The Qinghai Tibet Plateau is a sensitive region of global climate change. Land surface temperature (LST), as the main parameter of land surface energy balance, characterizes the degree of energy and water exchange between land and atmosphere, and is widely used in the research of meteorology, climate, hydrology, ecology and other fields. In order to study the land atmosphere interaction over the Qinghai Tibet Plateau, it is urgent to develop an all-weather land surface temperature data set with long time series and high spatial-temporal resolution. However, due to the frequent cloud coverage in this region, the use of existing satellite thermal infrared remote sensing land surface temperature data sets is greatly limited. Compared with the previous version released in 2019, Western China Daily 1km spatial resolution all-weather land surface temperature data set (2003-2018) V1, this data set (V2) adopts a new preparation method, namely satellite thermal infrared remote sensing reanalysis data integration method based on new land surface temperature time decomposition model. The main input data of the method are Aqua MODIS LST products and GLDAS data, and the auxiliary data include vegetation index and surface albedo provided by satellite remote sensing. This method makes full use of the high frequency and low frequency components of land surface temperature and the spatial correlation of land surface temperature provided by satellite thermal infrared remote sensing and reanalysis data. The evaluation results show that this data set has good image quality and accuracy, which is not only seamless in space, but also highly consistent with the amplitude and spatial distribution of 1 km daily Aqua MODIS LST products widely used in current academic circles. When MODIS LST was used as the reference value, the mean deviation (MBE) of the data set in daytime and nighttime was -0.28 K and -0.29 K respectively, and the standard deviation (STD) of the deviation was 1.25 K and 1.36 K respectively. The test results based on the measured data of six stations in the Qinghai Tibet Plateau and Heihe River Basin show that under clear sky conditions, the data set is highly consistent with the measured LST in daytime / night, and its MBE is -0.42-0.25 K / - 0.35-0.19 K; The root mean square error (RMSE) was 1.03 ~ 2.28 K / 1.05 ~ 2.05 K; Under the condition of non clear sky, the MBE of this data set in daytime / night is -0.55 ~ 1.42 K / - 0.46 ~ 1.27 K; The RMSE was 2.24-3.87 K / 2.03-3.62 K. Compared with the V1 version of the data, the two kinds of all-weather land surface temperature show the characteristics of seamless (i.e. no missing value) in the spatial dimension, and in most areas, the spatial distribution and amplitude of the two kinds of all-weather land surface temperature are highly consistent with MODIS land surface temperature. However, in the region where the brightness temperature of AMSR-E orbital gap is missing, the V1 version of land surface temperature has a significant systematic underestimation. The mass of trims land surface temperature is close to that of V1 version outside AMSR-E orbital gap, while the mass of trims is more reliable inside the orbital gap. Therefore, it is recommended that users use V2 version. The time span of this data set is from 2000 to 2021 and will be updated continuously; The time resolution is twice a day (corresponding to the two transit times of aqua MODIS in the daytime and at night); The spatial resolution is 1 km. In order to facilitate the majority of colleagues to carry out targeted research around the Qinghai Tibet Plateau and its adjacent areas, and reduce the workload of data download and processing, the coverage of this data set is limited to Western China and its surrounding areas (72 ° E-104 ° E，20 ° N-45 ° N）。 Therefore, this dataset is abbreviated as trims lst-tp (thermal and reality integrating modem resolution spatial seamless LST – Tibetan Plateau) for user's convenience.
2022-05-16
Land surface temperature (LST) is one of the important parameters of the interface between the earth's surface and atmosphere. It is not only the direct reflection of the interaction between the surface and the atmosphere, but also has a complex feedback effect on the earth atmosphere process. Therefore, land surface temperature is not only a sensitive indicator of climate change and an important prerequisite for mastering the law of climate change, but also a direct input parameter of many models, which has been widely used in many fields, such as meteorology, climate, environmental ecology, hydrology and so on. With the deepening and refinement of Geosciences and related fields, there is an urgent need for all weather LST based on satellite remote sensing. The generation principle of this dataset is a satellite thermal infrared remote sensing reanalysis data integration method based on a new land surface temperature time decomposition model. The main input data of the method are Aqua MODIS LST products and GLDAS data, and the auxiliary data include vegetation index and surface albedo provided by satellite remote sensing. The method makes full use of the high-frequency and low-frequency components of land surface temperature and the spatial correlation of land surface temperature provided by satellite thermal infrared remote sensing and reanalysis data, and finally reconstructs a high-quality all-weather land surface temperature data set. The evaluation results show that this data set has good image quality and accuracy, which is not only seamless in space, but also highly consistent with the amplitude and spatial distribution of 1 km daily Aqua MODIS LST products widely used in current academic circles. When MODIS LST is used as reference, the mean deviation (MBE) of the data set is 0.08k to 0.16k, and the standard deviation of deviation (STD) is 1.12k to 1.46k. Compared with the daily 1km AATSR LST product released by ESA, the MBE and STD of the product are -0.21k to 0.25k and 1.27k to 1.36k during the day and night. Based on the measured data of 15 stations in Heihe River Basin, Northeast China, North China and South China, the test results show that the MBE is -0.06k to -1.17k, and the RMSE is 1.52k to 3.71k, and there is no significant difference between clear sky and non clear sky. The time resolution of this data set is twice a day, the spatial resolution is 1km, and the time span is from 2000 to 2021; The spatial scope includes the main areas of China's land (including Hong Kong, Macao and Taiwan, excluding the islands in the South China Sea) and the surrounding areas (72 ° E-135 ° E，19 ° N-55 ° N）。 This dataset is abbreviated as trims LST (thermal and reality integrating modem resolution spatial sealing LST) for users to use. It should be noted that the spatial subset of trims LST, trims lst-tp (1 km daily land surface temperature data set in Western China, trims lst-tp; 2000-2021) V2) has also been released in the national Qinghai Tibet Plateau scientific data center to reduce the workload of data download and processing for relevant users.
2022-05-16
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.
2022-04-19
The dataset is the normalized difference water index (NDWI) products from 1970s to 2020 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the NDWI equation which use the difference ratio between the green band and NIR band to enhance the water information, and then to weaken the information of vegetation, soil, buildings and other targets.And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow.NDWI is usually used to extract surface water information effectively, therefore it is widely used in water resoureces, hydrology, forestry and agriculture.
2022-04-19
Based on a large number of measured aboveground biomass data of grassland, the temperate grassland types were divided according to the vegetation type map of China in 1980s Based on the Landsat remote sensing data of engine platform, the random forest model of grassland aboveground biomass and remote sensing data was constructed for different grassland types. On the basis of reliable verification, the annual aboveground biomass of grassland from 1993 to 2019 was estimated, and the annual spatial data set of aboveground biomass of temperate grassland in Northern China from 1993 to 2019 was formed. Aboveground biomass is defined as the total amount of organic matter of vegetation living above the ground in unit area. The original grid value has been multiplied by a factor of 100, unit: 0.01 g / m2 (g / m2). This data set can provide a scientific basis for the dynamic monitoring and evaluation of temperate grassland resources and ecological environment in northern China.
2022-04-18
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.
2022-04-18
Fraction of Absorbed Photosynthetically Active Radiation (FPAR) is a key physiological variable in the study of carbon cycling and is one of the basic variables to describe vegetation ecosystems. The classification results of surface vegetation types in Qinghai-Tibet Plateau region are obtained based on the Landsat reflectance data(30m spatial resolution). According to NDVI of different vegetation types, the remote sensing inversion model is constructed to produce the growing season FPAR products for each vegetation type. This product can be used as one of the parameters to calculate vegetation carbon sequestration and evaluate vegetation ecosystem status.
2022-04-18
The dataset is the Landsat surface reflectance products from 1980s to 2019 over the Tibetan Plateau, it is the key input parameter of many surface geophysical parameters (such as leaf area index, chlorophyll and biomass). The dataset is retrieved based on Landsat level 4 products from China satellite remote sensing ground station, and it is retrived by using the atmospheric correction based on 6S model and BRDF correction model based on C-factor .The RMSE of geometric correction is less than 12m and the RMSD of surface reflectance is less than 5%. And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow.The Landsat surface reflectance play an important role in forest, water resources, climate change.
2022-04-18
Evapotranspiration over the Qinghai Tibet Plateau is calculated by etwatch, a land surface evapotranspiration remote sensing model based on multi-scale and multi-source data. Etwatch adopts the method of combining the residual term method with P-M formula to calculate evapotranspiration. Firstly, according to the characteristics of the data image, the suitable model is selected to retrieve the evapotranspiration on a sunny day; the remote sensing model is often lack of data because the weather conditions can not obtain a clear image. In order to obtain the daily continuous evapotranspiration, the penman Monteith formula is introduced, and the evapotranspiration results on a sunny day are regarded as the "key frame", and the surface impedance information of the key frame is used as the basis to construct the surface impedance Based on the daily meteorological data, the time series data of evapotranspiration are reconstructed. Through the data fusion model, the high spatial and temporal resolution evapotranspiration data set is constructed by combining the low and medium resolution evapotranspiration temporal variation information with the high resolution evapotranspiration spatial difference information, so as to generate the 8 km resolution evapotranspiration of the Qinghai Tibet Plateau Data sets (1990-2015).
2022-04-18
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.
2022-04-18
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