Abstract
Soil moisture (SM) is one of the determining variables of natural ecosystems and human managed regions. The downscaling of coarse spatial-resolution SM data is an important way to obtain high-spatial-resolution data and conduct precise agricultural management. However, the spatial heterogeneity of agricultural management makes it challenging to improve the accuracy of SM data downscaling in croplands. Previously, natural index was considered, such as meteorology, crop growth, topography and soil texture variables, but less so have human agricultural management index. We integrated important agricultural management index (crop type and irrigation) into random forest (RF) model for a downscaling study involving Global Land Data Assimilation System (GLDAS) SM products (from 0.25° to 1 km). The integration of crop type and irrigation improved the downscaling accuracy (R-values of 0.724 and 0.749, respectively) and increased the time series consistency (the Euclidean distance (ED) were reduced by 51% and 20%, respectively) for different soil layers (0–10 cm and 10–40 cm). The best correlations were found for winter wheat and summer maize (both had R-values >0.780). The relative importance of the crop type and irrigation in SM downscaling reached 15% ∼ 18%, behind only to the effects of topography (35%) and meteorology (19% ∼ 22%). This study emphasizes the important impact of human activities on soil moisture in croplands.
Published Version
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