Sample Size Optimization for Digital Soil Mapping: An Empirical Example

Author:

Saurette Daniel D.12ORCID,Heck Richard J.1ORCID,Gillespie Adam W.1,Berg Aaron A.3ORCID,Biswas Asim1ORCID

Affiliation:

1. School of Environmental Sciences, University of Guelph, 50 Stone Rd East, Guelph, ON N1G 2W1, Canada

2. Ontario Ministry of Agriculture, Food and Rural Affairs, 1 Stone Rd West, Guelph, ON N1G 2Y4, Canada

3. Department of Geography, Environment & Geomatics, University of Guelph, 50 Stone Rd East, Guelph, ON N1G 2W1, Canada

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 (DJS), a novel tool recently applied to DSM, to determine optimal sample sizes for a 2790 km2 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 DJS 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 DJS 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.

Funder

Natural Science and Engineering Research Council (NSERC) of Canada, which supported and funded this project through an NSERC Postgraduate Scholarship

Publisher

MDPI AG

Reference89 articles.

1. Mapping Systems Working Group (1981). A Soil Mapping System for Canada: Revised., Land Resource Research Institute, Research Branch, Agriculture Canada.

2. Coen, G.M. (1987). Soil Survey Handook, Land Resource Research Centre, Research Branch, Agriculture Canada.

3. Divergence Metrics for Determining Optimal Training Sample Size in Digital Soil Mapping;Saurette;Geoderma,2023

4. Machine Learning for Digital Soil Mapping: Applications, Challenges and Suggested Solutions;Wadoux;Earth-Sci. Rev.,2020

5. A Conditioned Latin Hypercube Method for Sampling in the Presence of Ancillary Information;Minasny;Comput. Geosci.,2006

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