Investigating snow cover duration changes based on a cloud-free snow cover product developed using a spatiotemporal cloud removal method for Northeast China

Optical remote sensing satellites have great potential for detecting long-term and large-scale snow cover properties with minimal manual effort. However, cloud contamination makes it challenging to obtain snow cover extent using optical sensors. To address this issue, we develop a series of spatiotemporal cloud-clearing processes to mitigate cloud contamination based on Moderate Resolution Imaging Spectroradiometer (MODIS) snow products. A 21-year daily gap-filled snow cover dataset over Northeast China from the snow years of 2000–2020 is generated and validated against China Meteorological Administration-classified snow depth observations and Landsat images, achieving mean overall accuracies of 92.47% and 90.54%, respectively. The annual snow cover duration (SCD) calculated on the basis of this gap-filled snow cover product revealed an overall decreasing trend, with a rate of decrease of 1.044 days per year. The rate of decrease in the seasonal SCD was most significant in spring, followed by that in winter and autumn. The correlation between changes in SCD and changes in snowfall is stronger than the correlation between SCD changes and air temperature changes, indicating that SCD is more sensitive to changes in snowfall, which could have significant implications for our understanding of climate change and its impact on snow cover.