The extraction of glacier surface movement is of great significance in the study of glacier dynamics and material balance changes. In view of the shortcomings of the current application of autonomous remote sensing satellite data in glacier movement monitoring in China, the SAR data covering typical glaciers in alpine areas of the Qinghai Tibet Plateau from 2019 to 2020 obtained under the GF-3 satellite FSI mode was used to obtain the glacier surface velocity distribution in the study area with the help of a parallel offset tracking algorithm. With its good spatial resolution, GF-3 image has significant advantages in extracting glacier movement with small scale and slow movement, and can better reflect the details and differences of glacier movement. This study is helpful to analyze the movement law and spatio-temporal evolution characteristics of glaciers in the Qinghai Tibet Plateau under the background of climate change.
YAN Shiyong
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
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
This data is a high-resolution soil freeze/thaw (F/T) dataset in the Qinghai Tibet Engineering Corridor (QTEC) produced by fusing sentinel-1 SAR data, AMSR-2 microwave radiometer data, and MODIS LST products. Based on the newly proposed algorithm, this product provides the detection results of soil F/T state with a spatial resolution of 100 m on a monthly scale. Both meteorological stations and soil temperature stations were used for results evaluation. Based on the ground surface temperature data of four meteorological stations provided by the national meteorological network, the overall accuracy of soil F/T detection products achieved 84.63% and 77.09% for ascending and descending orbits, respectively. Based on the in-situ measured 5 cm soil temperature data near Naqu, the average overall accuracy of ascending and descending orbits are 78.58% and 76.66%. This high spatial resolution F/T product makes up traditional coarse resolution soil F/T products and provides the possibility of high-resolution soil F/T monitoring in the QTEC.
ZHOU Xin , LIU Xiuguo , ZHOU Junxiong , ZHANG Zhengjia , CHEN Qihao , XIE Qinghua
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
Under the funding of the first project (Development of Multi-scale Observation and Data Products of Key Cryosphere Parameters) of the National Key Research and Development Program of China-"The Observation and Inversion of Key Parameters of Cryosphere and Polar Environmental Changes", the research group of Zhang, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, developed the snow depth downscaling product in the Qinghai-Tibet Plateau. The snow depth downscaling data set for the Tibetan Plateau is derived from the fusion of snow cover probability dataset and Long-term snow depth dataset in China. The sub-pixel spatio-temporal downscaling algorithm is developed to downscale the original 0.25° snow depth dataset, and the 0.05° daily snow depth product is obtained. By comparing the accuracy evaluation of the snow depth product before and after downscaling, it is found that the root mean square error of the snow depth downscaling product is 0.61 cm less than the original product. The details of the product information of the Downscaling of Snow Depth Dataset for the Tibetan Plateau (2000-2018) are as follows. The projection is longitude and latitude, the spatial resolution is 0.05° (about 5km), and the time is from September 1, 2000 to September 1, 2018. It is a TIF format file. The naming rule is SD_yyyyddd.tif, where yyyy represents year and DDD represents Julian day (001-365). Snow depth (SD), unit: centimeter (cm). The spatial resolution is 0.05°. The time resolution is day by day.
YAN Dajiang, MA Ning, MA Ning, ZHANG Yinsheng
This dataset contains daily 0.05°×0.05° land surface soil moisture products in Qilian Mountain Area in 2019. The dataset was produced by utilizing the optimized wavelet-coupled-RF downscaling model (RF-OWCM) to downscale the “AMSR-E and AMSR2 TB-based SMAP Time-Expanded Daily 0.25°×0.25° Land Surface Soil Moisture Dataset in Qilian Mountain Area (SMsmapTE, V1)”. The auxiliary datasets participating in the downscaling model include GLASS Albedo/LAI/FVC, Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST – Tibetan Plateau (TRIMS LST-TP) by Ji Zhou and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
The Qinghai-Tibetan Plateau (QTP), the largest high-altitude and low-latitude permafrost zone in the world, has experienced rapid permafrost degradation in recent decades, and one of the most remarkable resulting characteristics is the formation of thermokarst lakes. Such lakes have attracted significant attention because of their ability to regulate carbon cycle, water, and energy fluxes. However, the distribution of thermokarst lakes in this area remains largely unknown, hindering our understanding of the response of permafrost and its carbon feedback to climate change.Based on more than 200 sentinel-2A images and combined with ArcGIS, NDWI and Google Earth Engine platform, this data set extracted the boundary of thermokarst lakes in permafrost regions of the Qinghai-Tibet Plateau through GEE automatic extraction and manual visual interpretation.In 2018, there were 121,758 thermokarst lakes in the permafrost area of the Qinghai-Tibet Plateau, covering an area of 0.0004-0.5km², with a total area of 1,730.34km² respectively.The cataloging data set of Thermokarst Lakes provides basic data for water resources evaluation, permafrost degradation evaluation and thermal karst study on the Qinghai-Tibet Plateau.
CHEN Xu, MU Cuicui, JIA Lin, LI Zhilong, FAN Chengyan, MU Mei, PENG Xiaoqing, WU Xiaodong
This dataset contains daily 0.01°×0.01° land surface soil moisture products in the Qinghai-Tibet Plateau in 2005, 2010, 2015, 2017, and 2018. The dataset was produced by utilizing the multivariate statistical regression model to downscale the “SMAP Time-Expanded 0.25°×0.25° Land Surface Soil Moisture Dataset in the Qinghai-Tibet Plateau (SMsmapTE, V1)”. The auxiliary datasets participating in the multivariate statistical regression include GLASS Albedo/LAI/FVC, 1km all-weather surface temperature data in western China by Ji Zhou, and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
This dataset contains land surface soil moisture products with SMAP time-expanded daily 0.25°×0.25°in Qinghai-Tibet Plateau Area. The dataset was produced based on the Random Forest method by utilizing passive microwave brightness temperature along with some auxiliary datasets. The temporal resolution of the product in 1980,1985,1990,1995 and 2000 is monthly, by using SMMR, SSM/I, and SSMIS brightness temperature from 19 GHz V/H and 37 GHz V channels. The temporal resolution of the product between June 20, 2002 and Dec 30, 2018 is daily, by utilizing AMSR-E and AMSR2 brightness temperature from 6.925 GHz V/H, 10.65 GHz V/H, and 36.5 GHz V channels. The auxiliary datasets participating in the Random Forest training include the IGBP land cover type, GTOPO30 DEM, and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
The long-term evolution of lakes on the Tibetan Plateau (TP) could be observed from Landsat series of satellite data since the 1970s. However, the seasonal cycles of lakes on the TP have received little attention due to high cloud contamination of the commonly-used optical images. In this study, for the first time, the seasonal cycle of lakes on the TP were detected using Sentinel-1 Synthetic Aperture Radar (SAR) data with a high repeat cycle. A total of approximately 6000 Level-1 scenes were obtained that covered all large lakes (> 50 km2) in the study area. The images were extracted from stripmap (SM) and interferometric wide swath (IW) modes that had a pixel spacing of 40 m in the range and azimuth directions. The lake boundaries extracted from Sentinel-1 data using the algorithm developed in this study were in good agreement with in-situ measurements of lake shoreline, lake outlines delineated from the corresponding Landsat images in 2015 and lake levels for Qinghai Lake. Upon analysis, it was found that the seasonal cycles of lakes exhibited drastically different patterns across the TP. For example, large size lakes (> 100 km2) reached their peaks in August−September while lakes with areas of 50−100 km2 reached their peaks in early June−July. The peaks of seasonal cycles for endorheic lakes were more pronounced than those for exorheic lakes with flat peaks, and glacier-fed lakes with additional supplies of water exhibited delayed peaks in their seasonal cycles relative to those of non-glacier-fed lakes. Large-scale atmospheric circulation systems, such as the westerlies, Indian summer monsoon, transition in between, and East Asian summer monsoon, were also found to affect the seasonal cycles of lakes. The results of this study suggest that Sentinel-1 SAR data are a powerful tool that can be used to fill gaps in intra-annual lake observations.
ZHANG Yu, ZHANG Guoqing
This data set uses SMMR (1979-1987), SSM / I (1987-2009) and ssmis (2009-2015) daily brightness temperature data, which is generated by double index (TB V, SG) freeze-thaw discrimination algorithm. The classification results include four types: frozen surface, melted surface, desert and water body. The data covers the source area of three rivers, with a spatial resolution of 25.067525 km. It is stored in geotif format in the form of ease grid projection. Pixel values represent the state of freezing and thawing: 1 for freezing, 2 for thawing, 3 for deserts, 4 for water bodies. Because all TIF files in the dataset describe the scope of Sanjiangyuan National Park, the row and column number information of these files is unchanged, and the excerpt is as follows (where the unit of cellsize is m): ncols 52 nrows 28 cellsize 25067.525 nodata_value 0
This dataset was derived from long-term daily snow depth in China based on the boundary of the three-river-source area. The snow depth ranges from 0 to 100 cm, and the temporal coverage is from January 1 1980 to December 31 2020. The spatial and temporal resolutions are 0.25o and daily, respectively. Snow depth was produced from satellite passive microwave remote sensing data which came from three different sensors that are SMMR, SSM/I and SSMI/S. Considering the systematic bias among these sensors, the inter-sensor calibrations were performed to obtain temporal consistent passive microwave remote sensing data. And the long-term daily snow depth in China were produced from this consistent data based on the spectral gradient method.For header file information, refer to the data set header.txt.
DAI Liyun
River lake ice phenology is sensitive to climate change and is an important indicator of climate change. 308 excel file names correspond to Lake numbers. Each excel file contains six columns, including daily ice coverage information of corresponding lakes from July 2002 to June 2018. The attributes of each column are: date, lake water coverage, lake water ice coverage, cloud coverage, lake water coverage and lake ice coverage after cloud treatment. Generally, the ice cover area ratio of 0.1 and 0.9 is used as the basis to distinguish the lake ice phenology. The excel file contained in the data set can further obtain four lake ice phenological parameters: Fus, fue, bus, bue, and 92 lakes. Two parameters, Fus and bue, can be obtained.
QIU Yubao
There are many lakes in the Qinghai Tibet Plateau. The glacial phenology and duration of lakes in this region are very sensitive to regional and global climate change, so they are used as the key indicators of climate change research, especially the comparative study of the three polar environmental changes of the earth. However, due to its poor natural environment and sparse population, there is a lack of conventional field measurement of lake ice phenology. The lake ice was monitored with a resolution of 500 meters by using the normalized difference snow index (NDSI) data of MODIS. The traditional snow map algorithm is used to detect the lake daily ice amount and coverage under the condition of sunny days, and the lake daily ice amount and coverage under the condition of cloud cover are re determined through a series of steps based on the spatiotemporal continuity of the lake surface conditions. Through time series analysis, 308 lakes larger than 3km2 are identified as effective records of lake ice range and coverage, forming a daily lake ice range and coverage data set, including 216 lakes.
QIU Yubao
1、Based on field eddy correlation (EC) measurement data, using the standard data processing method for EC data, including despiking, coordinate rotation, air density corrections, outlier rejection, and friction velocity threshold (u*) corrections, gap filled, and NEE partition. The dataset collects carbon flux data and microclimate measurement data from 2003 to 2016 in three typical alpine grassland ecosystems on the Qinghai-Tibet Plateau, including Damxung alpine meadow, Haibei alpine meadow ，Naqu alpine meadow，Zoige alpine grassland，Qilian mountion grassland . The time resolution of data is high (30 min), and the interpolation of data is complete throughout the year. This dataset can be applied to carbon flux assessment, comparison and prediction in these alpine meadows, attribution of climate factors affecting carbon flux, validation of model simulation results, etc. 2、Based on the MCDGF43 dataset, we produce the visible and near-infared albedo of Tibetan Plateau, using the standard data processing of hdf to tif , including the moasic, resample and masked by Tibetan Plateau's boundary. The time resolution of dataset is 8 days and the spatial resolution is 500 meters, which span the period of 2003-2016.
ZHANG Yangjian, SU Peixi, YANG Yan
The variation in the duration of snow on the Tibetan Plateau is relatively great, and the high mountainous areas around the plateau are rich in snow and ice resources. Taking full account of the terrain of the Tibetan Plateau and the snow characteristics in the mountains, the data set adopted AVHRR data to gradually realize generating data products for daily, ten-day, and monthly snow cover areas while maintaining the snow classification accuracy. These data included the daily/10-day/monthly snow cover area data for the Tibetan Plateau from 2007 to 2015, the average accuracy of which is 0.92. It can provide reliable data for snow changes during the historical periods of the Tibetan Plateau.
QIU Yubao
Due to the short snow duration and thin snow layer on the Tibetan Plateau, dynamic monitoring data for daily fractional snow cover are urgently needed in order to better understand water cycling and other processes. This data set is based on MODIS Snow Cover Daily L3 Global 500 m Grid data and includes the Normalized Difference Snow Index (NDSI) data product generated from MODIS/Terra data (MOD10A1) and MODIS/Aqua data (MYD10A1). The data are in the .hdf format. The projection method is sinusoidal map projection. Combining the advantages of 90 m SRTM terrain data and fractional snow cover estimation algorithms under multiple cloud coverage types, the fractional snow cover under different cloud coverage conditions can be re-estimated to meet the production requirements of the daily less cloud (< 10%) data products in High Asia. On the basis of this method, the MODIS daily fractional snow cover data set over High Asia (2002-2016) was constructed. By taking the binary snow product under cloudless conditions as a reference, the spatial and temporal comparisons between snow distribution and snow coverage show that the spatio-temporal characteristics of the product and the binary products are highly consistent. Taking the winter of 2013 as an example, when the fractional snow cover is greater than 50%, the correlation can reach 0.8628. This data set provides daily fractional snow cover data for use in studying snow dynamics, the climate and environment, hydrology, energy balance, and disaster assessment in High Asia.
QIU Yubao
Snow duration on the Tibetan Plateau changes relatively quickly, and the mountainous areas around the plateau are characterized by abundant snow and ice resources and active atmospheric convection. Optical remote sensing is often affected by clouds. Snow cover monitoring needs to consider the cloud-removal problem on a daily time scale. Taking full account of the terrain of the Tibetan Plateau and the characteristics of snow on the mountains, this data set adopted a combination of various cloud-removing processes and steps to gradually remove the daily snow cover by maintaining the cloud-classify accuracy of the snow cover. In addition, a step-by-step comprehensive classification algorithm was formed, and the “MODIS daily cloud-free snow cover product over the Tibetan Plateau (2002-2015)” was completed. Two snow seasons from October 1, 2009, to April 30, 2011, were selected as test data for algorithm research and accuracy verification, and the snow depth data provided by 145 ground stations in the study area were used as a ground reference. The results showed that in the plateau region, when the snow depth exceeds 3 cm, the total classification accuracy of the cloud-free snow cover products is 96.6%, and the snow cover classification accuracy is 89.0%. The whole algorithm procedure, based on WGS84 projected MODIS snow products (MOD10A1 and MYD10A1) with medium resolution, results in a small loss of cloud-removal accuracy, which made the data highly reliable.
QIU Yubao
The proportion data set of daily cloudless MODIS snow cover area in babaohe river basin (2008.1.1-2014.6.1) was obtained after cloud removal processing using a cloud removal algorithm based on cubic spline function interpolation on the basis of daily cloudless MODIS snow cover product-mod10a1 (tang zhiguang, 2013). This data set adopts the projection method of UTM (horizontal axis isometric cutting cylinder), with a spatial resolution of 500m, and provides Daily Snow Albedo daily-sad results for the babao river basin.The data set is a daily file from January 1, 2008 to June 1, 2014.Each file is the snow albedo result of the day, with a value of 0-100 (%), is the ENVI standard file, and the naming rule is: mod10a1.ayyyyddd_h25v05_snow_sad_grid_2d_reproj_babaohe_nocloud.img, where YYYY represents the year, DDD stands for Julian day (001-365/366).The file can be opened directly with ENVI or ARCMAP software. The original MODIS snow cover data products processed by declouding are derived from MOD10A1 products processed by the us national snow and ice data center (NSIDC). This data set is in HDF format and USES sinusoidal projection. The attributes of the cloud-free MODIS albedo data set (2008.1.1-2014.1.1) in babaohe river basin are composed of the spatial and temporal resolution, projection information and data format of the dataset.
WANG Jian, PAN Haizhu
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