Abstract
Soil moisture (SM) plays an important role for understanding Earth’s land and near-surface atmosphere interactions. Existing studies rarely considered using multi-source data and their sensitiveness to SM retrieval with few in-situ measurements. To solve this issue, we designed a SM retrieval method (Multi-MDA-RF) using random forest (RF) based on 29 features derived from passive microwave remote sensing data, optical remote sensing data, land surface models (LSMs), and other auxiliary data. To evaluate the importance of different features to SM retrieval, we first compared 10 filter or embedded type feature selection methods with sequential forward selection (SFS). Then, RF was employed to establish a nonlinear relationship between the in-situ SM measurements from sparse network stations and the optimal feature subset. The experiments were conducted in the continental U.S. (CONUS) using in-situ measurements during August 2015, with only 5225 training samples covering the selected feature subset. The experimental results show that mean decrease accuracy (MDA) is better than other feature selection methods, and Multi-MDA-RF outperforms the back-propagation neural network (BPNN) and generalized regression neural network (GRNN), with the R and unbiased root-mean-square error (ubRMSE) values being 0.93 and 0.032 cm3/cm3, respectively. In comparison with other SM products, Multi-MDA-RF is more accurate and can well capture the SM spatial dynamics.
Highlights
Soil moisture (SM) is usually defined as a volume of water stored within the unsaturated zone [1,2], and surface (0–5 cm) SM is an important variable associated with global terrestrial water, energy, and carbon cycles [3,4]
Our product is more consistent with the in-situ measurements, which means it outperforms the other compared products, including official satellites and land surface models (LSMs)
The results show that the performance of MultiMDA-random forest (RF) is better than the other models, which means that our model is more adaptable at different networks
Summary
Soil moisture (SM) is usually defined as a volume of water stored within the unsaturated zone [1,2], and surface (0–5 cm) SM is an important variable associated with global terrestrial water, energy, and carbon cycles [3,4]. It is necessary to obtain accurate and timely SM data. Traditional in-situ SM acquiring methods can provide accurate data each day, they only obtain scattered and limited point data [5]. The in-situ measurements cannot well describe the spatial variability at large scale, especially when the measurements are sparse. Satellite microwave remote sensing is a more advocated method with the advantages of large-scale observation and high temporal resolution, which was proven to be a more effective way to estimate SM. Optical remote sensing data, and land surface models (LSMs) all provide products to estimate SM values
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