VNIR Diffuse Reflectance Spectroscopy for Agricultural Soil Property Determination Based on Regression-Kriging
In this article, the regression-kriging method was used to account for spatial dependence among soil samples and aid in prediction model development.
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: Transactions of the ASABE. 50(3): 1081-1092. @2007
Authors: Y. Ge, J. A. Thomasson, C. L. Morgan, S. W. Searcy
Abstract
Visible and near-infrared diffuse reflectance spectroscopy has been widely applied in precision agriculture to develop soil property prediction models. This method assumes that residuals of prediction are independently and identically distributed. However, this assumption is violated by spatial dependence common in soil samples collected from agricultural fields, and subsequent prediction models are usually sub-optimal. In this article, the regression-kriging method was used to account for spatial dependence among soil samples and aid in prediction model development. A total of 273 soil samples were collected from an agricultural field in Quitman County, Mississippi. Particle size distribution (clay and sand) and chemical analysis (Ca, K, Mg, Na, P, and Zn) were performed in the laboratory. Soil reflectance spectra were measured with a spectroradiometer (250 to 2500 nm). Soil samples were divided into two groups: 245 samples in the calibration set, and 28 samples in the validation set. The calibration set was first used to develop the principal component regression (PCR) models for each soil property. Semivariance analysis of prediction residuals from PCR revealed strong spatial dependence in Na; medium spatial dependence in Ca, Mg, and sand; weak spatial dependence in K and P; and a pure nugget effect in Zn and clay. Fitted theoretical semivariograms were then used to develop the regression-kriging models. Both the PCR and regression-kriging models were tested with the validation set, and their prediction capability was evaluated by R2 and RMSE (root mean squared error). The results showed that the only two soil properties that could be predicted by the PCR models were Mg (R2 = 0.4 and RMSE = 25.4%) and Ca (R2 = 0.33 and RMSE = 16.6%). On the other hand, the regression-kriging models were able to predict most soil properties with reasonably high R2 (reaching 0.65) and low RMSE. Most impressively, substantial increases of R2 and decreases of RMSE were achieved by the regression-kriging models for Na (R2 = 0.65 and RMSE = 29.0%, compared to R2 = 0.10 and RMSE = 44.4% in the PCR model) and sand (R2 = 0.49 and RMSE = 19.8%, compared to R2 = 0.06 and RMSE = 26.0% in the PCR model). It is anticipated that the proposed method could be integrated into GIS packages for various precision agriculture applications, such as digital soil mapping based on remotely sensed hyperspectral images.
VNIR Diffuse Reflectance Spectroscopy for Agricultural Soil Property Determination Based on Regression-Kriging (full article)