Spatial and Temporal Adaptive Gap-Filling Method Producing Daily Cloud-Free NDSI Time Series

Abstract

The normalized difference snow index (NDSI) is the most popular snow detection index. Due to cloud cover, it is difficult to produce complete and gap-free NDSI datasets. In this study, a spatial and temporal adaptive gap-filling method (STAGFM) is developed, whereby a weighted cloud-free similar pixel function is established for NDSI prediction. Cloud-covered NDSI gaps are filled by combining daily MOD10A1 and MYD10A1, and adjacent temporal composite is applied. STAGFM is implemented with long-time interval data to completely recover NDSI gaps. Moderate Resolution Imaging Spectroradiometer NDSI product data (from November 1, 2017 to March 31, 2018) of Northeast China are chosen as an example, and daily cloud-free NDSI time series over this period are produced. The method effectiveness was validated by cloud assumption and snow depth data, and the results show that STAGFM completely removes clouds and achieves an average correlation coefficient (r), root-mean-square error, and mean absolute error of 0.95, 0.08, and 0.06, respectively.

Keywords

• Algorithms
• clouds
• image restoration
• remote sensing
• snow