Evaluating the synergy of three soil spectrometers for improving the prediction and mapping of soil properties in a high anthropic management area: A case of study from Southeast Brazil

Abstract Precision agriculture aspires to manage the soil spatial variability within a field. However, agricultural practices can decrease the spatial autocorrelation of soil properties at a short distance, which requires an increase in sampling density to improve the reliability of interpolated surfaces. Data fusion of several soil spectrometers is a promising alternative that can increase knowledge about soil physicochemical variability. Therefore, this research aimed to evaluate whether the synergy of portable X-ray fluorescence, Vis-NIR, and Mid-Infrared spectrometers can successfully increase sampling density and improve the quality of the soil attribute maps, in a high fertilization management study area. We conducted a case study on a sugarcane field in Southeast Brazil. Our spectral data fusion approach did not increase the prediction performance for soil properties, achieving the same accuracy (α ≤ 5%) of the single spectral ranges (Vis-NIR or MIR or pXRF) individually. Thus, Vis-NIR was chosen to evaluate if spectroscopy could improve soil properties prediction and mapping. We used three dataset scenarios to build soil property maps: two were based on soil wet-chemical laboratory analysis using 1 sample per ha, and 1 sample per 0.25 ha and a third used a spectroscopy model with 0.25 samples per ha calibrated using soil wet-chemical laboratory analysis at the rate of 1 sample per ha. In a field with strong anthropic influences, due to liming and soil fertilization, Vis-NIR spectroscopy cannot predict well the amount of phosphorus and pH at a sampling density of 1 sample per 0.25 ha. Increased soil sampling enhances soil mapping accuracy only for chemical properties (e.g., potassium) that are correlated with soil texture. Nevertheless, soil spectra can classify areas of similar soil texture and help farmers with the site-specific management of crops.