Feasibility and Validation of the HISTARFM Gap-Filling Algorithm for Sentinel-2 Data

Optical remote sensing is fundamental for land monitoring, climate analysis, and agricultural applications; however, its effectiveness is often compromised by cloud contamination, which disrupts temporal continuity. Although cloud detection and masking techniques have advanced, additional gap-filling methods are needed to ensure consistent records of surface reflectance. This letter presents an enhanced version of the highly scalable temporal adaptive reflectance fusion model (HISTARFM) adapted for Sentinel-2 imagery. The method generates gap-filled reflectance at 10-m spatial resolution with a five-day temporal frequency, implemented on Google Earth Engine for large-scale processing. Validation across representative sites in North America, Europe, and East Asia demonstrates strong accuracy, with an average relative RMSE below 15%. The proposed approach offers a reliable framework for generating continuous, high-resolution optical datasets, thereby supporting diverse environmental, agricultural, and climate-related studies.