A Multifactor Eigenvector Spatial Filtering-Based Method for Resolution-Enhanced Snow Water Equivalent Estimation in the Western United States

Accurate snow water equivalent (SWE) products are vital for monitoring hydrological processes and managing resources effectively. However, the coarse spatial resolution (typically at 25 km from passive microwave remote sensing images) of existing SWE cannot meet needs explicit modeling. Linear regression ignores autocorrelation (SA) in variables, measure SA data assimilation algorithm is not explicit. This study develops a Resolution-enhanced Multifactor Eigenvector Spatial Filtering (RM-ESF) method to estimate daily western United States based on 6.25 enhanced-resolution record. The RM-ESF brightness temperature gradience algorithm, incorporating only factors including geolocation, environmental, topographical, features but also eigenvectors generated weights matrix take into account. results indicate that estimation obviously outperforms other given its overall highest correlation coefficient (0.72) lowest RMSE (56.70 mm) MAE (43.88 mm), compared with AMSR2 (0.33, 131.38 mm, 115.45 GlobSnow3 (0.50, 100.03 83.58 NCA-LDAS (0.48, 98.80 81.94 ERA5 (0.65, 67.33 51.82 respectively. model considers effectively estimates km, which provides feasible efficient approach higher precision finer resolution.