Improving Soil Moisture Estimation by Identification of NDVI Thresholds Optimization: An Application to the Chinese Loess Plateau

Accuracy soil moisture estimation at a relevant spatiotemporal scale is scarce but beneficial for understanding ecohydrological processes and improving weather forecasting climate models, particularly in arid semi-arid regions like the Chinese Loess Plateau (CLP). This study proposed Criterion 2, new method to improve relative (RSM) by identification of normalized difference vegetation index (NDVI) thresholds optimization based on our previously iteration procedure 1. Apparent thermal inertia (ATI) temperature dryness (TVDI) were applied subregional RSM retrieval CLP throughout 2017. Three optimal NDVI (NDVI0 was used computing TVDI, both NDVIATI NDVITVDI dividing entire CLP) firstly identified with best validation results (R¯) subregions 8-day periods. Then, we compared selected estimated each criterion. Results show that optimized robust which characterized variability better. The 2 showed increased accuracy (maximum R¯ 0.82 ± 0.007 0.75 0.008 1) coverage (45 38 periods (8-day) maps total area 939.52 × 104 km2 667.44 1, respectively) than Moreover, additional another strategy acquire wider estimation. improved could provide basis drought precision irrigation management.