Optical Multispectral Indices Versus Microwave-Based Soil Water Index for Surface Soil Moisture Assessment: A Linear Mixed-Effects Analysis of Pixel-Level Heterogeneity over a Tuscan Agricultural Landscape

Soil moisture (SM) assessment underpins applications ranging from climatological studies to land management and emergency response. Microwave techniques already support operational SM products, but at a spatial resolution limited to 1 km. Optical multispectral indices have been proposed as a complementary source of information at finer resolutions (10–100 m), yet vegetation heterogeneity has generally been handled by masking or stratifying the land cover rather than by modeling its effect. Here, we compare three indices (the Normalized Difference Infrared Index, NDII; the Normalized Multi-band Drought Index, NMDI; and the Visible and Shortwave Infrared Drought Index, VSDI) over a heterogeneous agricultural area near Grosseto, Tuscany, using 31 Landsat-8 OLI images acquired over four summers. Evaluated against the Copernicus Soil Water Index (SWI), VSDI performed best both when averaged over the test area and when analyzed at the pixel level (1 km) and was therefore selected for further analysis. A linear mixed-effects model with pixel-dependent intercepts resolves a within-pixel VSDI–SWI repeated measures correlation of 0.714, against 0.411 for the pooled regression. Allowing the slope to vary as well shows that vegetation density governs both the baseline level of VSDI and its sensitivity to soil moisture, with the within-pixel correlation falling from 0.84 on bare soil to 0.30 under a dense canopy.