Soil organic matter prediction with benchtop and implement‐mounted optical reflectance sensing approaches

Abstract

AbstractProximal sensing technologies can densely quantify soil organic matter (OM) variability using visible and near infrared (VNIR) reflectance spectroscopy. However, issues with global calibrations and varying water content can reduce accuracy. Therefore, research was conducted to determine OM prediction accuracy across soil volumetric water content (VWC) with (a) a planter‐mounted optical sensor and (b) multiple combinations of reflectance bands within the VNIR spectrum. Ninety soils collected across Missouri and Illinois were used in the study. Data were collected at three VWC with the Precision Planting SmartFirmer and a benchtop spectrometer. SmartFirmer OM was derived through an internal algorithm, while spectral pre‐processing and machine learning were used for OM prediction in all approaches that used reflectance from the benchtop spectrometer. Results found that SmartFirmer OM predictions were affected by soil VWC, with predicted OM decreasing with increasing VWC. Findings from three modeling approaches using the benchtop spectrometer showed that a continuous spectrum (i.e., 400–1,500 nm) improved performance (RMSE = 5.25 g kg–1) over the discrete waveband approach (RMSE = 9.23 g kg–1). Furthermore, including the entire VNIR region (400–2,500 nm) resulted in the best predictive capability (RMSE = 1.42 g kg–1). However, because a full‐spectrum approach increases cost, using reflectance from 400 to 1,500 nm along with spectral pre‐processing and machine learning may be an acceptable method for estimating OM. These findings contribute to the improvement of commercially available proximal sensors that may be used to monitor soil carbon (C) stocks, assess soil health, or for other precision agriculture applications.