Postprocessing products for remote sensing of surface velocity of McMurdo Dry Valleys 60m Sentinel-1/2/Landsat Glacier in Antarctica from 2015 to 2020

The Antarctic McMurdo Dry Valleys ice velocity product is based on the Antarctic Ice Sheet Velocity and Mapping Project (AIV) data product, which is post-processed with advanced algorithms and numerical tools. The product is mapped using Sentinel-1/2/Landsat data and provides uniform, high-resolution (60m) ice velocity results for McMurdo Dry Valleys, covering the period from 2015 to 2020.

0 2022-11-17

Crevasse dataset over typical ice shelves in Antarctica(2015、2016、2020)

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%.

0 2022-08-17

Distribution map of the relationship between vegetation and freeze-thaw changes in the Arctic (1982-2015)

As an important part of the global carbon pool, Arctic permafrost is one of the most sensitive regions to global climate change. The rate of warming in the Arctic is twice the global average, causing rapid changes in Arctic permafrost. The NDVI change data set of different types of permafrost regions in the Northern Hemisphere from 1982 to 2015 has a temporal resolution of every five years, covers the entire Arctic Rim countries, and a spatial resolution of 8km. Based on multi-source remote sensing, simulation, statistics and measured data, GIS method and ecological method are used to quantify the regulation and service function of permafrost in the northern hemisphere to the ecosystem, and all the data are subject to quality control.

0 2022-07-15

The glacier inventory of Qilian Mountain Area (v3.0, 2019)

This dataset contains the glacier outlines in Qilian Mountain Area in 2019. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2019 were used as basic data for glacier extraction. Sentinel-2 images, Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2019, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.

0 2022-06-13

The glacier inventory of Qilian Mountain Area(2021)

This dataset contains the glacier outlines in Qilian Mountain Area in 2021. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2021 were used as basic data for glacier extraction. Sentinel-2 images, Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2021, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.

0 2022-06-05

The glacier inventory of Qilian Mountain Area (v2.0, 2020)

This dataset contains the glacier outlines in Qilian Mountain Area in 2020. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2020 were used as basic data for glacier extraction. Sentinel-2 images, Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2020, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.

0 2022-06-05

The glacier inventory of Qilian Mountain Area (v2.0, 2018)

This dataset contains the glacier outlines in Qilian Mountain Area in 2015. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2018 were used as basic data for glacier extraction. Sentinel-2 images, Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2018, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.

0 2022-06-05

A daily, 0.01° Snow water equivalent dataset for Tibetan Plateau (2000-2018)

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.

0 2022-04-18

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.

0 2022-04-18

Antarctic ice sheet surface elevation data (2003-2009)

The Antarctic ice sheet elevation data were generated from radar altimeter data (Envisat RA-2) and lidar data (ICESat/GLAS). To improve the accuracy of the ICESat/GLAS data, five different quality control indicators were used to process the GLAS data, filtering out 8.36% unqualified data. These five quality control indicators were used to eliminate satellite location error, atmospheric forward scattering, saturation and cloud effects. At the same time, dry and wet tropospheric, correction, solid tide and extreme tide corrections were performed on the Envisat RA-2 data. For the two different elevation data, an elevation relative correction method based on the geometric intersection of Envisat RA-2 and GLAS data spot footprints was proposed, which was used to analyze the point pairs of GLAS footprints and Envisat RA-2 data center points, establish the correlation between the height difference of these intersection points (GLAS-RA-2) and the roughness of the terrain relief, and perform the relative correction of the Envisat RA-2 data to the point pairs with stable correlation. By analyzing the altimetry density in different areas of the Antarctic ice sheet, the final DEM resolution was determined to be 1000 meters. Considering the differences between the Prydz Bay and the inland regions of the Antarctic, the Antarctic ice sheet was divided into 16 sections. The best interpolation model and parameters were determined by semivariogram analysis, and the Antarctic ice sheet elevation data with a resolution of 1000 meters were generated by the Kriging interpolation method. The new Antarctic DEM was verified by two kinds of airborne lidar data and GPS data measured by multiple Antarctic expeditions of China. The results showed that the differences between the new DEM and the measured data ranged from 3.21 to 27.84 meters, and the error distribution was closely related to the slope.

0 2021-11-02