The data source of this data set is the European Space Agency (ESA) multispectral satellite Sentinel-2. It includes the annual mean data of CDOM and DOC of Qinghai Tibet Plateau lakes in 2017. Method of use: Based on the CDOM data of the measured sample points, the image reflectance information is extracted, the best prediction variable is selected through Pearson correlation analysis, and a multiple stepwise regression CDOM prediction model is constructed to obtain the CDOM results of the Qinghai Tibet Plateau water body. Because CDOM has a good correlation with DOC, DOC prediction results are calculated by CDOM. Adjustment R of the CDOM model of the final Qinghai Tibet Plateau ² Up to 0.81.
SONG Kaishan
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
Terrestrial actual evapotranspiration (ET) is an essential ecohydrological process linking the land surface energy, water and carbon cycles, and plays a critical role in the earth system. This global ET dataset is obtained based on ETMonitor model, which combines parameterizations for different processes and land cover types, with multi-source satellite data as input. Several open accessed remote sensing variables, e.g., LAI, FVC, albedo, surface soil moisture, dynamic surface water cover and snow/ice cover, were used as input to estimate daily ET. The meteorological variables from ERA5 reanalysis dataset were also adopted. The ETMonitor model is applied at daily scale to estimate the ET components at 1-km resolution, including vegetation transpiration, soil evaporation, canopy precipitation interception loss, water surface evaporation and snow/ice sublimation on daily step, and the total actual ET is estimated as the sum of these components. Overall, the actual ET estimated by ETMonitor agreed well with ground measurements from 251 flux towers across various ecosystems and climate zones globally, with high correlation (0.75), low bias (0.08mm/d), and low root mean square error (0.93 mm/d). The estimated ET showed reasonable spatial patterns, and superior in presenting the spatial variation of ET especially in the mountain regions and in the arid irrigated cropland regions. The ET estimation is conducted at daily temporal step and 1km spatial resolution. For easier publication, the daily/1-km ET from ETMonitor (https://doi.org//10.12237/casearth.6253cddc819aec49731a4bc2) was summed to obtain monthly ET in this dataset. The data type is 16-bit signed integer, the scale factor is 0.1, and the unit is mm/month. The missing values were filled by -1.
ZHENG Chaolei , JIA Li , HU Guangcheng
Timely and correct observation of the spatial and temporal patterns and dynamics of oases is important for the property socioeconomic development of arid zones. During this study, a complete of 9 periods of Landsat image knowledge in 1986, 1990, 1995, 2000, 2005, 2010, 2015, 2018, and 2020 were accustomed get oasis distribution knowledge within the Hexi region from 1986 to 2020 employing a combination of OSTU threshold methodology and manual visual interpretation, and combined with high-resolution Google Earth pictures and field validation knowledge were combined to ascertain random sample points supported confusion matrix to verify the accuracy of oasis extraction results. The overall accuracy of oasis data in Hexi Corridor is over 94%, and the Kappa coefficient is over 0.88. This dataset can provide data support for the ecological environment protection of Hexi oasis.
XIE Yaowen, ZHANG Xueyuan, LIU Yiyang, HUANG Xiaojun, LI Ruyan, ZONG Leli, XIAO Min, QIN Mengyao
Based on the Sentinel-2 and Landsat 5/7/8 multispectral instrument imageries combined with in-situ measured hydrological data, bankfull river geometry of six major exorheic river basins of the Qinghai-Tibet Plateau (the upper Yellow River, upper Jinsha River, Yalong River, Lantsang River, Nu River and Yalung Zangbo River) are presented. River surface of six mainstreams and major tributaries are included. For each river basin, two types of rivers are included: connected and disconnected rivers. Format of the dataset is .shp exported from the ArcGIS 10.5. Three products are included in the dataset: one original product (bankfull river surface dataset) and two derived products (bankfull river width dataset and bankfull river surface area dataset with a 1 km river length interval). These three products are in three folders. The first folder, “1-Bankfull River Surface”, contains river surface vectors for six river basins in the .shp file. The second folder, “2-Bankfull River Width”, contains bankfull river widths and corresponding coordinates with a 1 km-step river length for six mainstreams and some connected tributaries in .xlsx format. The river width vectors in the .shp files are also provided in the second folder. The third folder, “3-Bankfull River Surface Area”, contains bankfull river surface areas and corresponding coordinates with a 1 km-step river length for six mainstreams and some connected tributaries in .xlsx format. Three Supplementary Files are included: Supplementary File 1, tables and figures related to the dataset; Supplementary File 2, used for river surface extraction based on GEE platform; Supplementary File 3, used for river width extraction based on Matlab. The provided planform river hydromorphology data can supplement global hydrography datasets and effectively represent the combined fluvial geomorphology and geological background in the study area.
LI Dan , XUE Yuan , QIN Chao , WU Baosheng , CHEN Bowei , WANG Ge
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
Net Primary Productivity (NPP) refers to the total amount of organic matter produced by photosynthesis in green plants per unit time and area. As the basis of water cycle, nutrient cycle and biodiversity change in terrestrial ecosystems, NPP is an important ecological indicator for estimating earth support capacity and evaluating sustainable development of terrestrial ecosystems. This data set includes the monthly synthesis of 30m*30m surface LAI products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly NPP products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
WU Junjun , LI Yi, ZHONG Bo
Leaf Area Index (LAI) is defined as half of the total Leaf Area within the unit projected surface Area, and is one of the core parameters used to describe vegetation. LAI controls many biological and physical processes of vegetation, such as photosynthesis, respiration, transpiration, carbon cycle and precipitation interception, and meanwhile provides quantitative information for the initial energy exchange on the surface of vegetation canopy. LAI is a very important parameter to study the structure and function of vegetation ecosystem. This data set includes the monthly synthesis of 30m LAI products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly LAI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
WU Junjun , LI Yi, ZHONG Bo
Normalized Difference Vegetation Index (NDVI) is the sum of the reflectance values of the NIR band and the red band by the Difference ratio of the reflectance values of the NIR band and the red band. Vegetation index synthesis refers to the selection of the best representative of vegetation index within the appropriate synthesis cycle, and the synthesis of a vegetation index grid image with minimal influence on spatial resolution, atmospheric conditions, cloud conditions, observation geometry, and geometric accuracy and so on. This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly NDVI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
WU Junjun , LI Yi, ZHONG Bo
Fractional Vegetation Coverage (FVC) is defined as the proportion of the vertical projection area of Vegetation canopy or leaf surface to the total Vegetation area, which is an important indicator to measure the status of Vegetation on the surface. In this dataset, vegetation coverage is an evaluation index reflecting vegetation coverage. 0% means that there is no vegetation in the surface pixel, that is, bare land. The higher the value, the greater the vegetation coverage in the region. This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly FVC products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
WU Junjun , LI Yi, ZHONG Bo
China cloud-removal snow albedo product data set is raster data with a geospatial extent of 72 - 142E, 16 - 56N, using an equal latitude and longitude projection and a spatial resolution of 0.005°. The dataset covers the period from 1 January 2000 to 31 December 2020 with a temporal resolution of 1 day. The data contains six elements: black sky albedo (Black_Sky_Albedo), white sky albedo (White_Sky_Albedo), solar zenith angle (Solar_Zenith_Angle), pixel-level cloud label (Cloud_Mask), pixel-level forest pixel (Forest_Mask) and pixel-level retrieval label (Abnormal_Mask). Black_Sky_Albedo records the black sky albedo calculated by retrieved, with as a calculation factor of 0.0001 and a data range of 0-10000. White_Sky_Albedo records the white sky albedo calculated by retrieved, with as a calculation factor of 0.0001 and a data range of 0-10000. Cloud_Mask records whether the pixel is cloud type, with a value of 0 indicating non-cloud and 1 indicating cloud. Forest_Mask records whether the pixel has been corrected as a forest type, with a value of 0 indicating that it has not been corrected and 1 indicating that it has been corrected. Abnormal_Mask records whether the retrieval of the black sky albedo and white sky albedo of the pixel is an anomaly of less than 0 or greater than 10000, with a value of 0 indicating a non-anomaly and 1 indicating an anomaly. ChinaSA was retrieved based on the MODIS land surface reflectance product MOD09GA, the snow cover product MOD10A1/MYD10A1 and the global digital elevation model SRTM. The snow albedo retrieval model was developed based on the ART model and produced using the GEE and local side interactions.
XIAO Pengfeng , HU Rui , ZHANG Zheng , QIN Shen
The considerable amount of solid clastic material in the Yarlung Tsangpo River Basin (YTRB)) is one of the important components in recording the uplift and denudation history of the Tibet Plateau. Different types of unconsolidated sediments directly reflect the differential transport of solid clastic material. Revealing its spatial distribution and total accumulation plays an important value in the uplift and denudation process of the Tibet Plateau. The dataset includes three subsets: the type and spatial distribution of unconsolidated sediments in theYTRB, the thickness spatial distribution, and the quantification of total deposition. Taking remote sensing interpretation and geological mapping as the main technical method, the classification and spatial distribution characteristics of unconsolidated sediments in the whole YTRB (16 composite sub-basins) were comprehensively clarified for the first time. Based on the field measurement of sediment thickness, the total accumulation was preliminarily estimated. A massive amount of sediment is an important material source of landslide, debris flow and flood disasters in the basin. Finding out its spatial distribution and total amount accumulation not only has theoretical significance for revealing the key information recorded in the process of sediment source to sink, such as surface environmental change, regional tectonic movement, climate change and biogeochemical cycle, but also has important application value for plateau ecological environment monitoring and protection, flooding disaster warning and prevention, major basic engineering construction, and soil and water conservation.
LIN Zhipeng, WANG Chengshan , HAN Zhongpeng, BAI Yalige, WANG Xinhang, ZHANG Jian, MA Xinduo, HU Taiyu, ZHANG Chenjin
This dataset contains daily land surface evapotranspiration products of 2021 in Qilian Mountain area. It has 0.01 degree spatial resolution. The dataset was produced based on Gaussian Process Regression (GPR) method by fusing six satellite-derived evapotranspiration products including RS-PM (Mu et al., 2011), SW (Shuttleworth and Wallace., 1985), PT-JPL (Fisher et al., 2008), MS-PT (Yao et al., 2013), SEMI-PM (Wang et al., 2010a) and SIM (Wang et al.2008). The input variables for the evapotranspiration products include MODIS products, and MERRA meteorological data.
YAO Yunjun, LIU Shaomin, SHANG Ke
ChinaSA is raster data with a geospatial extent of 72 - 142E, 16 - 56N, using an equal latitude and longitude projection and a spatial resolution of 0.005°. The dataset covers the period from 1 January 2000 to 31 December 2020 with a temporal resolution of 1 day. The data contains six elements: black sky albedo (Black_Sky_Albedo), white sky albedo (White_Sky_Albedo), solar zenith angle (Solar_Zenith_Angle), pixel-level cloud label (Cloud_Mask), pixel-level forest pixel (Forest_Mask) and pixel-level retrieval label (Abnormal_Mask). Black_Sky_Albedo records the black sky albedo calculated by retrieved, with as a calculation factor of 0.0001 and a data range of 0-10000. White_Sky_Albedo records the white sky albedo calculated by retrieved, with as a calculation factor of 0.0001 and a data range of 0-10000. Cloud_Mask records whether the pixel is cloud type, with a value of 0 indicating non-cloud and 1 indicating cloud. Forest_Mask records whether the pixel has been corrected as a forest type, with a value of 0 indicating that it has not been corrected and 1 indicating that it has been corrected. Abnormal_Mask records whether the retrieval of the black sky albedo and white sky albedo of the pixel is an anomaly of less than 0 or greater than 10000, with a value of 0 indicating a non-anomaly and 1 indicating an anomaly. ChinaSA was retrieved based on the MODIS land surface reflectance product MOD09GA, the snow cover product MOD10A1/MYD10A1 and the global digital elevation model SRTM. The snow albedo retrieval model was developed based on the ART model and produced using the GEE and local side interactions. To assess the retrieval quality of ChinaSA, the accuracy of the snow albedo product was verified using observations from in-situ meteorological stations and the sample observation validation method, and compared with the accuracy of four commonly used albedo products (GLASS, GlobAlbedo, MCD43A3 and SAD). The validation results show that ChinaSA outperforms the other products in all validations, with a root mean square error (RMSE) of less than 0.12, and can achieve a RMSE of 0.021 in forest areas.
XIAO Pengfeng , HU Rui , ZHANG Zheng , QIN Shen
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
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
This data set is based on the remote sensing monitoring data set of landuse status in China, Chinese Academy of Sciences, and the data of land use types of Qilian Mountain National Park in 1985 through cutting, splicing and other operations. Data production is the vector data generated by manual visual interpretation using Landsat TM / ETM Remote sensing images as the main data source. Landuse types include cropland, forest, shrub, grassland, wetland, water, tundra, impervious surface, bareland, glacier and permanent snow. We can analyze the historical landuse types in Qilian mountain area, and analyze the changes of land use types in Qilian mountain area combined with the current landuse type data.
NIAN Yanyun
Lidar, multispectral and thermal infrared data are important observation data in the research fields of hydrology, ecology and environmental monitoring. This data set is the observation data of UAV in the integrated observation experiment of heaven and earth in the middle reaches of Heihe River Basin in 2020. The data set includes UAV remote sensing data from August 16 to 21, 2020. The UAV platform is Dajiang Phantom 4-multispectral version. Including lidar data of Daman superstation (August 16-21), Huazhaizi station (August 19) and Wetland station (August 21). The laser scanning system is Tovos DroneScan, with scanning frequency of 300000 points/s, point density of 100 points/m2 and scanning accuracy of 5 cm; Multispectral data of Daman superstation (August 18), Huazhaizi station (August 19) and Wetland station (August 21). The data set includes five bands of images, namely blue (450nm ± 16nm), green (560nm ± 16nm), red (650nm ± 16nm), red edge (730nm ± 16nm) and near infrared (840nm ± 26nm); And NDVI and reflectance data products corresponding to Wetland station and Huazhaizi station. The spatial resolution of the above data is about 0.2 m; In addition, it also includes the thermal infrared data of Huazhaizi station (August 18 and 19) and Wetland station (August 21). The wavelength range of thermal infrared channel is 7.5-13.5 μm. Imaging system sensitivity (nedt) < 50 mk, maximum frame rate: 30Hz, scene range (high gain): 640 × 512: -25° to 135℃, 336 × 256: -25° to 100℃, scene range (low gain): -40° to 550℃.
This vegetation water content data set is derived from the ground synchronous observation in the Luanhe River Basin soil moisture remote sensing experiment, including 55 sampled plots.The vegetation types involved in these sampled plots include grass, corn, potatoes, naked oats and carrots. The data measurement time is from September 13, 2018 to September 26, 2018.
ZHENG Xingming, JIANG Tao
Water clarity, as a first-order indicator that reflects the optical characteristics of water bodies, represents a comprehensive proxy for aquatic ecosystems’ trophic state. Optical remote sensing technology makes it possible to monitor water clarity changes of lakes (including reservoirs) at large scales. Water clarity annual dynamics dataset of lakes (>1 ha) across China covers the period from 1990 to 2018, with a time resolution of 5-year and spatial resolution of 30 meters, which sources from the Landsat top of air reflectance data embedded in the GEE platform. Three in-situ SDD measurement datasets were used for model calibration and validation. The first dataset was obtained from 37 field campaigns by our team during 2004-2018. Three quarters of this dataset (N= 976) were used to calibrate the model, for which the R2 and rRMSE were 0.79 and 61.9%, respectively; the remaining dataset (N= 325) was used to validate the model, and the validation results indicated stable performance by showing comparative errors (R2=0.80, rRMSE = 57.6%). The second and the third datasets were both used to validate model performance with a major focus on testing the temporal transferability of the model. The second dataset (340 samples), collected as part of the Chinese lakes survey conducted by Nanjing Institute of Geography and Limnology from 2007 to 2009, also indicated a good model performance (R2=0.78, rRMSE% = 59.1%); the third dataset (229 samples) was assembled by the first lake surveys conducted in the 1980s, demonstrating a stable performance for lake SDD before 1990s (R2=0.81, rRMSE = 50.6%). Comparison of validation results for these different periods and datasets demonstrated the stable performance of the SDD model. Finally, based on the water clarity estimation model, the algorithms of cloud mask and water index were conducted on the GEE platform to accomplish the water clarity of lakes across China. The water clarity information could assist local, provincial or even national level decision-making on policies/management for protecting or improving inland water quality.
TAO Hui, SONG Kaishan, LIU Ge, WANG Qiang, WEN Zhidan
NDVI is a very important vegetation index for the research of vegetation growth and land cover classification. This dataset provides the monthly normalized differential vegetation index (NDVI) of UAV remote sensing with a spatial resolution of 0.2 m from June to October in 2020. It was measured in the midstream of Heihe River Basin over typical stations. The Pix4D mapper software was used for image mosaic and NDVI calculation.
LIU Shaomin, ZHOU Ji, JIN Zichun, WANG Ziwei
Land Surface temperature is one of the important parameters of surface energy balance. This dataset is the monthly land surface temperature data of typical stations in Heihe River Basin from June to October in 2020; In flight, DJI M600 Pro UAV was equipped with the WIRIS Pro sc thermal imager. taking SD station in the wetland, DM station in the oasis and Hz station in the desert as the center, the land surface temperature was observed, and the surface brightness temperature image was obtained. The flying height of the UAV was about 300m, the pixel of the thermal imager was 336x256, and the spatial resolution of the image was 0.4m. The surface temperature retrieval algorithm is an improved single channel algorithm, which is applied to the surface brightness temperature data obtained by UAV thermal imager, and finally the land surface temperature data with 0.4 m spatial resolution is obtained.
LIU Shaomin, ZHOU Ji, WANG Ziwei
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
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
This dataset is the data of human activities in the key areas of Qilian Mountain in 2018, spatial resolution 2m. This dataset focuses on mine mining, urban expansion, cultivated land development, hydropower construction, and tourism development in the key areas of Qilian Mountain.Through high-resolution remote sensing images, compare the changes before and after the statistics. For the maps of the landforms in the Qilian Mountains, check and verify them one by one; re-interpret the plots that are suspicious of the map; collect the relevant data in the field that cannot be reflected by the images, check and correct the location. At the same time, unified input and editing of map attribute information. Generating a data set of human activities in the key areas of the Qilian Mountains in 2018.
QI Yuan, ZHANG Jinlong, JIA Yongjuan, ZHOU Shengming, WANG Hongwei
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 contains NPP products data produced by the maximum synthesis method of the three source regions of the Yellow River, the Yangtze River and the Lancang River. The data of remote sensing products MOD13Q1, MOD17A2, and MOD17A2H are available on the NASA website (http://modis.gsfc.nasa.gov/). The MOD13Q1 product is a 16-d synthetic product with a resolution of 250 m. The MOD17A2 and MOD17A2H product data are 8-d synthetic products, the resolution of MOD17A2 is 1 000 m, and the resolution of MOD17A2H is 500 m. The final synthetic NPP product of MODIS has a resolution of 1 km. The downloaded MOD13Q1, MOD17A2, and MOD17A2H remote sensing data products are in HDF format. The data have been processed by atmospheric correction, radiation correction, geometric correction, and cloud removal. 1) MRT projection conversion. Convert the format and projection of the downloaded data product, convert the HDF format to TIFF format, convert the projection to the UTM projection, and output NDVI with a resolution of 250 m, EVI with a resolution 250 m, and PSNnet with resolutions of 1 000 m and 500 m. 2) MVC maximum synthesis. Synthesize NDVI, EVI, and PSNnet synchronized with the ground measured data by the maximum value to obtain values corresponding to the measured data. The maximum synthesis method can effectively reduce the effects of clouds, the atmosphere, and solar elevation angles. 3) NPP annual value generated from the NASA-CASA model.
Kamel Didan*, Armando Barreto Munoz, Ramon Solano, Alfredo Huete
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
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
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
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
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 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
This dataset contains three basic remote sensing data of digital topography (DEM), TM remote sensing image and NDVI vegetation index of badan jilin desert. 1. DEM, digital terrain data, from the SRTM1 data set released by NASA in the United States, was cropped in the desert area.The resolution is 30 m.The data is stored in the DEM folder, and the dm.ovr file can be opened by ArcGIS. 2. TM image data.The composite data of Landsat TM/ETM + 543 band released by NASA were cropped in the desert lake group distribution area.The resolution is 30 m.From 1990 to 2010, one scene was selected in summer and one scene in autumn every five years to analyze the long-term changes of the lake.In 2002, there was a scene for each quarter to analyze the changes of the lake during the year.The data is stored in TM folder, TIFF format, can be opened by ArcGIS or ENVI software.The file naming rule is yyyymm.tif, where yyyy refers to the year and mm to the month. For example, 199009 refers to the time corresponding to the impact data of September 1990. 3. NDVI, vegetation index.The modis-ndvi product MOD13Q1, released by NASA, was cropped in desert areas.The NDVI data of every ten days of the growing season (June, July, August and September) from 2000 to 2012 are included. The spatial resolution is 250 m and the temporal resolution is 16 days.Stored in NDVI folder, TIFF format, can be opened by ArcGIS or ENVI software.Mosaic_tmp_yyyyddd.hdfout.250m_16_days_ndvi_roi.tif, Where yyyy represents the year and DDD represents the day of DDD of the year.
JIN Xiaomei, HU Xiaonong
The dataset of ground truth measurement synchronizing with PROBA CHRIS was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on Jun. 22, 2008. Observation items included: (1) Albedo by the shortwave radiometer in Huazhaizi desert No. 2 plot. R =10H (R for FOV radius; H for the probe height). Data were archived in Excel format. (2) BRDF of maize in Yingke oasis maize field by ASD (350-2 500 nm) from Beijing University and the observation platform of BNU make. The maximum height of the platform was 5m above the ground with the azimuth 0~360° and the zenith angle -60°~60°; BRDF in Huazhaizi desert No. 2 plot by ASD from Institute of Remote Sensing Applications (CAS) and the observation platform of its own make, whose maximum height was 2m above the ground with the zenith angle -70°~70°. Raw data were binary files direct from ASD (by ViewSpecPro), and pre-processed data on reflectance were in Excel format. (3) Atmospheric parameters in Huazhaizi desert No. 2 plot 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, GUO Xinping, REN Huazhong, ZOU Jie, LIU Sihan, ZHOU Chunyan, FAN Wenjie, TAO Xin
The dataset of ground truth measurement synchronizing with EO-1 Hyperion was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on May 25, 2008. Observation items included: (1) Atmospheric parameters on the ICBC resort office roof 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. (2) Ground object reflectance spectra f new-born rape and the bare land in Biandukou foci experimental area by ASD FieldSpec (350~2500 nm) from BNU. Raw data were binary files direct from ASD (by ViewSpecPro), and pre-processed data on reflectance were in Excel format. (3) Soil moisture (0-40cm) by the cutting ring and the soil temperature (0-40cm) by the thermocouple in Huazhaizi desert No. 1 plot and the windbreak forest; and soil moisture and the soil temperature (0-100cm) in Yingke oasis maize field. Data were archived in Excel format. (4) LAI. The maximum leaf length and width of each alfalfa and barley were measured. Data were archived in Excel format. (5) Coverage of maize and wheat in Yingke oasis maize field, of vegetation (Reaumuria soongorica) in Huazhaizi desert No. 1 and 2 plots by the self-made coverage instrument and the camera (2.5m-3.5m above the ground). 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 surroundings environment was also recorded. Data included the primarily measured image and final fraction of vegetation coverage.
CHEN Ling, QIAN Yonggang, REN Huazhong, WANG Haoxing, YAN Guangkuo, GE Yingchun, SHU Lele, WANG Jianhua, XU Zhen, GUANG Jie, LI Li, XIN Xiaozhou, ZHANG Yang, ZHOU Chunyan, TAO Xin, YAN Binyan, YAO Yanjuan
The dataset of ground truth measurement synchronizing with MODIS was obtained in the Linze grassland foci experimental area on Jun. 22, 2008. Simultaneous east-west ground measurements on the canopy temperature, the half-height temperature and the land surface radiative temperature were carried out by the hand-held infrared thermometer at intervals of 125m in 8 quadrates (2km×2km), No.1 quadrate (H01-H08) on Jun. 22, No.2 quadrate (H09-H16) on Jun. 23,No.3 quadrate (H17-H24) on Jun. 22, No.4 quadrat (H25-H32) on Jun. 23, No.5 quadrate (H33-H40) on Jun. 22, No.6 quadrate (H41-H48) on Jun. 23, No,7 quadrate (H49-H56) and No.8 quadrate (H57-H64) on Jun. 23. Data were archived in Excel format. See WATER: Dataset of setting of the sampling plots and stripes in the foci experimental area of Linze station for more information.
CHAO Zhenhua, NIAN Yanyun, WANG Xufeng, LIANG Wenguang
The dataset of diurnal FPAR change observations was obtained in the Yingke oasis foci experimental areas. Observation items included: (1) Maize canopy reflectance spectra by ASD and 50% grey board, leaf SPAD by the chlorophyll meter and leaf photosynthesis by LI-6400 in Yingke oasis maize field on Jul. 5, 2008 (fixed point observations from 10:00-20:00 at intervals of one hour, and half an hour from 16:00) Besides, Photo: photosynthetic rate (µmol CO2 m-2 s-1), Cond: stomatal conductance (mol H2O m-2 s-1), Ci: intercellular CO2 viscosity (µmol CO2 mol-1), Trmmol: transpiration rate (mmol H2O m-2 s-1), VpdL: vapor pressure deficiency of leaves (kPa), Tleaf: leaf temperature (°C), ParIn_µm: active radiation of interior photosynthesis (µmol m-2 s-1), and ParOutµm: active radiation of outdoor photosynthesis (µmol m-2 s-1) were all archived. (2) Maize canopy reflectance spectra, leaf photosynthesis and diurnal FPAR change by ASD (Institute of Remote Sensing Applications), 50% grey board (Institute of Remote Sensing Applications), LI-6400 (Institute of Remote Sensing Applications) and SUNSCAN (Beijing academy of Agriculture and Forestry Sciences). Based on calibration lamp data (serial number: 64831), radiance spectrum on Jul. 9 by 1050 spectrometer (Beijing academy of Agriculture and Forestry Sciences) and 50% grey board and 99% white board calibration data, the spectrum data were preprocessed. Calibration was undertaken in accordance with the following precedures: a) The original DN was converted into radiance and further into readable EXCEL format by the spectrometer-matched calibration lamp data and ASD. b) Solar radiance was got by 99% white board radiance. solar radiance=the reference board radiance/the reference board reflectance. c) Spectrum from Agriculture and Forestry Sciences was sampled at an interval of 1.438nm, which was made into data at 1nm intervals by segmentation interpolation. d) Based on b=16.087a (where a is radiance before fitting and b after fitting), radiance data got by 68731 spectrograph were processed. The original maize leaf photosynthesis data (by LI-6400) were introduced into EXCEL format, diurnal changes of each leaf were archived as one single unit according to leaf classification. Maize FPAR (the fraction of photosynthetically active radiation) was got by FPAR= (canopyPAR－surface transmissionPAR－canopy reflection PAR+surface reflectionPAR) /canopy PAR; APAR= FPAR×canopy PAR. The unit for PAR was µmol m-2 s-1. The data included number (the whole leaf), observation time (hh:mm:ss), upper light (µmol m-2 s-1), upper reflectivity (µmol m-2 s-1), lower light (µmol m-2 s-1), lower reflectivity (µmol m-2 s-1) and Spread: variation coefficients of the probe optical intensity.
WANG Dacheng, YANG Guijun, CHENG Zhanhui, Liu Liangyun
The dataset of ground truth measurements synchronizing with the airborne WiDAS mission was obtained in the Linze station foci experimental area 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 CCD, MIR and TIR band data. The simultaneous ground data included: (1) soil moisture (0-5cm) nine times by the cutting ring (50cm^3) along LY06 and LY07 strips, and once by the cutting ring method and once by ML2X Soil Moisture Tachometer in the six points of Wulidun farmland quadrates. The preprocessed soil volumetric moisture data were archived as Excel files. (2) surface radiative temperature measured three times by three handheld infrared thermometer (5# and 6# from Cold and Arid Regions Environmental and Engineering Research Institute, and one from Institute of Geographic Sciences and Natural Resources, which were all calibrated) in LY06 and LY07 strips (98 sample points and repeated three times) and the Wulidun farmland quadrates (various points and repeated three times). Data were archived as Excel files. (3) maize canopy component temperature measured by the 5# handheld infrared thermometer (from Cold and Arid Regions Environmental and Engineering Research Institute) in Wulidun farmland quadrates. Six directions were measured, canopy backlighting and frontlighting, half height backlighting and frontlighting, the light and the shaded bareland, with each direction 20 measurements. (4) spectrum of maize, soil and soil with known moisture measured by ASD Spectroradiometer (350～2 500 nm) from BNU, and the reference board (40% before Jun. 15 and 20% hereafter) in Wulidun farmland quadrates. Raw spectral data were binary files , which were recorded daily in detail, and pre-processed data on reflectance (by ViewSpecPro) were archived as Excel.files (5) mltiangle maize spectrum measured by ASD Spectroradiometer (350～2 500 nm) from BNU, the reference board (40% before Jun. 15 and 20% hereafter), two observation platforms of BNU make and one of Institute of Remote Sensing Applications make in Wulidun farmland. Raw spectral data were archived as binary files, which were recorded daily in detail, and pre-processed data on reflectance and transmittivity were archived as text files (.txt). (6) LAI of maize measured by the fisheye camera (CANON EOS40D with a lens of EF15/28), shooting straight downwards, with exceptions of higher plants, which were shot upwards. Data included original photos (.JPG) and those processed by can_eye5.0 (in excel). (7) LAI of maize measured by LAI2000 in Wulidun farmland quadrates. Data educed from LAI2000 periodically were archived as text files (.txt) and marked with one ID. Raw data (table of word and txt) and processed data (Excel) were included. Besides, observation time, the observation method and the repetition were all archived. See the metadata record “WATER: Dataset of setting of the sampling plots and stripes in the Linze station foci experimental area” for more information of the quadrate locations.
DONG Jian, YU Yingjie, BAI Yanfen, HAO Xiaohua, Qian Jinbo, SHU Lele, WANG Yang, XU Zhen
The dataset of ground truth measurements for snow synchronizing with EO-1 Hyperion and Landsat TM was obtained in the Binggou watershed foci experimental area on Mar. 17, 2008. Observation items included: (1) Snow parameters as snow depth by the ruler, the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, the snow surface temperature and the snow-soil interface temperature by the handheld infrared thermometer simultaneous with the satellite in BG-A, BG-E, BG-F and BG-H. (2) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the Snowfork in BG-A, BG-E and BG-H. Besides, 25-hour fixed-point continuous observation was carried out at the Binggou cold region hydrometerological station. (3) The snow spectrum by ASD (Xinjiang Meteorological Administration) (4) Snow albedo by the total radiometer Two files including raw data and preprocessed data were archived.
BAI Yanfen, BAI Yunjie, GE Chunmei, HAO Xiaohua, LIANG Ji, SHU Lele, WANG Xufeng, XU Zhen, ZHU Shijie, MA Mingguo, CHANG Cun, DOU Yan, MA Zhongguo, JIANG Tenglong, XIAO Pengfeng , LIU Yan, ZHANG Pu
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