For each of these two areas a detailed legacy soil class map is available. The comparison was conducted in two study areas: Ammertal (Germany) and Raffelson (USA). These sampling designs were compared based on the overall accuracy of predicted soil class maps obtained by these three prediction methods. 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). 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. Simple random sampling (SRS), which does not utilize the environmental features, is added as a reference design. Two types of sampling design for calibrating the prediction models are compared: conditioned Latin hypercube sampling (CLHS) and feature space coverage sampling (FSCS). This study investigates sampling design for mapping soil classes based on multiple environmental features associated with the soil classes.
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