Characterization of field-scale soil variation using a stepwise multi-sensor fusion approach and a cost-benefit analysis

The potential of a stepwise fusion proximally sensed portable X-ray fluorescence (pXRF) spectra and electromagnetic induction (EMI) with remote Sentinel-2 bands digital elevation model (DEM) was investigated for predicting soil physicochemical properties in pedons across heterogeneous 80-ha crop field Wisconsin, USA. We found that pXRF partial least squares regression (PLSR) models can predict sand, total nitrogen (TN), organic carbon (OC), silt contents, clay validation R2 0.81, 0.74, 0.73, 0.68, 0.64 at the pedon scale but performed less well pH (R2 = 0.51). A combination EMI, Sentinel-2, DEM data showed promise mapping TN two depths Ap horizon thickness depth field. clustering analysis using combinations mapped or proximal sensing suggested improved characterization field-scale variability properties. cost-benefit most accurate management zones (MZs) topsoil be generated only estimated property maps while it costly as compared to other sources. For an intermediate-high (for topsoil) high (subsoil) accuracy moderate economic budget, sensors (proximal + DEM) might better approach effective MZs generation than collecting samples laboratory latter produced topsoil. It concluded useful key (e.g., TN, OC, silt, clay) different depths, provides way delineate are decision-making.