SOIL FERTILITY CHARACTERIZATION IN AGRICULTURAL FIELDS USING HYPERSPECTRAL REMOTE SENSING

Abstract

Airborne hyperspectral images provide high spatial and spectral resolution along with flexible temporal resolution that are ideally suited for precision agricultural applications. In this study, we have explored the potential of aerial visible/infrared (VIR) hyperspectral imagery for characterizing soil fertility factors in midwestern agricultural fields. Two fields (SW and NW) in Illinois and two fields (GV and FO) in Missouri were considered in this study. Field data included hyperspectral VIR images and soil fertility parameters including pH, organic matter (OM), Ca, Mg, P, K, and soil electrical conductivity. The VIR images were geo-registered and calibrated into apparent reflectance values. The FO field had the highest average reflectance, followed by SW, GV, and NW. The Illinois fields (SW and NW) were high in soil minerals, OM, and soil electrical conductivity. The measured soil fertility characteristics were modeled on first derivatives of the reflectance data using partial least square regression (PLSR). The PLSR model on derivative spectra was able to explain 66% of the overall variability in soil fertility variables considered in this study, with a predicted residual sum of square (PRESS) of 0.66. The model explained a higher degree of variability in some of the response variables, such as Ca (82%), Mg (72%), Veris shallow (86%), Veris deep (67%), and OM (66%), compared to factors such as pH (48%) and EM (50%). Analysis of the parameter estimates for each response variable showed that some of the wavebands, such as 625, 652, 658, 661, 754 and 784 nm, explained a high degree of variability in the model, whereas a large number of wavelengths had negligible contribution. In conclusion, this study showed that soil fertility factors important for precision agriculture applications can be successfully modeled on hyperspectral VIR remote sensing data with partial least square regression models.