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Copyright © 2023 Nicolas Francos et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0/

Abstract

Soil spectral libraries (SSLs) are important big-data archives (spectra associated with soil properties) that are analyzed via machine-learning algorithms to estimate soil attributes. Since different spectral measurement protocols are applied when constructing SSLs, it is necessary to examine harmonization techniques to merge the data. In recent years, several techniques for harmonization have been proposed, among which the internal soil standard (ISS) protocol is the most largely applied and has demonstrated its capacity to rectify systematic effects during spectral measurements. Here, we postulate that a spectral transfer function (TF) can be extracted between existing (old) SSLs if a subset of samples from two (or more) different SSLs are remeasured using the ISS protocol. A machine-learning TF strategy was developed, assembling random forest (RF) spectral-based models to predict the ISS spectral condition using soil samples from two existing SSLs. These SSLs had already been measured using different protocols without any ISS treatment the Brazilian (BSSL, generated in 2019) and the European (LUCAS, generated in 2009–2012) SSLs. To verify the TF’s ability to improve the spectral assessment of soil attributes after harmonizing the different SSLs’ protocols, RF spectral-based models for estimating organic carbon (OC) in soil were developed. The results showed high spectral similarities between the ISS and the ISS–TF spectral observations, indicating that post-ISS rectification is possible. Furthermore, after merging the SSLs with the TFs, the spectral-based assessment of OC was considerably improved, from R2 = 0.61, RMSE (g/kg) = 12.46 to R2 = 0.69, RMSE (g/kg) = 11.13. Given our results, this paper enhances the importance of soil spectroscopy by contributing to analyses in remote sensing, soil surveys, and digital soil mapping.

Details

Title
A Spectral Transfer Function to Harmonize Existing Soil Spectral Libraries Generated by Different Protocols
Author
Francos, Nicolas 1   VIAFID ORCID Logo  ; Heller-Pearlshtien, Daniela 1   VIAFID ORCID Logo  ; Demattê, José A M 2   VIAFID ORCID Logo  ; Bas Van Wesemael 3   VIAFID ORCID Logo  ; Milewski, Robert 4   VIAFID ORCID Logo  ; Chabrillat, Sabine 5   VIAFID ORCID Logo  ; Tziolas, Nikolaos 6   VIAFID ORCID Logo  ; Adrian Sanz Diaz 7 ; María Julia Yagüe Ballester 7   VIAFID ORCID Logo  ; Gholizadeh, Asa 8   VIAFID ORCID Logo  ; Ben-Dor, Eyal 1   VIAFID ORCID Logo 

 The Remote Sensing Laboratory, Department of Geography and Human Environment, Porter School of Environment and Earth Sciences, Tel-Aviv University, Zelig 10, Tel-Aviv 69978, Israel 
 Department of Soil Science, “Luiz de Queiroz” College of Agriculture—University of São Paulo, Avenida Pádua Dias 11, Piracicaba, SP 13418-260, Brazil 
 Earth and Life Institute, Georges Lemaître Center for Earth and Climate Research, Université Catholique de Louvain, Louvain-la-Neuve 1348, Belgium 
 Helmholtz Zentrum Potsdam Deutsches GeoForschungsZentrum (GFZ), Section 1.4. Remote Sensing and Geoinformatics, Potsdam, Germany 
 Helmholtz Zentrum Potsdam Deutsches GeoForschungsZentrum (GFZ), Section 1.4. Remote Sensing and Geoinformatics, Potsdam, Germany; Leibniz University Hannover, Institute of Soil Science, Herrenhäuser Str. 2, Hannover, Germany 
 School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, Thessaloniki 54123, Greece; Southwest Florida Research and Education Center, Department of Soil and Water Sciences, Institute of Food and Agricultural Sciences, University of Florida, 2685 State Rd 29N, Immokalee, FL 34142, USA 
 GMV Aerospace and Defence, 28760 Tres Cantos, Madrid, Spain 
 Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamycka 129, Suchdol, Prague 16500, Czech Republic 
Editor
Fedor Lisetskii
Publication year
2023
Publication date
2023
Publisher
John Wiley & Sons, Inc.
ISSN
16877667
e-ISSN
16877675
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2770537282
Copyright
Copyright © 2023 Nicolas Francos et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0/