Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool

Abstract

SummaryIn 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 Swedish 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.

Comparison of Vis–NIR on in situ, intact core and dried, sieved soil to estimate clay content at field to regional scales

“…The standard error of prediction (SEP = 5.7%) for in situ leave‐one‐core‐out cross‐validation with our Vis–NIR penetrometer was comparable to the estimation error reported by Wetterlind et al . () who used the Veris® penetrometer (RMSE = 5.5–6.5%) and by Quraishi & Mouazen () with a bulk density sensor prototype (RMSE = 5.48%). The in situ prediction errors for clay in this study, with complex and diverse clay mineralogy, were also comparable to the errors reported by Waiser et al .…”

“…For two very variable fields in Sweden, Wetterlind et al . () predicted clay content (RMSE = 5.5 and 5.6% with σ clay = 22.5–12.6% for the two fields, respectively) with leave‐one‐core‐out cross‐validation. The authors emphasized the need for further Vis–NIR penetrometer studies because of their small dataset (two fields with 20 locations and 60 samples per field).…”

“…The field performance was poor, which the authors attributed to signal attenuation in the bifurcated fibre optic. Two research teams (Kweon et al, 2009;Wetterlind et al, 2015) have evaluated the Veris P4000 TM Vis-NIR spectrometer-penetrometer package (sensor foreoptic plus Vis-NIR spectrometer) with a spectral bandwidth of 8 nm and range of 350-2200 nm. Combining spectral and auxiliary data (EC and insertion force), Kweon et al (2009) developed useful calibrations for bulk density in three out of six Kansas fields (RMSE = 0.07-0.13 g cm −3 , depending on the field) with leave-one-out cross-validation.…”

“…Combining spectral and auxiliary data (EC and insertion force), Kweon et al (2009) developed useful calibrations for bulk density in three out of six Kansas fields (RMSE = 0.07-0.13 g cm −3 , depending on the field) with leave-one-out cross-validation. For two very variable fields in Sweden, Wetterlind et al (2015) predicted clay content (RMSE = 5.5 and 5.6% with clay = 22.5-12.6% for the two fields, respectively) with leave-one-core-out cross-validation. The authors emphasized the need for further Vis-NIR penetrometer studies because of their small dataset (two fields with 20 locations and 60 samples per field).…”

Proximal sensing for soil carbon accounting

“…Using insertion 5 force as a surrogate for bulk density might be possible but would be prone to errors. Wetterlind et al (2015) tested the accuracy of predictions of soil organic matter (SOM) using the P4000 system and evaluated whether the predictions were improved when the sensors were combined. They found that the accuracy of predictions of SOM content using the vis-NIR alone was good, but the inclusion of insertion force only improved the accuracy of predictions of SOM content by about 10%.…”