A Robust Supervised Method for Estimating Soil Moisture Content From Spectral Reflectance

Due to the complex interaction of light with moist soils, the soil moisture content (SMC) is hard to estimate from the soil spectral reflectance. Spectral variability, caused by variations in viewing and illumination angle and between-sensor variability, further complicates the estimation. In this work, we developed a supervised methodology to accurately estimate SMC from spectral reflectance. The method determines a proxy for the SMC of moist soil, making use of the reflectance spectra of an air-dried and saturated soil sample. The proxy is made invariant to illumination and viewing angle, and sensor type. In the next step, the proxy is normalized with respect to the ground-truth SMC of the saturated soil to make the technique less dependent on the soil type. The normalized proxy can be directly used as an estimate of SMC. Alternatively, the nonlinear relationship between the normalized proxy and the actual SMC can be learned by supervised regression. Experiments are conducted on real moist soil data. In particular, we developed datasets of moist minerals, acquired by two different sensors, an Agrispec spectrometer and an Imec snapscan shortwave infrared (SWIR) hyperspectral camera, under strictly controlled experimental settings. The proposed methodology is also validated on the available real moist soil data from the literature. Compared to state-of-the-art methods, the proposed method accurately estimates the SMC.