The dataset was used to study the MODIS daily gap filling fraction snow products in the AWT region (AWT MODIS FSC) using the Multi Element Spectral Hybrid Analysis algorithm based on automatic endmember extraction (MESMA-AGE) and the Multi Step Spatiotemporal Interpolation algorithm (MSTI). The spatial resolution of AWT MODIS FSC products is 0.005 °, spanning from 2000 to 2022. 2745 scenes from Landsat-8 imagery were used for regional scale accuracy assessment. The accuracy indicators for fractional snow cover, including coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), are 0.80, 0.16, and 0.10, respectively. The binary recognition accuracy indicators, including overall accuracy (OA), producer accuracy (PA), and user accuracy (UA), are 95.17%, 97.34%, and 97.59%, respectively. The snow depth data observed at 175 meteorological stations were used to evaluate the accuracy of point scale, and the following accuracy indicators were obtained: OA was 93.26%, PA was 84.41%, UA was 82.14%, and Cohen kappa (CK) value was 0.79. The snow depth observation of meteorological stations is also used to evaluate the snow fraction under different weather conditions. The OA of MODIS clear sky observation (spatiotemporal reconstruction based on MSTI algorithm) is 95.36% (88.96%), PA is 87.75% (82.26%), UA is 86.86% (78.86%), and CK is 0.84 (0.72). The AWT MODIS FSC product can provide quantitative spatial distribution information of snow cover for mountain hydrological models, surface models, and numerical weather forecasts in the Asian water tower region.
MODIS surface reflectance data: MODIS surface reflectance products of MOD09GA and MYD09GA from 2000 to 2022 series 6 were used.
Landsat-8 data: Using the Google Earth Engine (GEE) cloud platform, a total of 2745 Landsat-8 images with cloud coverage less than 10% and snow coverage greater than 30% from 2013 to 2021 were selected as "ground truth" to verify some of our snow coverage products.
Surface snow depth data: 175 in-situ station data provided by the China Meteorological Administration in the Asian Water Tower area from February 26, 2000 to April 30, 2019 were used.
Auxiliary data: elevation and land cover type of the Asian water tower area. The GEE cloud platform provides MCD12Q1 V6.1 International Geosphere Biosphere Programme (IGBP) classification data (Sulla Menashe et al., 2019). Utilize the GEE cloud platform to obtain digital elevation model (DEM) data for the Space Shuttle Radar Topography Mission (STRM).
According to the accuracy evaluation of MOD10A1, MODSCAG, and MODAG snow products in the Qinghai Tibet Plateau region, MODAG products have the highest accuracy. Therefore, the MODAAGE partial snow inversion algorithm (MESMA-AGE algorithm) was selected to perform partial snow inversion on the Terra and Aqua MODIS surface reflectance version 6 data in the Asian Water Tower region. Secondly, based on the Terra/MODIS fractional snow inversion results, the Aqua/MODIS fractional snow inversion results were used to fill the data gap caused by cloud and missing observations. Thirdly, use the Geospatial Data Abstraction Library (GDAL) to re project and embed the score snow restoration results of 12 MODIS tiles. Fourthly, the MSTI algorithm has been developed to perform spatiotemporal interpolation on pixels with cloud cover or missing data, enabling the generation of daily cloud gap filling score snow products. Finally, accuracy evaluation and algorithm optimization were conducted on the MESMA-AGE algorithm and AWT MODIS FSC product using snow depth data from meteorological stations and Landsat-8 imagery.
According to the accuracy evaluation of MOD10A1, MODSCAG, and MODAG snow products in the Qinghai Tibet Plateau region, MODAG products have the highest accuracy. Therefore, the MODAAGE partial snow inversion algorithm (MESMA-AGE algorithm) was selected to perform partial snow inversion on the Terra and Aqua MODIS surface reflectance version 6 data in the Asian Water Tower region. Secondly, based on the Terra/MODIS fractional snow inversion results, the Aqua/MODIS fractional snow inversion results were used to fill the data gap caused by cloud and missing observations. Thirdly, use the Geospatial Data Abstraction Library (GDAL) to re project and embed the score snow restoration results of 12 MODIS tiles. Fourthly, the MSTI algorithm has been developed to perform spatiotemporal interpolation on pixels with cloud cover or missing data, enabling the generation of daily cloud gap filling score snow products. Finally, accuracy evaluation and algorithm optimization were conducted on the MESMA-AGE algorithm and AWT MODIS FSC product using snow depth data from meteorological stations and Landsat-8 imagery.
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.ZENODO.DB6673.2024
10.5281/zenodo.10005827
