Sampling Designs for Validating Digital Soil Maps: A Review

• Published in: Pedosphere Vol. 28; no. 1; pp. 1 - 15
• Main Authors: BISWAS, Asim, ZHANG, Yakun
• Format: Journal Article
• Language: English
• Published: Beijing Elsevier Ltd 01.02.2018; Elsevier Science Ltd; School of Environmental Sciences, University of Guelph, 50 Stone Road East, Guelph N1G 2W1(Canada)%Department of Natural Resource Sciences, McGill University, 21111 Lakeshore Road, Ste-Anne-de-Bellevue H9X 3V9(Canada); University of Wisconsin-Madison, Department of Soil Science, FD Hole Soils Lab, 1525 Observatory Drive, Madison WI 53706(USA)
• Subjects: 3-D technology; calibration; Conditioning; data collection; Digital mapping; Digital maps; Feasibility; Feature extraction; Geographical distribution; geographical space; geometry; Hypercubes; Interpolation; Latin hypercube sampling; Mapping; model-based design; Parameter estimation; probability; Response surface methodology; Sampling; Sampling designs; Sediments; Soil mapping; Soil maps; soil surveys; Soils; spatial coverage; Spatial distribution; Statistical analysis; three-dimensional (3D) digital soil mapping; spatial coverage; model-based design; Latin hypercube sampling; calibration; geographical space; three-dimensional (3D) digital soil mapping
• ISSN: 1002-0160; 2210-5107
• DOI: 10.1016/S1002-0160(18)60001-3

Sampling design (SD) plays a crucial role in providing reliable input for digital soil mapping (DSM) and increasing its efficiency. Sampling design, with a predetermined sample size and consideration of budget and spatial variability, is a selection procedure for identifying a set of sample locations spread over a geographical space or with a good feature space coverage. A good feature space coverage ensures accurate estimation of regression parameters, while spatial coverage contributes to effective spatial interpolation. First, we review several statistical and geometric SDs that mainly optimize the sampling pattern in a geographical space and illustrate the strengths and weaknesses of these SDs by considering spatial coverage, simplicity, accuracy, and efficiency. Furthermore, Latin hypercube sampling, which obtains a full representation of multivariate distribution in geographical space, is described in detail for its development, improvement, and application. In addition, we discuss the fuzzy k-means sampling, response surface sampling, and Kennard-Stone sampling, which optimize sampling patterns in a feature space. We then discuss some practical applications that are mainly addressed by the conditioned Latin hypercube sampling with the flexibility and feasibility of adding multiple optimization criteria. We also discuss different methods of validation, an important stage of DSM, and conclude that an independent dataset selected from the probability sampling is superior for its free model assumptions. For future work, we recommend: 1) exploring SDs with both good spatial coverage and feature space coverage; 2) uncovering the real impacts of an SD on the integral DSM procedure; and 3) testing the feasibility and contribution of SDs in three-dimensional (3D) DSM with variability for multiple layers.