VNIR Soil Spectroscopy for Field Soil Analysis

Abstract

The advent of affordable, ground-based, global positioning information (GPS)–enabled sensor technologies provides a new method to rapidly acquire georeferenced soil datasets in situ for high-resolution soil attribute mapping. Our research deployed vehicle-mounted electromagnetic sensor survey equipment to map and quantify soil variability (˜50 ha per day) using apparent electrical conductivity as an indirect measure of soil texture and moisture differences. A portable visible–near infrared (VNIR) spectrometer (350–2500 nm) was then used in the field to acquire hyperspectral data from the side of soil cores to a specified depth at optimized sampling locations. The sampling locations were derived by statistical analysis of the electromagnetic survey dataset, to proportionally sample the full range of spatial variability. The VNIR spectra were used to predict soil organic carbon (prediction model using field-moist spectra: R2 = 0.39; RPD = 1.28; and air-dry spectra: R2 = 0.80; RPD = 2.25). These point values were combined with the electromagnetic survey data to produce a soil organic carbon map, using a random forest data mining approach (validation model: R2 = 0.52; RMSE = 3.21 Mg C/ha to 30 cm soil depth; prediction model: R2 = 0.92; RMSE = 1.53 Mg C/ha to 30 cm soil depth). This spatial modeling method, using high-resolution sensor data, enables prediction of soil carbon stocks, and their spatial variability, at a resolution previously impractical using a solely laboratory-based approach.