In precision agriculture, rapid, non-destructive, cost-effective and convenient soil analysis techniques are needed for soil management, crop quality control using fertilizer, manure and compost, and variable-rate input for soil variability in a field. Visible and near-infrared (Vis-NIR) spectroscopy is an effective measurement method for estimating several soil parameters at once.

We developed nineteen-soil-parameter (NSP) calibration models based on Vis-NIR (305–1700 nm) underground soil reflectance spectra collected using the real-time soil sensor (RTSS) with a differential global positioning system (DGPS), in order to create each parameter soil maps. The investigated soil parameters were moisture content, soil organic matter, pH, electrical conductivity, cation exchange capacity, total carbon, ammonium nitrogen, hot water extractable nitrogen, nitrate nitrogen, total nitrogen, exchangeable potassium, exchangeable calcium, exchangeable magnesium, boron, copper, manganese, zinc, available phosphorus, and phosphorus absorptive coefficient.

The experimental site is a commercial upland field with alluvial soil located in Hokkaido, Japan. To develop NSP calibration models, soil samples were collected from the corresponding scanning positions of Vis-NIR data using the RTSS. Partial least-squares regression (PLSR) coupled with leave-one-out cross-validation method were used to establish the relationship between Vis-NIR underground soil reflectance spectra captured by the RTSS and NSP values obtained through soil chemical analysis. To develop NSP calibration models and obtain the sensitivity analysis using PLSR, the Unscrambler software was used. As results, we show the coefficient of correlation, coefficient of determination, root mean square error and residual prediction deviation. Accuracy of the calibration models in terms of each value of results was compared with that of previous studies, which includes field-based and lab-based results using Vis-NIR-MIR (mid-infrared) wavelengths.