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Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping
Author(s) -
Stumpf Felix,
Schmidt Karsten,
Behrens Thorsten,
SchönbrodtStitt Sarah,
Buzzo Giovanni,
Dumperth Christian,
Wadoux Alexandre,
Xiang Wei,
Scholten Thomas
Publication year - 2016
Publication title -
journal of plant nutrition and soil science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.644
H-Index - 87
eISSN - 1522-2624
pISSN - 1436-8730
DOI - 10.1002/jpln.201500313
Subject(s) - sampling (signal processing) , sampling design , covariate , sample size determination , sample (material) , latin hypercube sampling , digital soil mapping , statistics , field (mathematics) , set (abstract data type) , simple random sample , computer science , mathematics , environmental science , soil science , monte carlo method , soil water , soil classification , population , chemistry , demography , filter (signal processing) , chromatography , sociology , pure mathematics , computer vision , programming language
Most calibration sampling designs for Digital Soil Mapping (DSM) demarcate spatially distinct sample sites. In practical applications major challenges are often limited field accessibility and the question on how to integrate legacy soil samples to cope with usually scarce resources for field sampling and laboratory analysis. The study focuses on the development and application of an efficiency improved DSM sampling design that (1) applies an optimized sample set size, (2) compensates for limited field accessibility, and (3) enables the integration of legacy soil samples. The proposed sampling design represents a modification of conditioned Latin Hypercube Sampling (cLHS), which originally returns distinct sample sites to optimally cover a soil related covariate space and to preserve the correlation of the covariates in the sample set. The sample set size was determined by comparing multiple sample set sizes of original cLHS sets according to their representation of the covariate space. Limited field accessibility and the integration of legacy samples were incorporated by providing alternative sample sites to replace the original cLHS sites. We applied the modified cLHS design (cLHS adapt ) in a small catchment (4.2 km 2 ) in Central China to model topsoil sand fractions using Random Forest regression (RF). For evaluating the proposed approach, we compared cLHS adapt with the original cLHS design (cLHS orig ). With an optimized sample set size n = 30, the results show a similar representation of the cLHS covariate space between cLHS adapt and cLHS orig , while the correlation between the covariates is preserved ( r = 0.40 vs. r = 0.39). Furthermore, we doubled the sample set size of cLHS adapt by adding available legacy samples (cLHS adapt+ ) and compared the prediction accuracies. Based on an external validation set cLHS val ( n = 20), the coefficient of determination ( R 2 ) of the cLHS adapt predictions range between 0.59 and 0.71 for topsoil sand fractions. The R 2 ‐values of the RF predictions based on cLHS adapt+ , using additional legacy samples, are marginally increased on average by 5%.