Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensor

Sensors are needed to document the spatial variability of soil parameters for successful implementation of Site-Specific Management (SSM). This paper reports research conducted to document the ability of a previously developed near infrared (NIR) reflectance sensor to predict soil organic matter and soil moisture contents of surface and subsurface soils. Three soil cores (5.56 cm dia.×1.5 m long) were collected at each of 16 sites across a 144000 km2 area of the US Cornbelt. Cores were subsampled at eight depth increments, and wetted to six soil moisture levels ranging from air-dry to saturated. Spectral reflectance data (1603¯2598 nm) were obtained in the laboratory on undisturbed soil samples. Data were collected on a 6.6 nm spacing with each reflectance value having a 45 nm bandpass. The data were normalized, transformed to optical density [OD, defined as log10 (1/normalized reflectance)], and analyzed using stepwise multiple linear regression. Standard errors of prediction for organic matter and soil moisture were 0.62 and 5.31%, respectively. NIR soil moisture prediction can be more easily commercialized than can soil organic matter prediction, since a reduced number of wavelength bands are required (four versus nine, respectively).