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Simultaneous assessment of key properties of arid soil by combined PXRF and V is– NIR data
Author(s) -
Weindorf D. C.,
Chakraborty S.,
Herrero J.,
Li B.,
Castañeda C.,
Choudhury A.
Publication year - 2016
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.12320
Subject(s) - near infrared spectroscopy , gypsum , calibration , environmental science , mineralogy , soil science , materials science , chemistry , mathematics , optics , physics , statistics , composite material
Summary Arid soil is common worldwide and has unique properties that often limit agronomic productivity, specifically, salinity expressed as soluble salts and large amounts of calcium carbonate and gypsum. Currently, the most common methods for evaluating these properties in soil are laboratory‐based techniques such as titration, gasometry and electrical conductivity. In this research, we used two proximal sensors (portable X ‐ray fluorescence ( PXRF ) and visible near‐infrared diffuse reflectance spectroscopy ( V is– NIR DRS )) to scan a diverse set ( n = 116) of samples from arid soil in S pain. Then, samples were processed by standard laboratory procedures and the two datasets were compared with advanced statistical techniques. The latter included penalized spline regression ( PSR ), support vector regression ( SVR ) and random forest ( RF ) analysis, which were applied to V is– NIR DRS data, PXRF data and PXRF and V is– NIR DRS data, respectively. Independent validation (30% of the data) of the calibration equations showed that PSR + RF predicted gypsum with a ratio of performance to interquartile distance ( RPIQ ) of 5.90 and residual prediction deviation ( RPD ) of 4.60, electrical conductivity (1:5 soil : water) with RPIQ of 3.14 and RPD of 2.10, C a content with RPIQ of 2.92 and RPD of 2.07 and calcium carbonate equivalent with RPIQ of 2.13 and RPD of 1.74. The combined PXRF and V is– NIR DRS approach was superior to those that use data from a single proximal sensor, enabling the prediction of several properties from two simple, rapid, non‐destructive scans.