Remote-Sensing-Based Sampling Design and Prescription Mapping for Soil Acidity

Abstract

Soil acidification is a major problem in the inland Pacific Northwest. A potential solution is the application of lime to neutralize acidity and raise pH. As lime is an expensive input, precision variable rate application is necessary. However, high-resolution mapping of pH and buffer pH for lime prescription requires costly sampling and analysis. To reduce the amount of sampling needed, remote sensing, which correlates with soil pH and buffer pH, can be used to determine optimal sampling locations and allow optimal interpolation. We used soil and crop data from the Washington State University R.J. Cook Agronomy Farm to develop an optimal sampling plan for a farmer’s property, followed that sampling design, and used the measured pH and buffer pH to fit a Bayesian hierarchical model using remote sensing variables specific to that farmer’s land. Following this, a new model was developed for the research farm with similar covariates. Ultimately, on the farmer’s field, we observed a root mean square error (RMSE) of 0.2487 for soil pH at a depth of 0–10 cm and 0.1221 for modified Mehlich buffer at 0–10 cm of depth. For the research farm, where buffer pH was not measured, we saw an RMSE of 0.3272 for soil pH at 0–10 cm of depth and 0.3381 for soil pH at 10–20 cm of depth. The ability make predictions of soil acidity with uncertainty using this technique allows for prescription lime application while optimizing soil sampling and testing. Further, this paper serves as a case study of on-farm research.

Keywords

• soil acidity
• Landsat
• Sentinel 2
• sampling optimization
• Bayesian hierarchical modeling
• on-farm research