Abstract
The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN.). The results of the RF model show an RMSE of 0.05 m3 m−3 and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m3 m−3 after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally.
Highlights
Soil moisture (SM) is an essential climate variable that plays a fundamental role in the water and heat exchanges between the land and atmosphere [1,2,3]
The results consist of training and testing of the Random Forest (RF) model, the feature importance, partial dependence plot, and the evaluation of the robustness of the model with statistical metrics and the time-series comparison
In situ surface soil moisture (SSM) from 2206 stations was selected in this study, with their data extent are longer than one year
Summary
Soil moisture (SM) is an essential climate variable that plays a fundamental role in the water and heat exchanges between the land and atmosphere [1,2,3]. Soil moisture controls the allocating of the precipitation into runoff and infiltration and feedback to the atmosphere [4] via its role in partitioning the incoming radiation into latent, sensible, and ground heat fluxes. The initial SSM will significantly impact the climatic mean and predicted extremes [5]. Predicting and analyzing the surface soil moisture (SSM) at a global scale will contribute to understanding the hydrological cycle, land surface processes, and land-atmosphere interactions. There are three main methods for obtaining SSM: in situ measurements, remote sensing (RS)-based retrievals, and land surface model (LSM) simulations, each of which has its advantages and limitations.
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