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Daily 1-km all-weather land surface temperature dataset for Western China (TRIMS LST-TP; 2000-2021) V2

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.


Landsat normalized difference water index (NDWI) products over the Tibetan Plateau (1980s-2019)

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.


A daily, 0.05° Snow depth dataset for Tibetan Plateau (2000-2018)

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.


Land cover data for Southeast Asia (2015)

This data is the land cover data at 30m resolution of Southeast Asia in 2015. The data format of the data is NetCDF, and the variable name is "land cover type". The data was obtained by mosaicing and extracting the From-GLC data. Several land cover types, such as snow and ice that do not exist in Southeast Asia were eliminated.The legend were reintegrated to match the new data. The data provide information of 8 land cover types: cropland, forest, grassland, shrub, wetland, water, city and bare land. The overall accuracy of the data is 71% (Gong et al., 2019). The data can provide the land cover information of Southeast Asia for hydrological models and regional climate models.


Fraction of Absorbed Photosynthetically Active Radiation (FPAR) across Tibetan Plateau from 1987 to 2020

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.


Active landslides by InSAR recognition in Three-River-Parallel territory of Qinghai-Tibet Plateau (2007-2019)

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.


Hyperspectral remote sensing data of typical vegetation along Sichuan Tibet Railway (2019)

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.


Tibetan Plateau surface spectral data set (2019)

The spectral characteristics of different land use types are mainly determined by spectrograph in the surface spectral data set of Qinghai Tibet Plateau. The measured ground features are mainly divided into woodland, (Alpine) shrub, (Alpine) grassland, wetland, cultivated land and bare land. It includes the field observation points in Lhasa, Linzhi, Shigatse, Ali and Naqu. The spectral characteristics of forests were measured based on the different growth stages of vegetation; The spectral characteristics of grassland were measured based on different coverage; The spectral characteristics of cultivated land were measured based on the main crop types, rape flowers and highland barley; The measurements of wetlands were conducted on the rivers, low-lying valleys and lakes; The measurements of bare lands were conducted on the desert, Gobi and roads, which have no vegetation cover. The measurement conducted from July to August in 2019, and the data is daily observation data. The data set can provide a reference for the field verification of remote sensing interpretation.


Impervious surface product of Qinghai-Tibet Plateau with 10m resolution (2018)

Data content: The data set products include impervious surface products with a resolution of 10 meters in the Qinghai-Tibet Plateau, which can be used as a key parameter for related research on the Qinghai-Tibet Plateau ecosystem. Data source and processing method: Product inversion is mainly based on Sentinel series data, considering joint features, combining depth spatial features, long-time NDVI and other exponential features, and topographic features, and using random forest model to achieve impervious surface information extraction. Data quality: The overall accuracy is high. Data application results and prospects: The data set will be continuously updated and can be used to further clarify the impact of human activities on the ecosystem of the Qinghai-Tibet Plateau.


WATER: PROBA CHRIS dataset (2008-2009)

Proba (project for on board autonomy) is the smallest earth observation satellite launched by ESA in 2001. Chris (compact high resolution imaging Spectrometer) is the most important imaging spectrophotometer on the platform of proba. It has five imaging modes. With its excellent spectral spatial resolution and multi angle advantages, it can image land, ocean and inland water respectively for different research purposes. It is the only on-board sensor in the world that can obtain hyperspectral and multi angle data at the same time. It has high spatial resolution, wide spectral range, and can collect rich information in biophysics, biochemistry, etc. At present, there are 23 scenes of proba Chris data in Heihe River Basin. The coverage and acquisition time are as follows: 4 scenes in Arjun dense observation area, 2008-11-18, 2008-12-05, 2009-03-29, 2009-05-22; 1 scene in pingdukou dense observation area, 2009-07-13; 7 scenes in Binggou basin dense observation area, 2008-11-19, 2008-11-26, 2008-12-06, 2009-01-10, 2009-03-04, 2009-03-30, 2009-03-31; dayokou basin dense observation area, 2009-07-13 There are two views in the observation area, 2008-10-23, 2009-06-08; one in Linze area, 2008-06-23; one in Minle area, 2008-10-22; seven in Yingke oasis dense observation area, 2008-04-30, 2008-05-09, 2008-06-04, 2008-07-01, 2008-07-19, 2009-05-31, 2009-08-10. The product level is L1 without geometric correction. Except that there are only four angles for the images of 2009-03-29 and 2009-05-24 in the Arjun encrypted observation area, each image has five different angles. The remote sensing data set of the comprehensive remote sensing joint experiment of Heihe River, proba Chris, was obtained through the "dragon plan" project (Project No.: 5322) (see the data use statement for details).