Comparison of conditioned Latin hypercube and feature space coverage sampling for predicting soil classes using simulation from soil maps

By Tianwu Ma, Dick Brus, A-Xing Zhu, Lei Zhang, Thomas Scholten. 2020

56 citations recorded by the source corpus.

This study investigates sampling design for mapping soil classes based on multiple environmental features associated with the soil classes. Two types of sampling design for calibrating the prediction models are compared: conditioned Latin hypercube sampling (CLHS) and feature space coverage sampling (FSCS). Simple random sampling (SRS), which does not utilize the environmental features, is added as a reference design. The sample sizes used are 20, 30, 40, 50, 75, and 100 points, and at each sample size 100 sample sets were drawn using each of the three types of design. Each of these sample sets was then used to calibrate three prediction models: random forest (RF), individual predictive soil mapping (iPSM), and multinomial logistic regression (MLR). These sampling designs were compared based on the overall accuracy of predicted soil class maps obtained by these three prediction methods. The comparison was conducted in two study areas: Ammertal (Germany) and Raffelson (USA). For each of these two areas a detailed legacy soil class map is available. These soil class maps were used as references in a simulation study for the comparison. Results of both study areas show that on average FSCS outperforms CLHS and SRS for all three prediction methods. The difference in estimated medians of overall accuracy with CLHS and SRS was marginal. Moreover, the variation in overall accuracy among sample sets of the same size was considerably smaller for FSCS than that for CLHS. These results in the two study areas suggest that FSCS is a more effective sampling design.

Subfields: Civil and Structural Engineering, Environmental Engineering, Soil Science

View Paper: https://www.semanticscholar.org/paper/688c23255b4a972bfafb4c20b3fb314d8aa62b17

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In Civil and Structural Engineering

In Environmental Engineering

In Soil Science

Citations in the corpus

Machine learning for digital soil mapping: Applications, challenges and suggested solutions (2020, result): “These results were confirmed by Ma et al. (2020) for soil classes mapping with RF, and similar conclusions were drawn by Lagacherie et al. (2020) in a numerical experiment mapping a continuous soil property with quantile regression forest.”