Ramirez‐Lopez, L., Wadoux, A.M.J.-C., Franceschini, M.H.D., Terra, F.S., Marques, K.P.P., Sayão, V.M., and Demattê, J.A.M. (2019) Robust soil mapping at the farm scale with vis–NIR spectroscopy. European Journal of Soil Science, 70 (2). pp. 378-393.

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Abstract

Sustainable agriculture practices are often hampered by the prohibitive costs associated with the generation of fine-resolution soil maps. Recently, several papers have been published highlighting how visible and near infrared (vis–NIR) reflectance spectroscopy may offer an alternative to address this problem by increasing the density of soil sampling and by reducing the number of conventional laboratory analyses needed. However, for farm-scale soil mapping, previous studies rarely focused on sample optimization for the calibration of vis–NIR models or on robust modelling of the spatial variation of soil properties predicted by vis–NIR spectroscopy. In the present study, we used soil vis–NIR spectroscopy models optimized in terms of both number of calibration samples and accuracy for high-resolution robust farm-scale soil mapping and addressed some of the most common pitfalls identified in previous research. We collected 910 samples from 458 locations at two depths (A, 0–0.20 m; B, 0.80–1.0 m) in the state of São Paulo, Brazil. All soil samples were analysed by conventional methods and scanned in the vis–NIR spectral range. With the vis–NIR spectra only, we inferred statistically the optimal set size and the best samples with which to calibrate vis–NIR models. The calibrated vis–NIR models were validated and used to predict soil properties for the rest of the samples. The prediction error of the spectroscopic model was propagated through the spatial analysis, in which robust block kriging was used to predict particle-size fractions and exchangeable calcium content for each depth. The results indicated that statistical selection of the calibration samples based on vis–NIR spectra considerably decreased the need for conventional chemical analysis for a given level of mapping accuracy. The methods tested in this research were developed and implemented using open-source software. All codes and data are provided for reproducible research purposes.

Item ID:	94238
Item Type:	Article (Research - C1)
ISSN:	1365-2389
Copyright Information:	© 2018 The Authors. European Journal of Soil Science published by John Wiley & Sons Ltd on behalf of British Society of Soil Science. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Date Deposited:	22 Oct 2026 00:35
FoR Codes:	41 ENVIRONMENTAL SCIENCES > 4106 Soil sciences > 410602 Pedology and pedometrics @ 100%
SEO Codes:	18 ENVIRONMENTAL MANAGEMENT > 1806 Terrestrial systems and management > 180605 Soils @ 100%
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