This dataset is an 8-year (2011-2018) global spatiotemporally consistent surface soil moisture dataset with a 25km spatial grid resolution and daily temporal step in unit of cm3/cm3. This dataset is developed by applying a linear weight fusion algorithm based on the Triple Collocation Analysis (TCA) to merge the five soil moisture data products, i.e., SMOS, ASCAT, FY3B, CCI and SMAP in two steps. The first step is to fuse the SMOS, ASCAT and FY3B soil moisture products from 2011 to 2018. The second step is to refuse the merged soil moisture product in the first step, CCI and SMAP products from 2015 to 2018, and to obtain the finally merged soil moisture product from 2011 to 2018. In addition, the measured soil moisture data from seven ground observation networks around the world are used to evaluate and analyze the merged soil moisture product. The fused soil moisture product has the global spatial coverage ratio of more than 80%. With rhe minimum RMSE (root mean square error) of 0.036 cm3/cm3.
JIA Li , XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, HU Guangcheng
This data set is a code file set of TCA (triple collision analysis) algorithm, which is used to generate the global daily-scale soil moisture fusion dataset from 2011 to 2018.
XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, JIA Li , HU Guangcheng
Based on long-term series Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products, daily snow cover products without data gaps at 500 m spatial resolution over the Tibetan Plateau from 2002 to 2021 were generated by employing a Hidden Markov Random Field (HMRF) modeling technique. This HMRF framework optimally integrates spectral, spatiotemporal, and environmental information together, which not only fills data gaps caused by frequent clouds, but also improves the accuracy of the original MODIS snow cover products. In particular, this technology incorporates solar radiation as an environmental contextual information to improve the accuracy of snow identification in mountainous areas. Validation with in situ observations and snow cover derived from Landsat-8 OLI images revealed that these new snow cover products achieved an accuracy of 98.31% and 92.44%, respectively. Specifically, the accuracy of the new snow products is higher during the snow transition period and in complex terrains with higher elevations as well as sunny slopes. These gap-free snow cover products effectively improve the spatiotemporal continuity and the low accuracy in complex terrains of the original MODIS snow products, and is thus the basis for the study of climate change and hydrological cycling in the TP.
HUANG Yan , XU Jianghui
Glacier surface albedo is a key parameter in the process of glacier mass and energy balance. The data include annual mean glacier surface albedo and annual minimum glacier surface albedo for each year of the 2000-2020 ablation period (June-August) in the High Mountain Asia. Based on the MODIS 500m resolution daily snow albedo products (including MOD10A1 and MYD10A1), firstly, mean-synthesis was applied to the morning star data MOD10A1 and afternoon star data MYD10A1, followed by interpolation and null-filling using mean-filtering for data within a ±2 day window, and finally based on the minimum and mean methods to obtain the annual mean albedo and annual minimum albedo for glaciers in High Mountain Asia were obtained based on the minimum and mean methods. Compared to the original data, the accuracy and coverage of the data are greatly improved. It can provide ice surface albedo input data for studying the relationship between glacier albedo and matss balance and for glacier models.
XIAO Yao
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
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
Soil moisture (SM) plays a vital role in regulating the water and energy exchange between land surfaces and the atmosphere and is declared an essential climate variable by the Global Climate Observing System (GCOS). Vegetation optical depth (VOD) is a crucial parameter describing vegetation attenuation properties in microwave radiative transfer equation, and it has been proven to be a promising ecological indicator for studying plant hydraulics, carbon stocks, and vegetation phenology. A long-term SM and polarization-, frequency-dependent VODs (C/X/Ku) product was derived from the inter-calibrated AMSR-E/2 multi-frequency brightness temperature, using the multi-channel collaborative algorithm (MCCA). The MCCA comprehensively considers the physical relationship between multiple microwave channels and could simultaneously retrieve frequency- and polarization-dependent VODs and SM. The new MCCA AMSR-E/2 SM dataset was validated over 25 dense soil moisture networks from the International Soil Moisture Network (ISMN) and United States Department of Agriculture (USDA) watersheds. The results showed that MCCA performs best in terms of ubRMSE among the current publicly available SM datasets related to AMSR-E/2. In addition, polarization-, frequency-dependent VODs from MCCA may provide new insights for better understanding the water fluxes in plant physiology.
HU Lu, ZHAO Tianjie, JU Weimin , PENG Zhiqing , YAO Panpan, SHI Jiancheng
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
The Antarctic McMurdo Dry Valleys ice velocity product is based on the Antarctic Ice Sheet Velocity and Mapping Project (AIV) data product, which is post-processed with advanced algorithms and numerical tools. The product is mapped using Sentinel-1/2/Landsat data and provides uniform, high-resolution (60m) ice velocity results for McMurdo Dry Valleys, covering the period from 2015 to 2020.
JIANG Liming JIANG Liming JIANG Liming
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
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
Pine Island Glacier, Swett Glacier, etc. are distributed in the basins of the Antarctic Ice Sheet 21 and 22, which is one of the areas with the most severe melting in the Southwest Antarctica. This dataset first uses Cryosat-2 data (August 2010 to October 2018) to establish a plane equation in each regular grid, taking into account terrain items, seasonal fluctuations, backscattering coefficients, wave front width, lifting rails and other factors, and calculates the elevation change of ice cover surface in the grid through least square regression. In addition, we used ICESat-2 data (October 2018 to December 2020) to calculate the surface elevation change during the two periods by obtaining the elevation difference at the intersection of satellite lifting orbits in each regular grid. The spatial resolution of surface elevation change data in two periods is 5km × 5km, the file format is GeoTIFF, the projection coordinate is polar stereo projection (EPSG 3031), and it is named by the name of the satellite altimetry data used. The data can be opened using ArcMap, QGIS and other software. The results show that the average elevation change rate of the region from 2010 to 2018 is -0.34 ± 0.08m/yr, which belongs to the area with severe melting. The annual average elevation change rate from October 2018 to November 2020 is -0.38 ± 0.06m/yr, which is in an intensified state compared with CryoSat-2 calculation results.
YANG Bojin , HUANG Huabing , LIANG Shuang , LI Xinwu
Global solar radiation at Qomolangma station (The Tibetan Plateau) is measured by radiation sensor (pyranometers CM22, Kipp & Zonen Inc., The Netherlands), and water vapor pressure (hPa) at the ground is measured by HMP45C-GM (Vaisala Inc., Vantaa, Finland). This dataset includes hourly solar radiation and its absorbing and scattering losses caused by the absorbing and scattering atmospheric substances (MJ m-2, 200-3600 nm), and the albedos at the top of the atmosphere and the surface. The above solar radiations are calculated by using an empirical model of global solar radiation (Bai, J.; Zong, X.; Ma, Y.; Wang, B.; Zhao, C.; Yang, Y.; Guang, J.; Cong, Z.; Li, K.; Song, T. 2022. Long-Term Variations in Global Solar Radiation and Its Interaction with Atmospheric Substances at Qomolangma. Int. J. Environ. Res. Public Health, 19, 8906. https://doi.org/10.3390/ijerph19158906). The observed global solar radiation and meteorological variables are available at https://data.tpdc.ac.cn/zh-hans/data/b9ab35b2-81fb-4330-925f-4d9860ac47c3/. The data set can be used to study solar radiation and its attenuation at Qomolangma region.
BAI Jianhui
This data set includes five periods of lake transparency data, including 1995, 2002, 2005, 2010 and 2015. The data sources are: Landsat 5, Landsat 7 and Landsat 8. Method of use: It is convenient to measure the spectral reflectance. On the basis of analyzing the relationship between the spectral reflectance and the transparency measured synchronously, the semi empirical method is used to select the best band combination, establish the transparency algorithm of Qinghai Tibet Plateau lakes, and obtain the water transparency. The verification of measured points shows that the relative error of water transparency estimation is 35%.
SONG Kaishan
This dataset derives from the articles: (1) He, C., Liu, Z., Tian, J., & Ma, Q., (2014). Urban expansion dynamics and natural habitat loss in China: a multiscale landscape perspective. Global change biology, 20(9), 2886-2902.（2）Xu, M., He, C., Liu, Z., Dou, Y. (2016). How Did Urban Land Expand in China between 1992 and 2015? A Multi-Scale Landscape Analysis. PLoS ONE 11 (5): e0154839. To produce this dataset, the nighttime light data, vegetation index data, and land surface temperature data were preprocessed to obtain the multi-source remote sensing data in China from 1992 to 2020, and the economic regionalization, selection of samples, support vector machine classification, and inter-annual correction were used to extract the dynamic information of urban built-up area. According to the accuracy assessment based on Landsat TM/ETM+ data, Kappa coefficient is 0.60, overall accuracy is 92.62% This dataset has been used to assess the impacts of urban expansion on natural habitats and cropland, and can provide data support for understanding China’s urban expansion and its effects.
HE Chunyang, LIU Zhifeng, XU Min , LU Wenlu
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
Snow cover is an important component of the cryosphere and an indispensable variable in the scientific research of global change and Earth system. The distribution range and phenological information of snow cover are important indicators to measure the variation characteristics of snow cover, and also important parameters for snow melting runoff simulation in the hydrological model of cold regions. The High Mountain Asia is the source of many international rivers, and also the hot spot of global climate change research; The ecological and environmental problems caused by the change of ice and snow in the region, such as the reduction of water resources, the increase of extreme weather events, and the frequent occurrence of disasters, have attracted extensive attention from all countries. Therefore, it is very important for climate change research, water resources management, disaster early warning and prevention to accurately obtain long-term snow distribution and snow phenology data in High Mountain Asia . The daily cloudless MODIS normalized snow cover index (NDSI) product (2000-2021500 m) in the High Mountain Asia is based on the MODIS daily snow cover product (including Terra Morning Star data product MOD10A1 and Aqua Afternoon Star data product MYD10A1, C6 versions), and is processed by the same day afternoon star data fusion and cubic spline interpolation cloud removal algorithm; Among them, when there was only Morningstar data product MOD10A1 from 2000 to 2002, the cubic spline interpolation algorithm was directly used for cloud removal. The snow cover phenological data set for hydrological years 2002-2020 is prepared based on cloudless MODIS NDSI products in hydrological years, including three parameters: snow onset date (SOD), snow end date (SED) and snow duration days (SDD). This data set has reliable accuracy.
TANG Zhiguang , DENG Gang
Atmospheric water vapor is an important parameter for studying the water cycle. In the context of global warming, in order to better study the impact of atmospheric water vapor on the water cycle, a global daily scale AMSR-E/AMSR2 all-weather atmospheric precipitable water (TPW) dataset with a spatial resolution of 0.25 ° was constructed. In the data set, the TPW over land is mainly obtained by our newly developed 18.7 and 23.8 GHz brightness temperature data inversion algorithm based on AMSR-E and AMSR2; The ocean sky TPW data integrates AMSR-E/AMSR2 official TPW products. As a post-processing, in order to eliminate the systematic deviation between AMSR-E TPW and AMSR2 TPW, using AIRX2RET TPW as the benchmark, the histogram matching method was used to correct the systematic deviation of AMSR-E and AMSR2 TPW data on a global scale, to ensure the continuity of the data, and finally the global daily scale AMSR-E and AMSR2 TPW all-weather data sets were obtained. Among them, the time range of AMSR-E data is from July 8, 2002 to September 27, 2011, and the time range of AMSR-2 data is from January 1, 2013 to August 31, 2017. Each date contains two files: orbit raising and orbit lowering. The data format is Geotiff. The number of data layers is 2. The first layer is TPW data, with the unit of mm. The second layer is time information, which represents the number of seconds elapsed between the pixel observation time with UTC as the time base and 0:00:00 of the current day. The data set has reliable quality. Through verification and analysis with the global SuomiNET GPS TPW, the root mean square error of the data set is 3.5-5.2mm. As atmospheric precipitable water is an important geophysical parameter affecting surface remote sensing and also has an important impact on the earth's climate change, this data can be used for research on the impact of atmospheric water vapor on the water cycle, the assessment of atmospheric water resources and atmospheric correction in the context of climate warming.
JI Dabin, SHI Jiancheng, HUSI Letu, LI Wei , ZHANG Hongxing , SHANG Huazhe
Crop phenology refers to the date when a crop reaches a critical growth period. The main planting pattern in the North China Plain (NCP) is the rotation system of winter wheat and summer maize. Changes in the key phenological periods of winter wheat and summer maize reflect the response and adaptability of them to climatic conditions and production management measures. And the critical phenology dates are important parameters for evaluating crop growth status and irrigation water consumption in the NCP This study selected the winter wheat-summer maize stable planting area in the NCP. The GIMMS3g NDVI data from 1982 to 2015 was used. Multiple characteristis such as the maximum value, minimum value, slope, and percentage value of the curve were combined to extract phenology of winter wheat and summer maize: SOS (start of the season), PEAK (peak of the season), and EOS (end of the season). The extracted phenology was compared with the phenological records from the agro-meteorological stations. The R² was above 0.9, which was with high accuracy. (Details can be found in the reference) The phenological dataset can be applied to related researches about calculating the productivity of winter wheat and summer maize, evaluating the response of crops to climate change, and estimating irrigation water consumption in this region.
LEI Huimin
The North China Plain is an important food production area in China, with a large area of cropland and a complex planting structure. Accurately identifying the distribution of typical crops in this area and tracking the dynamic changes of planting structure are fundamental for detecting crop growth, evaluating crop irrigation water consumption and optimizing agricultural water resources allocation. This study used Fourier transform to obtatin the amplitudes and phases of the 0-5 harmonics of the MOD13Q1 NDVI data. Based on the field sample points and maximum likelihood supervised classification, the planting area of 6 typical crops (winter wheat-summer maize; winter wheat-rice; other double cropping systems; spring maize; cotton; other single cropping systems) in the North China Plain from 2001 to 2018 was identified. The identification results accuracy were evaluated through confusion matrix, comparison with the winter wheat planting area in the county-level statistical yearbook, and comparison with the proportion of winter wheat extracted by Landsat images, all of which showed good performance and high accuracy. The data can be applied to related research and analysis on crop production, irrigation water consumption estimation, and groundwater protection in the North China Plain.
LEI Huimin
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