Spectroscopic solutions for generating new global soil information
Status PubMed-not-MEDLINE Jazyk angličtina Země Spojené státy americké Médium electronic-ecollection
Typ dokumentu časopisecké články, přehledy
PubMed
40432775
PubMed Central
PMC12105478
DOI
10.1016/j.xinn.2025.100839
PII: S2666-6758(25)00042-6
Knihovny.cz E-zdroje
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
While global efforts to operationalize soil spectroscopy are progressing, cooperation is needed to fully leverage its potential for generating digital soil information to support sustainable soil management worldwide. The Global Soil Laboratory Network's soil spectroscopy initiative (GLOSOLAN-Spec), led by the Food and Agriculture Organization of the United Nations (FAO) through its Global Soil Partnership (GSP), is dedicated to the further development and adoption of soil spectroscopy by fostering international collaboration via a scientific community of practice to produce accurate and reliable soil information for sustainable soil management and decision-making. To support this effort, we, a global consortium of soil scientists under the auspices of the International Union of Soil Sciences (IUSS) and GLOSOLAN-Spec, aim to address seven key challenges hindering the adoption of soil spectroscopy worldwide. Here, we offer perspectives on what is needed to advance soil spectroscopy as a routine soil analysis method, emphasizing its potential to generate new and reliable spatial and temporal soil data.
College of Environmental and Resource Sciences Zhejiang University Hangzhou 310058 China
Data Science Department BUCHI Labortechnik AG 9230 Flawil Switzerland
Department of Agrochemistry and Environment University Miguel Hernandez of Elche 03202 Elche Spain
Department of Biological Systems Engineering University of Nebraska Lincoln Lincoln NE 68583 USA
Department of Environment Faculty of Bioscience Engineering Ghent University 9000 Ghent Belgium
Department of Soil and Environment Swedish University of Agriculture Sciences 53231 Skara Sweden
Food and Agriculture Organization of the United Nations 00153 Rome Italy
Imperial College London Imperial Business School London SW7 2AZ UK
LISAH University Montpellier IRD INRAE Institut Agro AgroParisTech 34060 Montpellier France
Nanjing Institute of Geography and Limnology Chinese Academy of Sciences Nanjing 210008 China
School of Environmental Sciences University of Guelph Guelph ON N1G 2W1 Canada
Soil and Land Health World Agroforestry Nairobi 00 100GPO Kenya
Soil and Landscape Science Curtin University Perth WA 6102 Australia
The James Hutton Institute Aberdeen AB15 8QH Scotland
University of the Chinese Academy of Sciences Beijing 100049 China
USDA ARS Range Management Research Unit Las Cruces NM 88003 USA
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McBratney A., Field D.J., Koch A. The dimensions of soil security. Geoderma. 2014;213:203–213. doi: 10.1016/j.geoderma.2013.08.013. DOI
Nocita M., Stevens A., van Wesemael B., et al. Soil spectroscopy: an alternative to wet chemistry for soil monitoring. Adv. Agron. 2015;132:139–159. doi: 10.1016/bs.agron.2015.02.002. DOI
Viscarra R.R.A., Behrens T., Ben-Dor E., et al. Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century. Eur. J. Soil Sci. 2022;73 doi: 10.1111/ejss.13271. DOI
Viscarra Rossel R.A., Shen Z., Ramirez Lopez L., et al. An imperative for soil spectroscopic modelling is to think global but fit local with transfer learning. Earth Sci. Rev. 2024;254 doi: 10.1016/j.earscirev.2024.104797. DOI
Brown D.J., Shepherd K.D., Walsh M.G., et al. Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma. 2006;132:273–290. doi: 10.1016/j.geoderma.2005.04.025. DOI
Hong Y., Chen Y., Chen S., et al. Improving spectral estimation of soil inorganic carbon in urban and suburban areas by coupling continuous wavelet transform with geographical stratification. Geoderma. 2023;430 doi: 10.1016/j.geoderma.2022.116284. DOI
Ma Y., Minasny B., Demattê J.A.M., et al. Incorporating soil knowledge into machine-learning prediction of soil properties from soil spectra. Eur. J. Soil Sci. 2023;74 doi: 10.1111/ejss.13438. DOI
Piccini C., Metzger K., Debaene G., et al. In-field soil spectroscopy in Vis–NIR range for fast and reliable soil analysis: A review. Eur. J. Soil Sci. 2024;75 doi: 10.1111/ejss.13481. DOI
Minasny B., McBratney A.B., Bellon-Maurel V., et al. Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon. Geoderma. 2011;167–168:118–124. doi: 10.1016/j.geoderma.2011.09.008. DOI
Stenberg B., Koganti T., Castaldi F., et al. D5.1 ProbeField: Best Practice Protocol for Field Spectroscopy and Assessment by Soil Spectral Library Based Calibrations (Version 1) Zenodo. 2024 doi: 10.5281/zenodo.14150972. DOI