Abstract
Soil moisture is a key factor affecting crop growth, and accurate monitoring soil moisture is of great significance for agriculture. GNSS-IR is a low-cost remote sensing technology, using the interference of GNSS direct and reflected signals to obtain environmental parameters, which can realize non-contact, large-scale, real-time and continuous soil moisture monitoring. In this paper, a random forest algorithm is proposed to conduct soil moisture inversion using SNR frequency, amplitude, phase observables of GPS L1, L2 respectively, and the processing flow and soil moisture inversion model are presented. Taking the inversion results of PRN 4 as an example, the \(R^{2}\) of L1 single frequency parameter inversion result is improved by 0.56% and 4.25% compared with the inversion results of amplitude and phase, RMSE decreases by 6.49% and 29.65% respectively. The \(R^{2} \) of L2 single frequency parameter inversion result is improved by 5.76% and 6.21% compared with the inversion results of amplitude and band single parameter, and the RMSE is reduced by 29.55% and 37.10% respectively. The results show that the random forest algorithm used in frequency inversion is more effective than the amplitude and phase.
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