Sample Size Optimization for Digital Soil Mapping: An Empirical Example

Daniel D. Saurette, Richard J. Heck, Adam W. Gillespie, Aaron A. Berg and Asim Biswas ()
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Daniel D. Saurette: School of Environmental Sciences, University of Guelph, 50 Stone Rd East, Guelph, ON N1G 2W1, Canada
Richard J. Heck: School of Environmental Sciences, University of Guelph, 50 Stone Rd East, Guelph, ON N1G 2W1, Canada
Adam W. Gillespie: School of Environmental Sciences, University of Guelph, 50 Stone Rd East, Guelph, ON N1G 2W1, Canada
Aaron A. Berg: Department of Geography, Environment & Geomatics, University of Guelph, 50 Stone Rd East, Guelph, ON N1G 2W1, Canada
Asim Biswas: School of Environmental Sciences, University of Guelph, 50 Stone Rd East, Guelph, ON N1G 2W1, Canada

Land, 2024, vol. 13, issue 3, 1-21

Abstract: In the evolving field of digital soil mapping (DSM), the determination of sample size remains a pivotal challenge, particularly for large-scale regional projects. We introduced the Jensen-Shannon Divergence (D JS ), a novel tool recently applied to DSM, to determine optimal sample sizes for a 2790 km 2 area in Ontario, Canada. Utilizing 1791 observations, we generated maps for cation exchange capacity (CEC), clay content, pH, and soil organic carbon (SOC). We then assessed sample sets ranging from 50 to 4000 through conditioned Latin hypercube sampling (cLHS), feature space coverage sampling (FSCS), and simple random sampling (SRS) to calibrate random forest models, analyzing performance via concordance correlation coefficient and root mean square error. Findings reveal D JS as a robust estimator for optimal sample sizes—865 for cLHS, 874 for FSCS, and 869 for SRS, with property-specific optimal sizes indicating the potential for enhanced DSM accuracy. This methodology facilitates a strategic approach to sample size determination, significantly improving the precision of large-scale soil mapping. Conclusively, our research validates the utility of D JS in DSM, offering a scalable solution. This advancement holds considerable promise for improving soil management and sustainability practices, underpinning the critical role of precise soil data in agricultural productivity and environmental conservation.

Keywords: Jensen–Shannon divergence; sample size; sample density; conditioned Latin hypercube; feature space coverage; simple random sampling; calibration; learning curve (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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