataset of ground truth measurement synchronizing with the airborne WiDAS mission and Landsat TM was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on Jul. 7, 2008. Observation items included: (1) the radiative temperature by the thermal camera (Institute of Remote Sensing Applications) of maize, wheat and the bare land of Yingke oasis maize field at a height of 1.2m above the ground. Optical photos of the scene were also taken. Raw data (read by ThermaCAM Researcher 2001) was archived in IMG format, and blackbody calibrated data and processed data were all archived as Excel files. (2) Maize albedo by the shortwave radiometer in Yingke oasis maize field. R =10H (R for FOV radius; H for the probe height). Data were archived in Excel format. (3) Reflectance spectra in Yingke oasis maize field by ASD FieldSpec (350-1603nm) from Institute of Remote Sensing Applications (CAS). The grey board and the black and white cloth were also used for calibration on the CCD camera. Raw data were binary files direct from ASD (by ViewSpecPro), and pre-processed data on reflectance were in Excel format. (4) the component temperature by the handheld radiometer in Yingke oasis maize field and Huazhaizi desert maize field. For maize, the component temperature included the vertical canopy temperature, the bare land temperature and the plastic film temperature; for the wheat, it included the vertical canopy temperature, the half height temperature, the lower part temperature and the bare land temperature. The data included raw data (in Word format), recorded data and the blackbody calibrated data (in Excel format). (5) the radiative temperature by the handheld radiometer (emissivity = 1.0) in Yingke oasis maize field (for the canopy mean temperature), Huazhaizi desert maize field (for the transect temperature), Zhangye airport (the black and white cloth for calibration) and Huazhaizi desert No. 2 plot (the diagonal radiative temperature and the radiative temperature of 30m*30m subplot). The component temperature was also measured. The data included raw data (in Word format), recorded data and the blackbody calibrated data (as Excel files). (6) The air temperature (°C) , the soy bean leaf temperature (°C) and the maize leaf temperature (°C) by SPAD (from Institute of Remote Sensing Applications (CAS)) in Yingke oasis maize field. Besides, spectrum, photosynthesis, fluorescence and chlorophyll were measured as well. (7) The leaf reflectance spectra ASD (serial number: 64831) and 50% grey board from Institute of Remote Sensing Applications (CAS). The spectral DN was changed into radiance based on the 50% grey board calibration data and calibration lamp data, which could further be transformed into Excel format. Moreover, the solar radiance=the reference board radiance/the reference reflectance. (8) The leaf fluorescence by ImagingPam from Beijing Academy of Agriculture and Forestry Sciences. YII = (Fm'-F)/Fm' was applied for caculation, F indicating fluorescence before saturating flash light, Fm' the maximum fluorescence before saturating flash light, and YII the quantum yield of photosystem II. Data were archived in pim and could be read by ImagingPam, which can be downloaded from http://www.zealquest.com. (9) The leaf photosynthesis by LI-6400. (10) The radiative temperature by the automatic thermometer (FOV: 10°; emissivity: 0.95), observing straight downwards at intervals of 1s in Yingke oasis maize field and Huazhaizi desert maize field. Raw data, blackbody calibrated data and processed data were all archived in Excel format. (11) FPAR (Fraction of Photosynthetically Active Radiation) by SUNSACN and the digital camera in Yingke oasis maize field. FPAR= (canopyPAR－surface transmissionPAR－canopy reflection PAR+surface reflectionPAR) /canopy PAR; APAR=FPAR* canopy PAR. Data were archived in the table format of Word. (12) Atmospheric parameters near Daman Water Management office by CE318 (produced by CIMEL in France). The total optical depth, aerosol optical depth, Rayleigh scattering coefficient, column water vapor in 936 nm, particle size spectrum and phase function were then retrieved from these observations. The optical depth in 1020nm, 936nm, 870nm, 670nm and 440nm were all acquired by CE318. Those data include the raw data in k7 format and can be opened by ASTPWin. ReadMe.txt is attached for detail. Processed data (after retrieval of the raw data) in Excel format are on optical depth, Rayleigh scattering, aerosol optical depth, the horizontal visibility, the near surface air temperature, the solar azimuth, zenith, solar distance correlation factors, and air column mass number.
CHEN Ling, REN Huazhong, WANG Tianxing, YAN Guangkuo, HAO Xiaohua, WANG Shuguo, LI Li, LI Hua, LIU Sihan, SU Gaoli, XIA Chuanfu, XIN Xiaozhou, ZHOU Chunyan, ZHOU Mengwei, LI Xinhui, YU Fan, ZHU Xiaohua, YANG Guijun, CHENG Zhanhui, Liu Liangyun
The data set contains all single glacial reserves (in KM3) in the Tibetan Plateau of 1970s and 2000s. This data set comes from the result data of the paper entitled "consolidating the Randolph glacier inventory and the glacier inventory of China over the Qinghai titanium plate and investigating glacier changes since the mid-20th century". The first draft of this paper has been completed and is planned to be submitted to earth system science data. The 1970s basic glacier catalog data in the dataset is extracted from Randolph glacier Inventory data set, 2000s basic glacial catalogue is from China's second glacial catalogue data set. Based on the glacial boundary extracted from the two data sets and combined with the grid based bedrock elevation data set (https://www.ngdc.noaa.gov/mgg/global/global.html, DOI: 10.7289/v5c8276m) and the glacial table obtained by a slope dependent method Based on the surface elevation data set, the single glacier reserves in the two catalogues are calculated. In addition, the calculation results of single glacier reserves obtained in this study have been compared and verified with the calculation results of partial glacier reserves, relevant remote sensing data sets, and the global glacier thickness data set based on the average of multiple glacier model sets in multiple directions, and the errors in the calculation results have also been quantified. The establishment of the data set is expected to provide the data basis for the future regional water resources estimation and glacier ablation research, and the acquisition of the data also provides a new idea for the future glacier reserves research.
WANG Zhongjing
The proportion data set of daily cloudless MODIS snow cover area in babaohe river basin (2008.1.1-2014.6.1) was obtained after cloud removal processing using a cloud removal algorithm based on cubic spline function interpolation on the basis of daily cloudless MODIS snow cover product-mod10a1 (tang zhiguang, 2013). This data set adopts the projection method of UTM (horizontal axis isometric cutting cylinder), with a spatial resolution of 500m, and provides Daily Snow Albedo daily-sad results for the babao river basin.The data set is a daily file from January 1, 2008 to June 1, 2014.Each file is the snow albedo result of the day, with a value of 0-100 (%), is the ENVI standard file, and the naming rule is: mod10a1.ayyyyddd_h25v05_snow_sad_grid_2d_reproj_babaohe_nocloud.img, where YYYY represents the year, DDD stands for Julian day (001-365/366).The file can be opened directly with ENVI or ARCMAP software. The original MODIS snow cover data products processed by declouding are derived from MOD10A1 products processed by the us national snow and ice data center (NSIDC). This data set is in HDF format and USES sinusoidal projection. The attributes of the cloud-free MODIS albedo data set (2008.1.1-2014.1.1) in babaohe river basin are composed of the spatial and temporal resolution, projection information and data format of the dataset.
WANG Jian, PAN Haizhu
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