Using a digital camera to measure soil organic carbon and iron contents

High-resolution digital soil mapping for applications like precision agriculture requires the collection of good-quality high spatial resolution information. Conventional soil analysis is expensive, time consuming and laborious. The development of proximal soil sensors to lessen the need for or to complement conventional soil analysis is important. Although proximal sensing may produce results that are not as accurate as conventional laboratory analysis, they allow for the collection of larger amounts of data using simpler, cheaper and less laborious techniques. This forms the rationale for proximal soil sensing. This article deals with indirect measurements of soil organic carbon (OC) and iron (Fe) contents using soil colour as the proxy. Measurements of soil colour were made using a digital camera. The RGB tristimuli were transformed to variables from other colour space models and a redness index (RI) and these were used to derive pedotransfer functions for soil OC and Fe. Predictions using univariate as well as full factorial regressions (FFR) of these tristimuli were compared to predictions using visible–near infrared (vis–NIR: 400–1100 nm) spectra with partial least squares regression (PLSR) and a reduced number of wavelengths selected using the variable importance for projection (VIP) with PLSR (VIP-PLSR). For predictions of soil OC content, the VIP-PLSR technique produced predictions with R2adj. and RMSE values of 0.91 and 0.46%. These were only very slightly better than predictions by an FFR of the CIELa*b* tristimuli (R2adj. of 0.91 and an RMSE of 0.48%) and PLSR (R2adj. of 0.91 and an RMSE of 0.50%). Predictions using the logarithmic regression of the CIEv* variable were least accurate with R2adj. and RMSE values of 0.88 and 0.56%. For predictions of Fe, an FFR of the CIELc*h* tristimuli produced an R2adj. of 0.71 and an RMSE of 0.068%, which was better than those obtained by PLSR (R2adj. values of 0.64 and RMSE 0.074%) and VIP-PLSR (R2adj. of 0.64 and 0.075%). Predictions of Fe using the logarithmic regression of the RI produced the least accurate results with R2adj. and RMSE values of 0.56 and 0.081%. In this study, we showed that a digital camera can be used for fast, accurate and non-destructive measurements of soil colour and predictions of soil OC and Fe contents in Brittany, France.