Normalized Difference Vegetation Index (NDVI) has been widely used for monitoring vegetation. This dataset employed all available Landsat 5/7/8 data on the Qinghai-Tibetan Plateau (QTP) (> 100,000 scenes), and reconstructed high spatiotemporal NDVI time-series data (30-m and 8-d) during 2000-2020 on the TP (QTP-NDVI30) by using the MODIS-Landsat fusion algorithm (gap filling and Savitzky–Golay filtering；GF-SG). For the details of GF-SG, please refer to Chen et al. (2021). This dataset has been evaluated carefully. The quantitative assessments show that the reconstructed NDVI images have an average MAE value of 0.02, correlation coefficient of 0.96, and SSIM value of 0.94. We compared the reconstructed images in some typical areas with the PlanetScope 3-m images and found that the spatial details were well preserved by QTP-NDVI30. The geographic coordinate system of this dataset is GCS_WGS_84. The spatial range covers the vegetation area of the QTP, which is defined as the areas with average NDVI during July- September larger than 0.15.
CAO Ruyin , XU Zichao , CHEN Yang , SHEN Miaogen , CHEN Jin
The dataset of landuse types in Qilian Mountains National Park in 1985 is a vector dataset based on the remote sensing monitoring dataset of the current landuse situation in China by CAS, which is obtained through cropping and splicing operations. The data production production is vector data generated by manual visual interpretation using Landsat TM/ETM remote sensing images as the main data source. 3 datasets for 2000-2020 are raster datasets with 30m resolution based on GlobeLand30 global 30m ground cover data, obtained through mask extraction and other operations. The land use types of all datasets include 10 primary types of cropland, forest, shrubland, grassland, wetland, water, tundra, impervious surface, bareland, glacier, and permanent snow. The data products can detect most of the land cover changes caused by human activities, which is very important in practical applications. This data can be used to analyze the historical land use types in the Qilian Mountains region and to analyze the changes of land use types in the Qilian Mountains region in combination with the current landuse type data.
NIAN Yanyun
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
The dataset includes three high-resolution DSM data as well as Orthophoto Maps of Kuqionggangri Glacier, which were measured in September 2020, June 2021 and September 2021. The dataset is generated using the image data taken by Dajiang Phantom 4 RTK UAV, and the products are generated through tilt photogrammetry technology. The spatial resolution of the data reaches 0.15 m. This dataset is a supplement to the current low-resolution open-source topographic data, and can reflect the surface morphological changes of Kuoqionggangri Glacier from 2020 to 2021. The dataset helps to accurately study the melting process of Kuoqionggangri Glacier under climate change.
LIU Jintao
This data set is the data set of human activities in key areas of Qilian Mountains in 2021, with a spatial resolution of 2m. This data set focuses on the monitoring of mining, urban expansion, cultivated land development, hydropower construction and tourism development in key areas of Qilian Mountains. Through high-resolution remote sensing images, the changes before and after statistics are compared. The map spots of land type change in Qilian mountain area were investigated and verified block by block; Reinterpret and verify the plots with suspicious mapping; For the land type that cannot be reflected by the image, verify the land type on the spot, collect relevant data, check and correct the position. At the same time, further check the attribute information of the monitoring content of key areas in the Qilian Mountains in 2021, input and edit the map spots and their attributes in a unified way, form a human activity data set in the Qilian Mountains in 2021, realize the current situation and timeliness of ecological governance in the Qilian Mountains, and provide data support for human activity monitoring in key areas in the Qilian Mountains.
QI Yuan , ZHANG Jinlong , ZHOU Shengming , YUAN Jing, WANG Hongwei
The dataset is the soil fertility data of Muli coal mine area on the Qinghai Tibet Plateau from 2000 to 2020. It is issued every five years, including five periods in 2000, 2005, 2010, 2015 and 2020; A total of 15 image data. The dataset covers a rectangular area (98.82 ° E-100.84 ° E, 37.5 ° N-38.25 ° N), defined by four vertexs of the southeast and northwest of Muli coal mine. The dataset is in grid format, with a spatial resolution of 30m, and the dataset format is GeoTIFF. The dataset takes the 30m surface albedo obtained by spatiotemporal fusion of GLDAS-2.1 albedo products and Landsat 5/8 albedo products, and the 30m surface temperature obtained by spatiotemporal fusion of GLDAS-2.1 surface temperature products and Landsat 5/8 surface temperature products as independent variables. Combined with the multiple regression model, the five-year dataset of total nitrogen（Unit: g / kg）, total phosphorus （Unit: g / kg）and total potassium （Unit: g / kg）in Muli coal mine area from 2000 to 2020 is regressed. The multiple regression model adopts the measured data of Huangshui River Basin stations in May 2018. On the premise that the independent variables are the albedo and surface temperature of Landsat 5/8, and the dependent variables are the total phosphorus, total nitrogen and total potassium observed in the field, the multiple regression model is established. These datasets fill the gap of high spatial resolution soil fertility dataset in Muli coal mine, and provide support for the study of temporal and spatial changes of soil fertility in Muli mining area.
CHEN Shaohui
In this study，a vegetation classification system for the vegetation types in the Qinghai-Tibet Plateau was designed. The integrated classification method，taken into account of multi-source vegetation classification / land cover classification products， was used to produce the actual vegetation map. This integrated classification method followed the principle of data consistency，and the resultant vegetation map was superior over other vegetation maps in terms of reflection of current situation， classification system， and classification accuracy. This vegetation map is timely and could better reflect current vegetation distribution than earlier ones. This vegetation map could be conducive to fully extract vegetation information from multi-source data products with high reliability and consistency. Compared with previous data products，the overall accuracy （78.09%，kappa coefficient is 0.75） of this new vegetation map was found to increase by 18.84%-37.17%，especially for grassland and shrub.
ZHANG Hui, ZHAO Cenliang, ZHU Wenquan
Hourly spatially complete land surface temperature (LST) products have a wide range of applications in many fields such as freeze-thaw state monitoring and summer high temperature heat wave monitoring. Although the LST retrieved from thermal infrared (TIR) remote sensing observations has high accuracy, it is spatially incomplete due to the influence of cloud, which heavily limits the application of LST. LST simulated by land surface models (LSM) is with high temporal resolution and spatiotemporal continuity, while the spatial resolution is relatively coarse and the accuracy is poor. Therefore, fusing the remote sensing retrieved LST and the model simulated LST is an effective way to obtain seamless hourly LST. The authors proposed a fusion method to generate 0.02° hourly seamless LST over East Asia and produced the corresponding data set. This dataset is the 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ verification results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively. The time period is 2016-2021 over (0-60°N, 80°E-140°E).
CHENG Jie, DONG Shengyue, SHI Jiancheng
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
This dataset includes the maximum normalized vegetation index (NDVI) data from 1982 to 2015, the maximum enhanced vegetation index (EVI) data from 2000 to 2020, and the land cover change (LUCC) data from 2001 to 2019 in the China-Mongolia-Russia Economic Corridor (CMREC). Among these, NDVI data was extracted from GIMMS satellite data with a resolution of 8 km; EVI and LUCC data were extracted from MODIS satellite data (MOD13A3 and MCD12C1) with a resolution of 1 km and 5 km, respectively. The dataset filters the outliers or missing values in the original data, which is of higher quality than the source data. Meanwhile, we adopted the maximum value composite (MVC) method to process NDVI and EVI data to obtain the annual maximum NDVI and EVI, which can better reflect the vegetation distribution and change in CMREC over the past several decades. The spatio-temporal changes of vegetation and land use extracted from satellite remote sensing data will provide scientifical guidance for the risk control and prevention of the ecological environment change in CMREC.
ZHANG Xueqin
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
This dataset consists of four files including (1) Lake ice thickness of 16 large lakes measured by satellite altimeters for 1992-2019 (Altimetric LIT for 16 large lakes.xlsx); (2) Daily lake ice thickness and lake surface snow depth of 1,313 lakes with an area > 50 km2 in the Northern Hemisphere modeled by a one-dimensional remote sensing lake ice model for 2003-2018 (in NetCDF format); (3) Future lake ice thickness and surface snow depth for 2071-2099 modeled by the lake ice model with a modified ice growth module (table S1.xlsx); (4) A lookup table containing lake IDs, names, locations, and areas. This daily lake ice and snow thickness dataset could provide a benchmark for the estimation of global lake ice and snow mass, thereby improving our understanding of the ecological and economical significance of freshwater ice as well as its response to climate change.
LI Xingdong, LONG Di, HUANG Qi, ZHAO Fanyu
The North China Plain (NCP), with an area of ~140,000 square kilometers, is among the most important agricultural producing bases in China. In addition to canal irrigation with surface water from the Yellow River, the NCP also needs much groundwater for intensive irrigation. Spatiotemporally continuous and daily evapotranspiration (ET) estimates of high spatial resolution could be valuable for improving our understanding of agricultural water consumption across the NCP, and also for improving water use efficiency for better agricultural water resource management practices over similar regions globally. This ET data set at 1 km spatial resolution and daily timescale across the NCP from Jan 2008 to Dec 2019 was generated using two source energy balance model (TSEB) and data fusion. The accuracy is generally comparable and even higher than published results, with our ET data set featuring spatiotemporal continuity and high spatial resolution for a decade. Furthermore, this data set and associated approaches are valuable for performing daily, monthly, seasonal, interannual, and trend analyses of ET in the NCP and similar regions globally.
ZHANG Caijin , LONG Di
Precipitation over the Tibetan Plateau (TP) known as Asia's water tower plays a critical role in regional water and energy cycles, largely affecting water availability for downstream countries. Rain gauges are indispensable in precipitation measurement, but are quite limited in the TP that features complex terrain and the harsh environment. Satellite and reanalysis precipitation products can provide complementary information for ground-based measurements, particularly over large poorly gauged areas. Here we optimally merged gauge, satellite, and reanalysis data by determining weights of various data sources using artificial neural networks (ANNs) and environmental variables including elevation, surface pressure, and wind speed. A Multi-Source Precipitation (MSP) data set was generated at a daily timescale and a spatial resolution of 0.1° across the TP for the 1998‒2017 period. The correlation coefficient (CC) of daily precipitation between the MSP and gauge observations was highest (0.74) and the root mean squared error was the second lowest compared with four other satellite products, indicating the quality of the MSP and the effectiveness of the data merging approach. We further evaluated the hydrological utility of different precipitation products using a distributed hydrological model for the poorly gauged headwaters of the Yangtze and Yellow rivers in the TP. The MSP achieved the best Nash-Sutcliffe efficiency coefficient (over 0.8) and CC (over 0.9) for daily streamflow simulations during 2004‒2014. In addition, the MSP performed best over the ungauged western TP based on multiple collocation evaluation. The merging method could be applicable to other data-scarce regions globally to provide high quality precipitation data for hydrological research. The latitude and longitude of the left bottom corner across the TP, the number of rows and columns, and grid cells information are all included in each ASCII file.
HONG Zhongkun , LONG Di
Aiming at the 179000 km2 area of the pan three rivers parallel flow area of the Qinghai Tibet Plateau, InSAR deformation observation is carried out through three kinds of SAR data: sentinel-1 lifting orbit and palsar-1 lifting orbit. According to the obtained InSAR deformation image, it is comprehensively interpreted in combination with geomorphic and optical image features. A total of 949 active landslides below 4000m above sea level were identified. It should be noted that due to the difference of observation angle, sensitivity and observation phase of different SAR data, there are some differences in the interpretation of the same landslide with different data. The scope and boundary of the landslide need to be corrected with the help of ground and optical images. The concept of landslide InSAR recognition scale is different from the traditional spatial resolution and mainly depends on the deformation intensity. Therefore, some landslides with small scale but prominent deformation characteristics and strong integrity compared with the background can also be interpreted (with SAR intensity map, topographic shadow map and optical remote sensing image as ground object reference). The minimum interpretation area can reach several pixels. For example, a highway slope landslide with only 4 pixels is interpreted with reference to the highway along the Nujiang River.
YAO Xin
Funded by the National Key R&D Program "Observation and Inversion of Key Parameters of Cryosphere and Polar Environmental Changes", "Multi-scale Observation and Data Product Development of Key Cryosphere Parameters", Changes and impacts of glaciers, snow cover and permafrost and how to deal with them (Grant NO.2019QZKK0201), and Pan-tertiary environmental change and the construction of green silk road (Grant NO.XDA20000000), the research group of Zhang Yinsheng, Institute of Qinghai-Tibet Plateau, Chinese Academy of Sciences developed downscaled snow water equivalent products in the Qinghai-Tibet Plateau. The sub-pixel space-time decomposition algorithm was used to downscale the 0.05° daily snow depth data set (2000-2018) over the Qinghai-Tibet Plateau. And the snow depth depletion model was used to supplement the estimation of the snow depth value in the shallow snow area that cannot be detected by passive microwave remote sensing. Finally, based on the snow density grid data, the snow depth data is converted into snow water equivalent data.
YAN Dajiang, ZHANG Yinsheng
