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Prediction of soil organic matter using a spatially constrained local partial least squares regression and the C hinese vis– NIR spectral library
Author(s) -
Shi Z.,
Ji W.,
Viscarra Rossel R. A.,
Chen S.,
Zhou Y.
Publication year - 2015
Publication title -
european journal of soil science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.244
H-Index - 111
eISSN - 1365-2389
pISSN - 1351-0754
DOI - 10.1111/ejss.12272
Subject(s) - partial least squares regression , calibration , local regression , zoning , regression analysis , regression , soil organic matter , spectral space , linear regression , statistics , mathematics , environmental science , soil science , soil water , polynomial regression , political science , pure mathematics , law
Summary We need to determine the best use of soil vis– NIR spectral libraries that are being developed at regional, national and global scales to predict soil properties from new spectral readings. To reduce the complexity of a calibration dataset derived from the C hinese vis– NIR soil spectral library ( CSSL ), we tested a local regression method that combined geographical sub‐setting with a local partial least squares regression (local‐ PLSR ) that uses a limited number of similar vis– NIR spectra ( k ‐nearest neighbours). The central idea of the local regression, and of other local statistical approaches, is to derive a local prediction model by identifying samples in the calibration dataset that are similar, in spectral variable space, to the samples used for prediction. Here, to derive our local regressions we used E uclidean distance in spectral space between the calibration dataset and prediction samples, and we also used soil geographical zoning to account for similarities in soil‐forming conditions. We tested this approach with the CSSL , which comprised 2732 soil samples collected from 20 provinces in the P eople's R epublic of C hina to predict soil organic matter ( SOM ). Results showed that the prediction accuracy of our spatially constrained local‐ PLSR method ( R 2 = 0.74, RPIQ = 2.6) was better than that from local‐ PLSR ( R 2 = 0.69, RPIQ = 2.3) and PLSR alone ( R 2 = 0.50, RPIQ = 1.5). The coupling of a local‐ PLSR regression with soil geographical zoning can improve the accuracy of local SOM predictions using large, complex soil spectral libraries. The approach might be embedded into vis– NIR sensors for laboratory analysis or field estimation.