Abstract
This chapter discusses methods for soil sampling that allow calibration of proximal sensor readings to soil properties. Conditioned Latin hypercube sampling (cLHS) was recently proposed as a method for sampling based on covariates obtained from proximal soil sensors. The method provides full coverage of the range of each variable by maximally stratifying the marginal distribution. A modification of cLHS was made so that it samples more on the edge of the distribution. This modification, called DLHS, is inspired by the D-optimality criterion in linear regression, where the design will place sample points on the corner of the distribution. We run a simulation to test the performance of cLHS. The simulation assumed a known form of the response function of EM38, EM31, and elevation to clay content. Results showed that when the form of the model is known, it is beneficial to place more sample points on the corners of the hypercube. When the form is unknown, conventional cLHS performs better.
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Acknowledgement
We thank Jaap de Gruijter for his comments and suggestions on the chapter.
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Minasny, B., McBratney, A. (2010). Conditioned Latin Hypercube Sampling for Calibrating Soil Sensor Data to Soil Properties. In: Viscarra Rossel, R., McBratney, A., Minasny, B. (eds) Proximal Soil Sensing. Progress in Soil Science. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8859-8_9
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DOI: https://doi.org/10.1007/978-90-481-8859-8_9
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