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Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool
Author(s) -
Wetterlind J.,
Piikki K.,
Stenberg B.,
Söderström M.
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.12228
Subject(s) - silt , soil texture , partial least squares regression , soil science , environmental science , soil organic matter , soil test , soil water , remote sensing , mathematics , geology , statistics , paleontology
Summary In soil mapping, combining information from conceptually different proximal soil sensors can increase the accuracy of prediction and robustness of the model when compared with using individual sensors. In this study the predictability of soil texture (clay, silt and sand fractions) and soil organic matter ( SOM ) content was tested with a commercial integrated soil profiling tool that included sensors for measuring apparent electrical conductivity ( EC a ), reflectance in the visible and near‐infrared (vis‐ NIR ) parts of the electromagnetic spectrum and insertion force ( IF ). The measurements were made at 20 locations on each of two S wedish farms. At every location, sensor measurements were made at 1.5‐cm intervals from the soil surface to a depth of 0.8 m. Soil samples were collected close to the sensor measurement points and analysed for texture and SOM content. Farm‐specific calibrations were developed for texture and SOM with each sensor separately and with combinations of all three sensors. The calibrations were made using both partial least squares regression ( PLSR ) and simple linear regression. The results for the two farms were quite consistent in terms of rank in prediction performance between the individual sensors and the sensor combinations. The vis‐ NIR spectrometer was the best individual sensor for predicting the soil properties tested on both farms, with root mean square error of cross‐validation ( RMSECV ) of 0.3–0.5% for SOM , about 6% for clay and silt and 10–11% for sand. The inclusion of IF reduced the RMSECV for predictions of SOM content by about 10%. For soil texture, including EC a reduced the RMSECV on average for all particle size fractions by 5–10%. However, the small improvements obtained by combining sensors do not provide strong support for combining vis‐ NIR sensor measurements with measurements of EC a and or IF .