Daily snow water equivalent product with SMMR, SSM/I and SSMIS from 1980 to 2020 over China

Increased snow cover enhances gross primary productivity in cold and dry regions of the Tibetan Plateau

“…It provides the most reliable existing and long‐term daily SWE data for China and was validated by daily snow depth observations of meteorological stations during the winter of 2011–2019 across the TP, Northeast China, and northern Xinjiang. The SWE product was compared with the meteorological station measurements (155,218 samples) with an unbiased root mean square error of approximately 10 mm, bias values of −1.3 mm, and a correlation coefficient of 0.84 (Jiang et al, 2022).…”

“…Snow cover data SWE and SCE products were used to obtain three snow cover indicators. The daily SWE product V1.2 was retrieved from the brightness temperature data of the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSMI/S) sensors using a mixed pixel method (Jiang et al, 2020) and was acquired from the Science Data Bank with a 0.25 × 0.25 grid covering 1980-2020. It provides the most reliable existing and long-term daily SWE data for China and was validated by daily snow depth observations of meteorological stations during the winter of 2011-2019 across the TP, Northeast China, and northern Xinjiang.…”

Changes in China's Snow Droughts Characteristics From 1993 to 2019

“…Considering that the mixed-pixel problem interferes with the snow depth retrieval of the passive satellite microwave data, sensor cross-correction in brightness temperature was carried out to maintain the consistency between Scanning Multichannel Microwave Radiometer, Special Sensor Microwave/Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSMI/S). Furthermore, observed weather station measurements and snow course data collected from local meteorological stations were used to correct snow depth estimates (Yang et al, 2020), with an overall unbiased root mean square error (RMSE) and bias value of 5.09 and −0.65 cm, respectively (Jiang et al, 2022). Thus, this data set was more reliable than others, such as MERRA-2 and GlobSnow-2.…”

“…Thus, this data set was more reliable than others, such as MERRA-2 and GlobSnow-2. Furthermore, the RMSE of the SWE product in China was approximately 10-15 mm, meeting the requirements for snowpack change research in China (Jiang et al, 2022). Additionally, owing to the seasonal characteristics of snowpacks and the availability of daily SWE data, we only considered the changes in snow from November to April (winter and spring) and from 1992 to 2019.…”

Observations and geophysical value-added datasets for cold high mountain and polar regions

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data stream services is needed to address emergency challenges, Earth science research, and UN SDGs (Guo, 2018).This special issue includes eight papers. Five of these are concerned with open valueadded datasets on snow (Jiang et al, 2022), lake ice (Wang et al, 2021), glaciers (Lhakpa et al, 2022, sea ice , and heat fluxes (Duan et al, 2022), respectively, and two others are about the applications based on the in situ and value-added data from remote sensing (Wu et al, 2021;Chalov et al, 2022). In another study (Liang et al, 2021), Big Earth Data is successfully used to study the melting of Antarctic ice based on cloud computing; the original EW mode dataset was open in this issue for further development.

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