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
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
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
The global monthly all-sky land surface temperature (2000-2020) is produced by the method from Chen et al. 2017 JHM.
CHEN Xuelong, BOB Su, MA Yaoming
The gridded desertification risk data of Iranian plateau in 2019 was calculated based on the environmentally sensitive area index (ESAI) methodology. The ESAI approach incorporates soil, vegetation, climate and management quality and is one of the most widely used approaches for monitoring desertification risk. Based on the ESAI framework, fourteen indicators were chosen to consider four quality domains. Each quality index was calculated from several indicator parameters. The value of each parameter was categorized into several classes, the thresholds of which were determined according to previous studies. Then, sensitivity scores between 1 (lowest sensitivity) and 2 (highest sensitivity) were assigned to each class based on the importance of the class’ role in land sensitivity to desertification and the relationships of each class to the onset of the desertification process or irreversible degradation. A more comprehensive description of how the indicators are related to desertification risk and scores is provided in the studies of Kosmas (Kosmas et al., 2013; Kosmas et al., 1999). The main indicator datasets were acquired from the Harmonized World Soil Database of the Food and Agriculture Organization, Climate Change Initiative (CCI) land cover of the European Space Agency and NOAA’s Advanced Very High Resolution Radiometer (AVHRR) data. The raster datasets of all parameters were resampled to 500m and temporally assembled to the yearly values. Despite the difficulty of validating a composite index, two indirect validations of desertification risk were conducted according to the spatial and temporal comparison of ESAI values, including a quantitative analysis of the relationship between the ESAI and land use change between sparse vegetation and grasslands and a quantitative analysis of the relationship between the ESAI and net primary production (NPP). The verification results indicated that the desertification risk data is reliable in Iranian plateau in 2019.
XU Wenqiang
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
Based on Landsat data (kh-9 data in 1976 as auxiliary data), glacial lake data of nearly 40 years (1970s-2018) in the western Nyainqentanglha range were obtained by manual digitization and visual interpretation. The variation characteristics of glacial lake over 0.0036 square kilometers in terms of type, size, elevation and watershed were analyzed in detail. The results show that, between 1976 and 2018, the number of glacial lakes increased by 56% from 192 to 299 and their total area increased by 35% from 6.75 ± 0.13 square kilometers to 9.12 ± 0.13 square kilometers ; the type of glacial lake is changing obviously; the smaller glacial lake is changing faster; the expansion of glacial lake is developing to higher altitude.
LUO Wei, ZHANG Guoqing
This data set is a three-level classification map of Eurasian grassland remote sensing in 2009. The data is in TIF grid format, with a spatial resolution of 1km. The three-level grassland is classified as: temperate meadow grassland, temperate typical grassland, temperate desertification grassland, temperate grassland desertification, and temperate desert. The data is processed according to the ESA global cover 2009 Product global cover map, combined with the historical meteorological data (precipitation, annual accumulated temperature, humidity coefficient, evaporation) and DEM data of ECMWF website. The data can be used to provide the basis for the distribution information and temporal and spatial variation analysis of warm grassland in Eurasia.
TANG Jiakui
Thematic data on desertification in Western Asia, includes two parts: Distribution Map of Sandy Land in Western Asia, Distribution Map of Grassland Degradation in Western Asia. The spatial resolution of the data is 30m. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences, the spatial resolution of data is 30 m. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101. The map of artificial oasis pattern in Amu river basin is based on Landsat TM and ETM image data in 2015. Firstly, with the help of eCognition software, the object-oriented classification is carried out. Secondly, the classification results are checked and corrected manually.
This data set is a spatiotemporal variation map of temperate grassland types in Eurasia - three level classification of Inner Mongolia region of China (2009). The data is in TIF grid format with a spatial resolution of 1km. The data is processed on the basis of the existing grass type map of Inner Mongolia grassland. The grassland type map of Inner Mongolia grassland is based on the field survey data, neimengqi County as the unit, the grassland type classification system, on the basis of prediction, the field sample data, remote sensing image and other information data are superposed, and the local historical grassland survey data and relevant data are referred to, and the field plot is modified. We select 2000-2009 historical meteorological data, further analyze and modify the satellite data, and carry out spatial interpolation calculation. The classification of temperate grassland in Inner Mongolia was obtained. The data can be used to provide the basis for the distribution information and temporal and spatial variation analysis of warm grassland in Eurasia.
TANG Jiakui
Lakes on the Tibetan Plateau (TP) are an indicator and sentinel of climatic changes. We extended lake area changes on the TP from 2010 to 2021, and provided a long and dense lake observations between the 1970s and 2021. We found that the number of lakes, with area larger than 1 k㎡ , has increased to ~1400 in 2021 from ~1000 in the 1970s. The total area of these lakes decreased between the 1970s and ~1995, and then showed a robust increase, with the exception of a slight decrease in 2015. This expansion of the lakes on the highest plateau in the world is a response to a hydrological cycle intensified by recent climate changes.
ZHANG Guoqing
The multi-decadal lake number and area changes in China during 1960s–2020 are derived from historical topographic maps and >42151 Landsat satellite images, including lakes as fine as ≥1 km^2 in size for the past 60 years (1960s, 1970s, 1990, 1995, 2000, 2005, 2010, 2015, 2020). From the 1960s to 2020, the total number of lakes (≥ 1 km ^ 2) in China increased from 2127 to 2621, and the area expanded from 68537 km ^ 2 to 82302 km ^ 2.
ZHANG Guoqing
Ecological carrying capacity refers to the maximum population scale with a certain level of social and economic development that can be sustainably carried by the ecosystem without damaging the production capacity and functional integrity of the ecosystem, per person/square kilometer. Spatial distribution data of ecological carrying capacity were calculated based on NPP data simulated by VPM model and FAO production and trade data of agriculture, forestry and animal husbandry. Based on NPP data and combined with the land use data of cci-ci and biomass ratio parameters of various ecosystems, ANPP data was obtained to serve as ecological supply quantity. Based on agricultural, forestry and animal husbandry production and trade data and combined with population data, per capita ecological consumption standards of countries along the One Belt And One Road line were obtained, and then national scale data space was rasterized. The spatial rasterized ecological bearing data are obtained by dividing the ecological supply data with the per capita ecological consumption standard.
YAN Huiming
This dataset is based on the long sequence (1981-2013)normalized difference vegetation index product(Version 3) of the latest NOAA Global Inventory Monitoring and Modeling System (GIMMS). First, the NDVI data products were re-sampled from the spatial resolution of 1/12 degree to 0.5 degree, then the time series of every year was smoothed by the double-logistic method, and the smoothed curvature was calculated. The maximum curvature of spring was selected as the returning green stage of the vegetation in Spring. This data can be used to analyze the temporal and spatial characteristics of the Holarctic vegetation phenology in Spring.
XU Xiyan
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