Satellite remote sensing provides an efficient pathway to map inland surface water extent across different spatial and temporal scales. However, how to monitor the surface water distribution and its spatiotemporal variability via combining optical and radar remote sensing datasets still faces substantial challenges. A monthyly surface water data set of China derived from optical and radar remote sensing (2018-2020) is provided. The dataset was generated by Seamless Surface Water Mapping Framework (SSWMF) proposed by Yang et al. (2022). The validity of this dataset was further proved over China with an overall accuracy of 92.39% and Kappa coefficient of 0.83. With seamless surface water monitoring, the changes of surface water area can be potentially used to characterize the drought/flood process and evaluate the natural hazard impact.
YANG Yongmin
The SMC dataset contains land soil moisture data for Chinese land spanning from 2002 to 2018, the unit is m3/m3, in monthly temporal and 0.05° spatial resolution. More specifically, it is produced by from three passive microwave remote sensing products: the Japan Aerospace Exploration Agency (JAXA)’s Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) Level 3 soil moisture data, and SMOS product that was developed by the Institut National de la Recherche Agronomique) (INRA) and Centre d’Etudes Spatiales de la BIOsphère (CESBIO) soil moisture data. To overcome the deficiencies of passive microwave soil moisture products with low resolution, we construct a spatially weighted decomposition (SWD) using TVDI that calculated by Moderate Resolution Imaging Spectroradiometer (MODIS) data, including the land surface temperature (LST) MYD11C3 data and the normalized difference vegetation index (NDVI) MYD13C2 data. Overall, the downscaled soil moisture (SM) products were consistent with the in-situ measurements (R>0.78) and exhibited a low root mean square error (ubRMSE < 0.05 m3/m3), which indicates good accuracy throughout the time series. The dataset can be widely used to significantly improve hydrologic and drought monitoring and can serve as an important input for ecological and other geophysical models.
MAO Kebiao
This data set includes the monthly synthesis of 30m*30m surface LAI products in Qilian mountain area in 2019. 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, ZHONG Bo
This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2019. 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, ZHONG Bo
This data set includes the monthly synthesis of 30m LAI products in Qilian mountain area in 2020. 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, ZHONG Bo
This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2020. 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, ZHONG Bo
The data set consists of four sub tables, which are remote sensing monitoring of Lake area from 2000 to 2019, total lake water storage based on underwater 3D simulation model, Lake area volume equation based on underwater 3D simulation model, and key parameters and results of water storage measurement and Simulation of 24 typical lakes in Qinghai Province. The first sub table is the time series Lake area data from 2000 to 2019 from remote sensing image data monitoring. The third sub table stores the area storage capacity equation of the lake based on the underwater three-dimensional simulation model of the lake. The second sub table is the estimation result by combining the time series Lake area data and the area storage capacity equation, Finally, the key parameters and results of water storage measurement and Simulation of 24 typical lakes in Qinghai Province from 2000 to 2019 are obtained, including simulated water depth, maximum water depth, simulated reference water level and corresponding Lake area of each lake, which are stored in the fourth sub table.
FANG Chun, LU Shanlong, JU Jianting, TANG Hailong
Greenland digital elevation models (DEMs) are indispensable to fieldwork, ice velocity calculations, and mass change estimations. Previous DEMs have provided reasonable estimations for the entire Greenland, but the time span of applied source data may lead to mass change estimation bias. To provide a DEM with a specific time-stamp, we applied approximately 5.8×108 ICESat-2 observations from November 2018 to November 2019 to generate a new DEM, including the ice sheet and glaciers in peripheral Greenland. A spatiotemporal model fit process was performed at 500 m, 1,2, and 5 km grid cells separately, and the final DEM was posted at the modal resolution of 500 m. A total of 98% of the grids were obtained by the model fit, and the remaining DEM gaps were estimated via the ordinary Kriging interpolation method. Compared with IceBridge mission data acquired by the Airborne Topographic Mapper (ATM) Lidar system, the ICESat-2 DEM was estimated to have a maximum median difference of -0.48 m. The performance of the grids obtained by model fit and interpolation was similar, which both agreed well with the IceBridge data. DEM uncertainty rises in regions of low latitude and high slope or roughness. Furthermore, the ICESat-2 DEM showed significant accuracy improvements compared with other altimeter-derived DEMs, and the accuracy was comparable to those derived from stereo-photogrammetry and interferometry. Overall, the ICESat-2 DEM showed excellent accuracy stability under various topographic conditions, which can provide a specific time-stamped DEM with high accuracy that will be useful to study Greenland elevation and mass balance changes.
FAN Yubin, KE Changqing, SHEN Xiaoyi
The SSTG dataset is a global sea surface temperature data during the period of 2002-2019, in Celsius, in monthly temporal and 0.041° spatial resolution. It is produced by combing daily in situ SST data and daily satellite SST retrieval data from two infrared (MODIS and AVHRR) and three passive microwave (AMSR-E, AMSR2, Windsat) radiometers after calibration by using a temperature depth and observation time correction model. The accuracy assessments indicate that the reconstructed dataset exhibits significant improvements and can be used for mesoscale ocean phenomenon analyses.
MAO Kebiao
Hengduanshan multi-scale disaster causing, disaster pregnant and disaster bearing data spatiotemporal unified data set includes a series of geomorphic data derived from elevation data, annual average normalized vegetation index data, annual average temperature and rainfall data, and night light data of viirs. The geomorphological data cover Hengduanshan area, the vegetation and climate data cover the Qinghai Tibet Plateau, and the nighttime lamp index data cover the whole country. Data collection time varies according to different sources, with the earliest in 2000 and the latest in 2018. The data set is mainly prepared for disaster and risk assessment. In this data set, these data are processed by resampling, spatial correction, optical correction, geomorphic factor calculation, spatial statistics and other processes. The accuracy of the data is consistent with the original accuracy of the data source, and has not been processed by fuzzy processing such as downsampling. In the process of processing, the scientific standard process is used to distinguish continuous and discontinuous data, so as to minimize the data loss in the process of processing.
TANG Chenxiao
In this study, an algorithm that combines MODIS Terra and Aqua (500 m) and the Interactive Multisensor Snow and Ice Mapping System (IMS) (4 km) is presented to provide a daily cloud-free snow-cover product (500 m), namely Terra-Aqua-IMS (TAI). The overall accuracy of the new TAI is 92.3% as compared with ground stations in all-sky conditions; this value is significantly higher than the 63.1% of the blended MODIS Terra-Aqua product and the 54.6% and 49% of the original MODIS Terra and Aqua products, respectively. Without the IMS, the daily combination of MODIS Terra-Aqua over the Tibetan Plateau (TP) can only remove limited cloud contamination: 37.3% of the annual mean cloud coverage compared with the 46.6% (MODIS Terra) and 55.1% (MODIS Aqua). The resulting annual mean snow cover over the TP from the daily TAI data is 19.1%, which is similar to the 20.6% obtained from the 8-day MODIS Terra product (MOD10A2) but much larger than the 8.1% from the daily blended MODIS Terra-Aqua product due to the cloud blockage.
ZHANG Guoqing
Data content: soil moisture data of the Aral Sea basin from 2015 to 2018. Data sources and processing methods: from the National Aeronautics and Space Administration of the United States, the daily soil moisture data are added to get the sum of eight days of soil, and then divided by the number of days to get the average value of eight days of rainfall. Data quality: the spatial resolution is 0.25 ° x 0.25 ° and the temporal resolution is 8 days. The value of each pixel is the average value of soil moisture in 8 days. Results and prospects of data application: under the background of climate change, it can be used to analyze the correlation between meteorological elements and vegetation characteristics, and can also be combined with other meteorological data to analyze the regional distribution of a certain vegetation type.
LIU Tie
Data content: evapotranspiration data set of the Aral Sea basin from 2015 to 2018. Data sources and processing methods: Based on IDL platform, SEBS algorithm and MODIS data of NASA were used to calculate the evapotranspiration results of the Aral Sea basin from 2015 to 2018. Data quality: spatial resolution is 1000m × 1000m, temporal resolution is 8 days. Results and prospects of data application: in the context of climate change, it can be used to analyze the correlation between meteorological elements and vegetation characteristics, and can also be combined with other vegetation data and ecological data to analyze land degradation.
LIU Tie
Data content: data set of planting structure in the Aral Sea Basin in 2019. Data sources and processing methods: 2019 is divided into three time periods, and the sentry-2 data with the least cloud cover and the highest quality in each time period is spliced into a complete map to obtain the remote sensing image of sentry-2 in the third phase of the Aral Sea basin. The NDVI values of the three images are calculated, and then combined with the cultivated land data and field sampling data, the random forest algorithm is used to classify them, and finally the planting structure type of each plot is obtained. Data quality: spatial resolution is 10m × 10m, temporal resolution is year, kappa coefficient is 0.8. Data application results: it can be used for crop yield estimation and water resource utilization efficiency calculation.
LIU Tie
Data content: normalized vegetation index data of the Aral Sea basin from 2015 to 2018. Data sources and processing methods: the first band of mod13a2 product was extracted from NASA medium resolution imaging spectrometer as leaf area index data and multiplied by the scale factor of 0.0001. Data quality: the spatial resolution is 1000m × 1000m, the temporal resolution is 8 days, and the value of each pixel is the average value of eight days' normalized vegetation index. Data application results: under the background of climate change, it can be used to analyze the correlation between meteorological elements and vegetation characteristics, and can also be combined with other vegetation data to analyze the regional distribution of a certain vegetation type.
LIU Tie
In 1970, land use was visually interpreted from MSS images, with an overall interpretation accuracy of more than 90%. Land classification was carried out in accordance with the land use classification system of the Chinese Academy of Sciences. For detailed classification rules, please read the data description document. The 2005 and 2015 data sets were collected from the European Space Agency (ESA) Data acquisition of global land cover types includes five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan) and Xinjiang, China. There are 22 land use types in the data set. The IPCC land use classification system is adopted. Please refer to the documentation for specific classification details.
LUO Geping
The data set records the comparison of natural and man-made disaster losses in Qinghai Province from 2011 to 2018. The data is collected from the Department of natural resources of Qinghai Province. The data set contains 12 data tables, which are: comparison of natural and man-made disasters in 2011, natural and man-made disasters in 2012, natural and man-made disasters in 2013, and natural and man-made disasters in 2014 The structure of the data table is the same, including two fields: Field 1: disaster causes Field 2: Proportion It is classified according to human factors and natural factors
Department of Natural Resources of Qinghai Province
The data of greenhouse land is based on Google Earth image interpretation in Lhasa city, 2018, with a spatial resolution of 0.52 meters. Most of the greenhouses in Lhasa are regular rectangles with high reflectivity, which is easy to identify. In the process of interpretation, the open fields with an area of more than 0.10 hectares and roads with a width of more than 7 meters in the greenhouse area of protected agriculture, as well as the greenhouse covered with black textile were removed, while the small empty fields and ridges between the farmland of protected agriculture were not removed. The accuracy of interpretation is 98%. The data well reflects the spatial pattern characteristics of greenhouse land in Lhasa city.
GONG Dianqing
High spatial and temporal resolution remote sensing image plays a very important role in land use change detection, disaster monitoring and bio-geochemical parameter estimation.Currently, Landsat multi-spectral series satellite data (including Landsat TM, ETM+ and OLI multi-spectral bands) is one of the most widely used multi-spectral data.Taking the One Belt And One Road key node area as the research area, and based on the data of Landsat TM/ETM+/OLI series with good quality from 2000 to 2016, python was used to clip the data in the research area with the masks .To solve the partial data missing problem, MODIS imagery on the missing date and Landsat-MODIS data pair of adjacent phases are combined for spatio-temporal fusion to obtain Landsat-like data.Finally, the high spatial and temporal resolution remote sensing images of 34 key node area during 2001 to 2016 lasted for 8 to 16 days was obtained.
YIN Zhixiang, LING Feng
The data set is based on the reflectance of MODIS channels and the observation data of SIF to establish the neural network model, so as to obtain the SIF data with high spatial and temporal resolution, which is often used as a reference for primary productivity. The data is from Zhang et al. (2018), and the specific algorithm is shown in the article. The source data range is global, and the Qinghai Tibet plateau region is selected in this data set. This data integrates the original 4-day time scale data into the monthly data. The processing method is to take the maximum value of the month, so as to achieve the effect of removing noise as much as possible. This data set is often used to evaluate the temporal and spatial patterns of vegetation greenness and primary productivity, which has practical significance and theoretical value.
ZHANG Yao
Contact Support
Northwest Institute of Eco-Environment and Resources, CAS
0931-4967287
poles@itpcas.ac.cn
Links
National Tibetan Plateau Data CenterFollow Us
A Big Earth Data Platform for Three Poles © 2018-2020 No.05000491 | All Rights Reserved
｜ 
No.11010502040845
Tech Support: westdc.cn
