This data set is hyperspectral observation data of typical vegetation along Sichuan Tibet Railway in September 2019, using the airborne spectrometer of Dajiang M600 resonon imaging system. Including the hyperspectral data observed in the grassland area of Lhasa in 2019, with its own latitude and longitude. The hyperspectral survey was mainly sunny. Before flight, whiteboard calibration was carried out; when data were collected, there was a target (that is, the standard reflective cloth suitable for the grass), which was used for spectral calibration; there were ground mark points (that is, letters with foam plates), and the longitude and latitude coordinates of each mark were recorded for geometric precise calibration. The DN value recorded by Hyperspectral camera of UAV can be converted into reflectivity by using Spectron Pro software. Hyperspectral data is used to extract spectral characteristics of different vegetation types, vegetation classification, inversion of vegetation coverage and so on.
ZHOU Guangsheng, JI Yuhe, LV Xiaomin, SONG Xingyang
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 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 Chenyan, MU Mei, PENG Xiaoqing, WU Xiaodong WU Xiaodong
Nighttime light remote sensing has been an increasingly important proxy for human activities including socioeconomics and energy consumption. Defense Meteorological Satellite Program-Operational Linescan System from 1992 to 2013 and Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite since 2012 are the most widely used datasets. Despite urgent needs for long-term products and pilot explorations in synthesizing them, the publicly available long-term products are limited. We propose a Night-Time Light convolutional Long Short-Term Memory (NTLSTM) network, and apply the network to produce annual Prolonged Artificial Nighttime-light DAtaset (PANDA) in China from 1984 to 2020. Model assessments between modelled and original images show that on average the Root Mean Squared-Error (RMSE) reaches 0.73, the coefficient of determination (R2) reaches 0.95, and the linear slope is 0.99 at pixel level, indicating a high confidential level of the data quality of the generated product. In urban areas, the modelled results can well capture temporal trends in newly built-up areas but slightly underestimate the intensity within old urban cores. Socioeconomic indicators (built-up areas, Gross Domestic Product, population) correlates better with the PANDA than with previous products in the literature, indicating its better potential in finding different controls of nighttime-light variances in different phases. Besides, the PANDA delineates different urban expansion types, outperforms other products in representing road networks, and provides potential nighttime-light sceneries in early years. PANDA provides the opportunity to better bridge the cooperation between human activity observations and socioeconomic or environmental fields
ZHANG Lixian, REN Zhehao, CHEN Bin, GONG Peng, FU Haohuan, XU Bing
This daily land surface kernel-driven BRDF model's coeciffients proudct is with a spatl resolution of 0.02 ° x 0.02 ° over the Tibet Plateau in 2016. Multi-sensory data is used to retrieve the the kernel-driven BRDF model and coupled with topographic effects, and prior knowledge is introduced for quality control inversion. The high-precision BRDF of good spatial-temporal continiuty is retrieved by combining MODIS reflectance data (a polar orbiting satellite) and himawari-8 AHI land surface reflectance (a geostationary satellite ). MODIS lans surface reflectance data and AHI TOA reflectance data are downloaded from the official websites. After registration, atmospheric correction and other processing, the daily resolution BRDF is synthesized with a period of 5 days. Compared with similar products, it has more advantages in capturing rapidly changing surface features, and has better temporal and spatial continuity with the shortest composition period. It can effectively support angular effects correction and the BRDF-releated parameters' retrieval.
WEN Jianguang, TANG Yong, YOU Dongqin
This dataset is the daily temopral resolution land surface albedo product over Qilian Mountain Area in 2019, with a spatial resolution of 500m. The BRDF / albedo model coupled with topographic effects is used to retrieve the parameters from MODIS land surface reflectance, where the prior knowledge is introduced for quality control. MODIS surface reflectance data is downloaded from the official website, and the daily resolution BRDF is composited with a period of 5 days, and albedo is estimated. The validation results shows it meets the accuracy requirements of albedo application. Compared with similar products, it has more advantages in capturing rapidly changing surface features, and has better temporal and spatial continuity. It can effectively support the study of radiation balance and environmental change in Qilian mountain area.
WEN Jianguang, TANG Yong, YOU Dongqin
This daily land surface albedo proudct is with a spatl resolution of 0.02 ° x 0.02 ° over the Tibet Plateau in 2016. Multi-sensory data is used to retrieve the Multisensor Combined BRDF Inversion Model developed from a kernel-driven BRDF model and coupled with topographic effects, and prior knowledge is introduced for quality control inversion. The high-precision BRDF / albedo of good spatial-temporal continiuty is retrieved by combining MODIS reflectance data (a polar orbiting satellite) and himiwarri-8 AHI land surface reflectance (a geostationary satellite ). MODIS lans surface reflectance data and AHI TOA reflectance data are downloaded from the official websites. After registration, atmospheric correction and other processing, the daily resolution BRDF is synthesized with a period of 5 days, and then the daily resolution albedo is estimated. The validation results show that it meets the accuracy requirements of albedo application. Compared with similar products, it has more advantages in capturing rapidly changing surface features, and has better temporal and spatial continuity. It can effectively support the study of radiation energy balance and environmental change in the Tibet Plateau.
WEN Jianguang, TANG Yong, YOU Dongqin
This daily land surface albedo proudct is with a spatl resolution of 0.02 ° x 0.02 ° over the Tibet Plateau in 2016. Multi-sensory data is used to retrieve the Multisensor Combined BRDF Inversion Model developed from a kernel-driven BRDF model and coupled with topographic effects, and prior knowledge is introduced for quality control inversion. The high-precision BRDF / albedo of good spatial-temporal continiuty is retrieved by combining MODIS reflectance data (a polar orbiting satellite) and himiwarri-8 AHI land surface reflectance (a geostationary satellite ). MODIS lans surface reflectance data and AHI TOA reflectance data are downloaded from the official websites. After registration, atmospheric correction and other processing, the daily resolution BRDF is synthesized with a period of 5 days, and then the daily resolution albedo is estimated. The validation results show that it meets the accuracy requirements of albedo application. Compared with similar products, it has more advantages in capturing rapidly changing surface features, and has better temporal and spatial continuity. It can effectively support the study of radiation energy balance and environmental change in the Tibet Plateau.
WEN Jianguang, TANG Yong, YOU Dongqin
File name:YYYYMMswalbedo_basins.tif (YYYY: year, MM: month) Monthly albedo in February 1982 Data version number: v1.0 Data reading mode: it can be opened by envi, ArcGIS and other software Projection + proj = longlat + datum = WGS84 + no_ defs Data format: GeoTIFF, 1790 rows x 1120 columns The valid range of albedo: (0,1) Band Description: 1-3 band, black sky albedo of AVHRR red light, AVHRR near infrared band and short wave band; 4-6 band, white sky albedo of AVHRR red light, AVHRR near infrared band and short wave band Fill value: 0
WEN Jianguang, YOU Dongqin, TANG Yong, WU Shanlong, ZHONG Bo
File name:YYYYMMswalbedo_basins.tif (YYYY: year, MM: month) Monthly albedo in February 1982 Data version number: v1.0 Data reading mode: it can be opened by envi, ArcGIS and other software Projection + proj = longlat + datum = WGS84 + no_ defs Data format: GeoTIFF, 1790 rows x 1120 columns The valid range of albedo: (0,1) Band Description: 1-3 band, black sky albedo of AVHRR red light, AVHRR near infrared band and short wave band; 4-6 band, white sky albedo of AVHRR red light, AVHRR near infrared band and short wave band Fill value: 0
WEN Jianguang, YOU Dongqin, TANG Yong, WU Shanlong, ZHONG Bo
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: precipitation data of the Aral Sea basin from 2015 to 2018. Data sources and processing methods: from the new generation of global precipitation measurement (GPM) of NASA, the daily rainfall can be obtained by adding the three-hour rainfall data, and then the eight day rainfall can be obtained. Data quality: the spatial resolution is 0.1 ° x 0.1 ° and the temporal resolution is 8 days. The value of each pixel is the sum of rainfall in 8 days. Data application results: under the background of climate change, it can be used to analyze the correlation between meteorological elements and vegetation characteristics.
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: cultivated land data of Aral Sea basin. Data sources and processing methods: the original satellite images are from Google Earth of the United States. In order to obtain cloud free images with high resolution, Google Earth integrates the data of different years by splicing, so the time span of the downloaded image data is 2016-2019. Using machine recognition method to predict the land boundary, the boundary is transformed into vector data, and then the results are superimposed with Google image, and the error information is manually checked one by one to get the cultivated land data of the Aral Sea basin. The wgs-1984 coordinate system was used for the final results. Data quality: the spatial resolution is 0.45M × 0.45M, and the accuracy is 90.32%. Data application results: in the context of climate change, it can be combined with meteorological elements and vegetation characteristics to analyze land degradation; it can be combined with vegetation characteristics and sampling points to analyze planting structure, and it can also be combined with meteorological data and statistical data to calculate water resource utilization efficiency and food yield.
LIU Tie
Data content: albedo data of the Aral Sea basin from 2015 to 2018. Data source and processing method: the "BRDF" in mcd43a1 product was extracted from NASA medium resolution imaging spectrometer_ Albedo_ Parameters_ nn. Num_ Parameters_ 01"，“BRDF_ Albedo_ Parameters_ nn. Num_ Parameters_ 02 "and" BRDF "_ Albedo_ Parameters_ nn. Num_ Parameters_ According to the MODIS official algorithm, the daytime albedo and night albedo are calculated and multiplied by the scale factor of 0.001. Data quality: the spatial resolution is 500m × 500m, the temporal resolution is 8 days, and the value of each pixel is the average value of surface albedo in 8 days. Description of the boundary of the Aral Sea Basin: the boundary of the Aral Sea basin is from hydrobases version 1 of WWF. For details, please refer to: https://www.hydrosheds.org/page/hydrobasins Results of data application: as an important parameter, surface evapotranspiration can be retrieved.
LIU Tie
Data content: leaf area index data of Aral Sea basin from 2015 to 2018. Data sources and processing methods: the second band of mod15a2 product was extracted from NASA medium resolution imaging spectrometer as leaf area index data, multiplied by the scale factor of 0.1. 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 leaf area index in 8 days. 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
Data content: surface temperature data of the Aral Sea basin from 2015 to 2018. Data sources and processing methods: the first band of mod11a2 product was extracted from the NASA medium resolution imaging spectrometer as the surface temperature data, multiplied by the scale factor of 0.02. 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 land surface temperature in 8 days. 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 meteorological data to analyze the regional distribution of a certain vegetation type.
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
This dataset includes Fraction Vegetation Coverage (FVC) data for five countries in Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan) during 2010, 2015 and 2020. The data is calculated from the MODIS-NDVI data set (product number MOD13A2.006) based on the empirical relationship between FVC in arid areas and NDVI. The product has a time resolution of 1 year and a spatial resolution of 1 km. The algorithm selects the best available pixel value based on low cloud, low detection angle and highest NDVI value from all the observation data of the year, and performs conversion.
XU Xiaofan, TAN Minghong
