Using Reflectance Spectroscopy and Artificial Neural Network to Assess Water Infiltration Rate into the Soil Profile

Abstract

We explored the effect of raindrop energy on both water infiltration into soil and the soil's NIR-SWIR spectral reflectance (1200–2400 nm). Seven soils with different physical and morphological properties from Israel and the US were subjected to an artificial rainstorm. The spectral properties of the crust formed on the soil surface were analyzed using an artificial neural network (ANN). Results were compared to a study with the same population in which partial least-squares (PLS) regression was applied. It was concluded that both models (PLS regression and ANN) are generic as they are based on properties that correlate with the physical crust, such as clay content, water content and organic matter. Nonetheless, better results for the connection between infiltration rate and spectral properties were achieved with the non-linear ANN technique in terms of statistical values (RMSE of 17.3% for PLS regression and 10% for ANN). Furthermore, although both models were run at the selected wavelengths and their accuracy was assessed with an independent external group of samples, no pre-processing procedure was applied to the reflectance data when using ANN. As the relationship between infiltration rate and soil reflectance is not linear, ANN methods have the advantage for examining this relationship when many soils are being analyzed.