Researchers from the Northwest Institute of Eco-Environment and Resources (NIEER) of the Chinese Academy of Sciences released a long-term Advanced Very High Resolution Radiometer (AVHRR) snow cover extent (SCE) product of cloud-free snow cover area in China from 1981 to 2019. 

Based on AVHRR remote sensing data from 1981 to 2019, the research team developed an integrated inversion algorithm for cloud and snow discrimination, snow recognition and snow recovery under the cloud by using Google Earth Engine (GEE) platform, and released a dataset with a spatial resolution of 5 km and a daily temporal resolution. 

Related results were published in Earth System Science Data. 

Snow cover area is an important factor and indicator for studying hydrology and climate change, which affects surface energy balance, water flux, hydrological process, atmosphere and ocean circulation. 

Therefore, a set of remote sensing products with long time series and high spatial resolution for snow cover area is significant to the study of climate change and its feedback. 

The newly developed algorithm can effectively solve the cloud and snow confusion problem through the cloud and snow discrimination scheme and the improved discriminant threshold. It can also improve the accuracy of cloud and snow discrimination by using multi-level decision tree algorithm and solve the problem of snow recovery under the cloud by using Hidden Markov Model (HMM) to construct a space-time cube. 

The product is verified by the ground measured snow data and higher resolution remote sensing image, which shows that the overall accuracy of the product is 86.1%, higher than that of similar international products (71.8%). 

This dataset will provide support for the study of snow temporal and spatial distribution and its impact on hydrology, climate and ecology under climate change in China.