We propose an algorithm for ice crack identification and detection using u-net network, which can realize the automatic detection of Antarctic ice cracks. Based on the data of sentinel-1 EW from January to February every year, in order to suppress the speckle noise of SAR image, the probabilistic patch based weights (ppb) algorithm is selected for filtering, and then representative samples are selected and input into the u-net network for model training, and the ice cracks are predicted according to the trained model. Taking five typical ice shelves（Amery、Fimbul、Nickerson、Shackleton、Thwaiters) in Antarctica as an example, the average accuracy of classification results can reach 94.5%, of which the local accuracy of fissure area can reach 78.6%, and the recall rate is 89.4%.
2022-08-17
We propose an algorithm for ice fissure identification and detection using u-net network, which can realize the automatic detection of ice fissures of Typical Glaciers in Greenland ice sheet. Based on the data of sentinel-1 IW from July and August every year, in order to suppress the speckle noise of SAR image, the probabilistic patch based weights (ppb) algorithm is selected for filtering, and then the representative samples are selected and input into the u-net network for model training, and the ice cracks are predicted according to the trained model. Taking two typical glaciers in Greenland (Jakobshavn and Kangerdlussuaq) as examples, the average accuracy of classification results can reach 94.5%, of which the local accuracy of fissure area can reach 78.6%, and the recall rate is 89.4%.
2022-08-17
Global solar radiation at Qomolangma station (The Tibetan Plateau) is measured by radiation sensor (pyranometers CM22, Kipp & Zonen Inc., The Netherlands), and water vapor pressure (hPa) at the ground is measured by HMP45C-GM (Vaisala Inc., Vantaa, Finland). This dataset includes hourly solar radiation and its absorbing and scattering losses caused by the absorbing and scattering atmospheric substances (MJ m-2, 200-3600 nm), and the albedos at the top of the atmosphere and the surface. The above solar radiations are calculated by using an empirical model of global solar radiation (Bai, J.; Zong, X.; Ma, Y.; Wang, B.; Zhao, C.; Yang, Y.; Guang, J.; Cong, Z.; Li, K.; Song, T. 2022. Long-Term Variations in Global Solar Radiation and Its Interaction with Atmospheric Substances at Qomolangma. Int. J. Environ. Res. Public Health, 19, 8906. https://doi.org/10.3390/ijerph19158906). The observed global solar radiation and meteorological variables are available at https://data.tpdc.ac.cn/zh-hans/data/b9ab35b2-81fb-4330-925f-4d9860ac47c3/. The data set can be used to study solar radiation and its attenuation at Qomolangma region.
2022-08-14
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
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
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.
2022-04-18
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.
2022-04-18
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.
2022-04-18
On August 19, 2018, DJI UAV was used to aerial photograph the wetland sample in Qumalai County of the Yangtze River Source Park. The overlap degree of adjacent photographs was not less than 70% according to the set flight route. The Orthophoto Image and DSM were generated using the photographs taken. The Orthophoto Image included three bands of red, green and blue, with a ground resolution of 2 cm, an area of 850 m x 1000 m and a resolution of 4.5 cm for DSM.
2021-11-02
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