In situ mesoscale soil moisture content monitoring based on global navigation satellite system interferometric reflectometry and ensemble modeling

The existing point-scale or large-scale soil moisture content (SMC) monitoring techniques cannot entirely satisfy the requirements of in situ SMC monitoring for agricultural purposes. We proposed a framework for in situ mesoscale SMC monitoring based on global navigation satellite system interferometric reflectometry (GNSS-IR) and ensemble modeling technique. The framework consists of a data collecting and preparing system and an SMC retrieval system. The amplitude, phase, and detection depth, which are the GNSS-IR signal-to-noise ratio parameters, were positively and negatively correlated with the volumetric SMC data, respectively. In the SMC retrieval stage, five kinds of models including linear regression, multilinear regression, k-neighbor regressor, support vector regressor, and random forest (RF) was tested as the first-order model. Two kinds of swarm optimization algorithms including sparrow search algorithm (SSA) and particle swarm optimization (PSO) were examined for models’ hyper-parameter optimization. The results show that the RF performed best and had a coefficient of determination (R2) of 0.798, a root mean square error (RMSE) of 0.043 cm3 / cm3, and a mean absolute error (MAE) of 0.034 cm3 / cm3 for the validation set. Both the SSA and PSO are effective for models’ optimization. After input variable selection, the second-order ensemble RF model outperformed the first-order RF model and had an R2 of 0.819, an RMSE of 0.040 cm3 / cm3, and an MAE of 0.031 cm3 / cm3 for the validation set. The proposed framework is potentially valuable for popularization because of its cost-effectiveness and high accuracy.