SMART-Soil: Satellite-based machine learning approach for reliable tracking of soil moisture across CONUS

Estimation of daily soil moisture (SM) is crucial because it directly affects agricultural productivity, irrigation scheduling, and drought risk assessment. However, most existing satellite-based SM products suffer from two key limitations: (i) strong reliance on radar retrievals, which restricts temporal coverage to the post-2015 period and therefore prevents long-term historical analysis; and (ii) fine-resolution SM maps that are typically obtained via statistical or dynamical downscaling of coarse products, introducing systematic errors and scale-dependent biases. To overcome these limitations, this study proposes a novel framework that estimates daily SM at multiple depths using only land and atmospheric variables from the Moderate Resolution Imaging Spectroradiometer (MODIS). By fully bypassing radar-based inputs and avoiding downscaling of coarse-resolution SM products, our approach natively generates 1-km SM estimates extending back to the early 2000s, enabling consistent, long-term, high-resolution monitoring over the Contiguous United States (CONUS). This study aims to estimate SM in four different depths. The Light Gradient Boosting Machine (LightGBM) optimized with Covariance Matrix Adaptation Evolution Strategy (CMA-ES) was selected to perform the soil moisture estimation on 5, 10, 20 and 50 cm depths. While the increase in depth results in slight accuracy drops, the LightGBM-CMA-ES could perform an accurate estimation. Based on results and considering the surface and deepest soil layer, RMSE ranges from 0.029 to 0.037 m 3 /m 3 while the R 2 spans from 0.86 to 0.79, respectively. Spatial analysis of estimations and their associated errors reveals that despite the models' good estimation accuracy, soils with fine and coarse textures have respectively negative and positive biases. Temporal evaluation showed that the model can perfectly estimate monthly mean SM levels but has some evidence of under-estimation in modelling monthly maxima's specifically in spring months. Output of this estimation framework would be SM maps with MODIS's reference spatial resolution of 1 km, which enables better and more sustainable agricultural productivity, allows better water resources management and facilitates more efficient environmental monitoring and disaster risk management.