In-situ multiple parameter calibration and mapping using a mobile soil sensor

Abstract

ABSTRACT The purpose of this study was to estimate local calibration models for 30 chemical and 4 physical soil properties, based on visible and near-infrared subsurface soil reflectance spectra (VNIR-SRS). Further, we discussed a case of decision-making for precision farming by a grower, using high-resolution soil maps. The experimental sites were located in rotational upland fields of taro in Saitama Prefecture, Japan. The parent materials in the experimental sites were Light Clay, Clay Loam, and Sandy Clay Loam. A mobile soil sensor (SAS3000) attached to a tractor was used to measure the VNIR-SRS (350 to 1700 nm). A total of 231 field-moist soil samples and corresponding VNIR-SRS data were collected from 23 fields (6 growers), from 2017 to 2018 period, to estimate local calibration models for the 34 properties. A total of 34 local calibration models were estimated using the second derivative pretreatment and partial least squares regression analysis. Excluding several outliers, the coefficient of determination (R 2) for the local calibration models was 0.81 or more. Immediately after soil sensing in the field, we generated high-resolution soil property maps for the growers’ fields, using the VNIR-SRS data and the 34 local calibration models stored in the SAS3000. The grower involved in the map-based decision-making recalled past crop management, growth, yield, etc., in the target field, and selected a cost-effective soil management technique. In addition, the six growers in the study stressed the importance of understanding the high-resolution relative variability among multiple soil properties, between fields and within a given field.