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ARS Home » Southeast Area » Stoneville, Mississippi » Crop Production Systems Research » Research » Publications at this Location » Publication #265651
Title: A comparison of regression and regression-kriging for soil characterization using remote sensing imagery

Author
item GE, YUFENG - Texas A&M University
item THOMASSON, J - Texas A&M University
item Sui, Ruixiu
item WOOTEN, JAMES - Mississippi State University

Submitted to: Frontiers of Earth Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/17/2011
Publication Date: 6/25/2011
Citation: Ge, Y., Thomasson, J.A., Sui, R., Wooten, J. 2011. A comparison of regression and regression-kriging for soil characterization using remote sensing imagery. Frontiers of Earth Science. 5(3):239-244.

Interpretive Summary: Evaluating in-field variability of soil properties is an essential part of many precision agriculture researches including management zone delineation, real-time soil sensor development, and variable-rate applications. In many of these studies, regression has been applied extensively to quantify the relationships among soil properties such as moisture content, textures, pH, organic matter, nitrogen, and micronutrients. One of the potential problems overlooked in using regression is that: regression assumes sample or residue independence, which is violated by spatial correlation existing in soil samples collected from the field. In this study, a regression-kriging method was attempted in relating soil properties to the remote sensing image of a cotton field near Vance, Mississippi. The regression-kriging model was developed and tested by using 273 soil samples collected from the field. The result showed that by properly incorporating the spatial correlation information of regression residuals, the regression-kriging model generally achieved higher prediction accuracy than the stepwise multiple linear regression model.

Technical Abstract: In precision agriculture regression has been used widely to quality the relationship between soil attributes and other environmental variables. However, spatial correlation existing in soil samples usually makes the regression model suboptimal. In this study, a regression-kriging method was attempted in relating soil properties to the remote sensing image of a cotton field near Vance, Mississippi. The regression-kriging model was developed and tested by using 273 soil samples collected from the field. The result showed that by properly incorporating the spatial correlation information of regression residuals, the regression-kriging model generally achieved higher prediction accuracy than the stepwise multiple linear regression model. Most strikingly, a 50% increase in prediction accuracy was shown in Na. Potential usages of regression-kriging in future precision agriculture applications include real-time soil sensor development and digital soil mapping.