Estimation of soil water content based on simulated multi-spectral broadband reflectance and machine learning1

Abstract

The soil water content is a key evaluation factor in the agricultural and ecological fields. The objective of this study was to explore the combination of multi-satellite information to address the issue of low accuracy in soil water content retrieval from single-band, single-source information and to establish a model for the estimation of the soil water content based on a simulated multi-spectral method. An experiment involving the hyperspectral determination of soil samples (Oxisols) with varying values of the water content was conducted. The reflectance of eight multi-spectral satellite sensors was resampled based on the spectral response functions. The vegetation indices (VIs) were established by pairing all available sensor bands. The significant VIs and band reflectance were extracted using the correlation coefficient and out-of-bag (OOB) data importance analysis methods, and then linear models and nonlinear models were established for soil water content estimation. A significant correlation was achieved between the simulated multi-spectral reflectance, VIs, and soil water content. The nonlinear models had a better performance than the linear model. The combined OOB and random forest (OOB-RF) model achieved the highest prediction accuracy, with R2calibration and R2prediction values of 0.852 and 0.834, respectively. Overall, it was verified that the OOB-RF modeling method based on multi-spectral remote sensing was feasible for estimating the soil water content.