Characterization of Soil Water Content Using Measured Visible and Near Infrared Spectra

Abstract

Soil water content (WC) affects the accuracy of the visible (VIS) and near infrared (NIR) spectroscopic measurement of other soil properties, for example, C, N, and other nutrients. This study was conducted to subtract the WC contribution to VIS‐NIR spectra by classifying soil spectra into different WC groups. This classification might improve the accuracy of prediction of other soil properties with calibration models established separately for each group of WC. A mobile, fiber‐type, VIS‐NIR spectrophotometer (Zeiss Corona 1.7 visnir fiber), with a measurement range of 306.5 to 1710.9 nm was used to measure the light reflectance of two sample sets: one (275 samples) collected from a single field and the other (360 samples) collected from multiple fields in Belgium and northern France. The partial least squares (PLS) regression analysis and factorial discriminant analysis (FDA) were applied to the VIS‐NIR spectra to quantify WC and classify spectra into different WC groups, respectively. Samples were divided into calibration and validation sets with ratios of 10:1 and 3:1 for the PLS and FDA, respectively. The PLS for the single‐field sample set provided better estimation of WC (R2 = 0.98) than for the multiple‐field sample set (R2 = 0.88). For the single‐field sample set, spectra were successfully classified into six WC groups with correct classification (CC) of 94.1 and 95.6% for the calibration and validation datasets, respectively. Due to the large variability in the multiple‐field sample set, soils were successfully classified into three WC groups only. The CC obtained were 88.1 and 79.7% for the calibration and validation sets, respectively. These results suggested that the FDA can be successfully used to classify soil VIS‐NIR spectra into different WC levels, particularly when soil variability is minimal.