How similar is "similar," or what is the best measure of soil spectral and physiochemical similarity?

R Zeng,W J Pan,X Y Jin,C Y Jiang,W C Gao,K Cai,P Y Zhang,D C Li,B W Wu,J P Zhang,C H Wang,Priyabrata Santra

doi:10.1371/journal.pone.0247028

Abstract

Spectral similarity indices were used to select similar soil samples from a spectral library and improve the predictive accuracy of target samples. There are many similarity indices available, and precisely how to select the optimum index has become a critical question. Five similarity indices were evaluated: Spectral angle mapper (SAM), Euclidean distance (ED), Mahalanobis distance (MD), SAM_pca and ED_pca in the space of principal components applied to a global soil spectral library. The accordance between spectral and compositional similarity was used to select the optimum index. Then the optimum index was evaluated if it can maintain the greatest predictive accuracy when selecting similar samples from a spectral library for the prediction of a target sample using a partial least squares regression (PLSR) model. The evaluated physiochemical properties were: soil organic carbon, pH, cation exchange capacity (CEC), clay, silt, and sand content. SAM and SAM_pca selected samples were closer in composition compared to the target samples. Based on similar samples selected using these two indices, PLSR models achieved the highest predictive accuracy for all soil properties, save for CEC. This validates the hypothesis that the accordance information between spectral and compositional similarity can help select the appropriate similarity index when selecting similar samples from a spectral library for prediction.

Highlights

Visible and near-infrared (VNIR) spectroscopy has demonstrated its ability to predict many soil physiochemical properties, such as soil organic matter (SOM), particle size, and iron content [1,2,3]
Euclidean distance (ED), Mahalanobis distance (MD), and ED_pca selected the same most similar spectra, primarily because of the similar distance calculations of ED and MD. Their matched spectra nearly overlapped with the target samples because the calculations of ED and MD focus on the relative difference of the reflectance values
For cation exchange capacity (CEC), model performances were inconsistent with other results, as the samples selected by SAM_pca were most similar in CEC content; the partial least squares regression (PLSR) models based on similar samples chosen by SAM_pca performed relatively poorly (RPD = 1.49)

Summary

Introduction

Visible and near-infrared (VNIR) spectroscopy has demonstrated its ability to predict many soil physiochemical properties, such as soil organic matter (SOM), particle size, and iron content [1,2,3]. In addition to its wide use for soil properties, comparison of spectra from soil. Explore the relationship between soil spectral and physiochemical similarity awarded to RZ), National Key Research and Development Program of China (No.2017YFC0803807), National College Students Innovation and Entrepreneurship Training Program (201910300005Z, awarded to JPZ) and National Key Research and Development Program of China (2017YFB0503903).

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