Generating surface soil moisture at the 30 m resolution in grape-growing areas based on stacked ensemble learning

Accurate and timely monitoring of drought conditions in grape-producing regions is crucial for achieving healthy growth of grapes. Current soil moisture (SM) products are primarily available at coarse resolutions (e.g. several to tens of kilometres), constraining its applications at fine scales. Here, we trained a weighted stacking ensemble model including three tree-based models (categorical boosting, random forest, and gradient boosting decision tree), using seven forcing parameters related to spectral reflectance (SR), land surface temperature (LST), and evapotranspiration (ET), in conjunction with the digital elevation model (DEM) feature. The weighted stacking ensemble model exhibited an average R2 of 0.86 and an average RMSE of 0.021 m3/m3 in simulating SM in the vegetive stage and the mid-ripening stage of grape. Then we generated high spatiotemporal downscaled SM (HSM) data at a grape growing area at high spatiotemporal resolutions (30 m, 8-day) from 2009 to 2018. Our HSM dataset demonstrated strong spatial, seasonal and interannual dynamics that align with 500 m SM dataset derived from single MODIS data, and the HSM dataset shows more details in SM distribution. Additionally, the SM time series in the HSM is consistently correlated with drought events, offering intricate spatiotemporal information for drought monitoring. The application of downscaled SM results identified a concentration of drought events in the eastern foothills of the Helan Mountains, particularly severe drought conditions were observed in the Hongsipu production area. Drought occurrences in the Hongsipu production area ranged from 90% to 91% during May and June, decreasing to 73% and 41% in July and August, respectively. These findings significantly contribute to enhancing high spatiotemporal SM monitoring capabilities, offering valuable guidance for timely water management in grape-growing regions.