Snow cover is an important component of cryosphere and an indicator of climate change. As an important feature of snow cover, snow-cover-extent (SCE) is a vital input parameter of coupled climate model and hydrological model, the main index of regional water resources assessment as well as an indispensable parameter in climate, weather, hydrology, environment and other related studies. 

Based on Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data, a research team from the Northwest Institute of Eco-Environment and Resources of the Chinese Academy of Sciences produced a new MODIS SCE product from 2000 to 2020 over China, which is committed to addressing currently known problems of the standard snow products. 

Related results have been published in Hydrology and Earth System Sciences. 

Based on different land cover types, the researchers developed a multi-index snow discrimination algorithm and a "three-step method" vacancy value filling algorithm. 

The product contains two preliminary clear-sky SCE datasets, Terra-MODIS and Aqua-MODIS SCE dataset and a final daily cloud-gap-filled SCE dataset.  

Besides, they used different snow discrimination algorithms for different land cover types, the snow discrimination algorithm based on Normalized Difference Vegetation Index-Normalized Difference Forest Snow Index (NDVI-NDFSI) piecewise function is used for forest area, and the snow discrimination algorithm based on Normalized Difference Snow Index (NDSI) threshold optimization used for non-forest area. 

According to the researchers, the two sets of primary clear sky snow area products can be prepared by using the snow discrimination algorithm, which can effectively improve the problem of missing judgment of snow in forest area and improve the recognition accuracy of snow. 

The product can provide more reliable data support for spatial-temporal distribution of SCE under climate change in China, thus better serving the relevant research fields of climate, hydrology and environment in China. 

Contact: 

HAO Xiaohua 

E-mail: haoxh@lzb.ac.cn