Bridging satellite and UAS scales for surface soil moisture mapping: A two-step random forest downscaling framework.

Quantifying surface soil moisture (SSM) is essential for understanding hydrological cycles, land-atmosphere exchanges, and field-scale water management. However, satellite-based and land surface model products, typically available at kilometer-scale resolution, cannot resolve the sub-field heterogeneity required for small-catchment or precision agriculture applications. This study develops and evaluates a two-step Random Forest (RF) downscaling framework that links a regional 1 km SSM product to a 30 m product and then tests a UAS-supported 16 cm super-resolution mapping experiment. The framework was implemented at the Monteforte Cilento experimental sub-catchment in southern Italy. Model I used 1 km SSM and land surface predictors to generate a five-year 30 m SSM product (SSM30m). Model II used the 30 m product and scale-consistent predictors to test whether Uncrewed Aerial Systems (UAS)-derived 16 cm features, including land surface temperature, diurnal temperature difference, vegetation index, and micro-topographic variables, can further disaggregate the 30 m patterns for a single UAS campaign day. Internal model testing yielded R2 values of 0.805 for Model I and 0.837 for Model II. For independent field evaluation: Dynamics of SSM30m closely tracked cosmic-ray neutron sensor references, whereas the single-day 16 cm resolution surface soil moisture map (SSM16cm) achieved a Pearson correlation coefficient of 0.71 and an unbiased root mean squared error (ubRMSE) of 0.0473 cm3 cm−3 against 20 time-domain reflectometry (TDR) measurements. From Model I to Model II, predictor importance shifted from regional LST at coarser scales to diurnal thermal contrast and micro-topography at sub-meter scale. Furthermore, an analyse of spatial variance revealed strong spatial coherence across all resolutions, from 1 km to 16 cm. This research demonstrates a scalable framework from regional monitoring to “single plant” scale water management, providing a vital tool for sustainable environmental and agricultural decision-making.