MODIS surface reflectance reconstruction based on an RTLSR inversion strategy with dynamically adjusted multi-surface parameters - Hu, Junjie; Gao, Bo; Ma, Hao; Gong, Huili; Liu, Yuanyuan; Liu, Jiahao; Feng, Yinchuan; Lin, Heping; Wang, Ziteng (Frontiers in Remote Sensing, 2025)
IntroductionTo address the spatiotemporal discontinuities in Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance time series caused by cloud contamination, snow cover, and sensor limitations, this study proposes an an optimized RTLSR inversion strategy with dynamiclly adjusted of multi-surface parameters.MethodsThe method specifically aims to improve surface reflectance reconstruction accuracy in seasonally snow-covered regions and regions with significant vegetation phenological changes. To enhance the quality control of input data, the conventional NDVI threshold-based snow masking approach was replaced with the more rigorous “Internal Snow Mask” from the MOD09GA product. Additionally, vegetation indices exhibiting higher saturation resistance—namely the Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI)—were adopted in place of NDVI to better characterize surface reflectance variations during significant phenological transitions.ResultsExperiments conducted in East and South Asia show that in seasonally snow-covered regions (e.g., eastern Tibetan Plateau and parts of northern Asia), RMSE reductions of 5.8%–7.1% are achieved in visible bands (Band1, Band3, Band4). Across the entire study area, the average RMSE across all MODIS bands (Band1–7) is reduced by 4.5%, with notable improvements in vegetation-sensitive near-infrared bands: Band2 and Band5 exhibit RMSE decreases of 14.3% and 6.3%, respectively. Compared with the MCD43A1 product, the proposed method demonstrates superior spatiotemporal continuity in mid- to low-latitude monsoon regions during summer and autumn, achieving a 9.77% increase in annual data availability.DiscussionThese results indicate that the improved approach effectively fills gaps in surface reflectance time series in persistently cloudy regions and offers a reliable complementary solution to existing MODIS products.