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 f or successful implementation of Site-Specific Management (SSM). This paper reports research conducted to document the ability of a previously develope d 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. x 1.5 m long) were collected at each of 16 sites across a 144 000 km(2) area of the US Cornbelt. Cores were subsampled at eight depth inc rements, and wetted to six soil moisture levels ranging from air-dry to sat urated. Spectral reflectance data (1603-2598 nm) were obtained in the labor atory 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 normaliz ed, transformed to optical density [OD, defined as log,, (1/normalized refl ectance)l, 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 commerci alized than can soil organic matter prediction, since a reduced number of w avelength bands are required (four versus nine, respectively). (C) 2001 Els evier Science B.V. All rights reserved.