{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T09:36:20Z","timestamp":1771061780056,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:00:00Z","timestamp":1679011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the European Community\u2019s Framework Programme Horizon 2020","award":["870378"],"award-info":[{"award-number":["870378"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In-situ infrared soil spectroscopy is prone to the effects of ambient factors, such as moisture, shadows, or roughness, resulting in measurements of compromised quality, which is amplified when multiple sensors are used for data collection. Aiming to provide accurate estimations of common physicochemical soil properties, such as soil organic carbon (SOC), texture, pH, and calcium carbonates based on in-situ reflectance captured by a set of low-cost spectrometers operating at the shortwave infrared region, we developed an AI-based spectral transfer function that maps fields to laboratory spectra. Three test sites in Cyprus, Lithuania, and Greece were used to evaluate the proposed methodology, while the dataset was harmonized and augmented by GEO-Cradle regional soil spectral library (SSL). The developed dataset was used to calibrate and validate machine learning models, with the attained predictive performance shown to be promising for directly estimating soil properties in-situ, even with sensors with reduced spectral range. Aiming to set a baseline scenario, we completed the exact same modeling experiment under laboratory conditions and performed a one-to-one comparison between field and laboratory modelling accuracy metrics. SOC and pH presented an R2 of 0.43 and 0.32 when modeling the in-situ data compared to 0.63 and 0.41 of the laboratory case, respectively, while clay demonstrated the highest accuracy with an R2 value of 0.87 in-situ and 0.90 in the laboratory. Calcium carbonates were also attempted to be modeled at the studied spectral region, with the expected accuracy loss from the laboratory to the in-situ to be observable (R2 = 0.89 for the laboratory and 0.67 for the in-situ) but the reduced dataset variability combined with the calcium carbonate characteristics that are spectrally active in the region outside the spectral range of the used in-situ sensor, induced low RPIQ values (less than 0.50), signifying the importance of the suitable sensor selection.<\/jats:p>","DOI":"10.3390\/rs15061624","type":"journal-article","created":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T03:33:57Z","timestamp":1679024037000},"page":"1624","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["On-Site Soil Monitoring Using Photonics-Based Sensors and Historical Soil Spectral Libraries"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1190-5772","authenticated-orcid":false,"given":"Konstantinos","family":"Karyotis","sequence":"first","affiliation":[{"name":"Laboratory of Remote Sensing, Spectroscopy and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece"},{"name":"Department of Information and Electronic Engineering, International Hellenic University of Greece (IHU), Alexander Campus, 57400 Sindos, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1904-9029","authenticated-orcid":false,"given":"Nikolaos L.","family":"Tsakiridis","sequence":"additional","affiliation":[{"name":"Laboratory of Remote Sensing, Spectroscopy and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1502-3219","authenticated-orcid":false,"given":"Nikolaos","family":"Tziolas","sequence":"additional","affiliation":[{"name":"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1619-778X","authenticated-orcid":false,"given":"Nikiforos","family":"Samarinas","sequence":"additional","affiliation":[{"name":"Laboratory of Remote Sensing, Spectroscopy and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece"}]},{"given":"Eleni","family":"Kalopesa","sequence":"additional","affiliation":[{"name":"Laboratory of Remote Sensing, Spectroscopy and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2366-1365","authenticated-orcid":false,"given":"Periklis","family":"Chatzimisios","sequence":"additional","affiliation":[{"name":"Department of Information and Electronic Engineering, International Hellenic University of Greece (IHU), Alexander Campus, 57400 Sindos, Greece"},{"name":"Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM 87131, USA"}]},{"given":"George","family":"Zalidis","sequence":"additional","affiliation":[{"name":"Laboratory of Remote Sensing, Spectroscopy and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/bs.agron.2022.08.002","article-title":"Chapter Three-Sensing technologies for characterizing and monitoring soil functions: A review","volume":"Volume 177","author":"Sparks","year":"2023","journal-title":"Advances in Agronomy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7415","DOI":"10.1007\/s00216-014-8202-0","article-title":"G\u00fcnter Gauglitz and David S. 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