{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T00:44:10Z","timestamp":1783817050641,"version":"3.55.0"},"reference-count":83,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T00:00:00Z","timestamp":1676592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002946","name":"DLR Space Agency","doi-asserted-by":"publisher","award":["50EE1529"],"award-info":[{"award-number":["50EE1529"]}],"id":[{"id":"10.13039\/501100002946","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002946","name":"DLR Space Agency","doi-asserted-by":"publisher","award":["CUP F83C2200016000"],"award-info":[{"award-number":["CUP F83C2200016000"]}],"id":[{"id":"10.13039\/501100002946","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Italian Space Agency (ASI)","award":["50EE1529"],"award-info":[{"award-number":["50EE1529"]}]},{"name":"Italian Space Agency (ASI)","award":["CUP F83C2200016000"],"award-info":[{"award-number":["CUP F83C2200016000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing and soil spectroscopy applications are valuable techniques for soil property estimation. Soil organic matter (SOM) and calcium carbonate are important factors in soil quality, and although organic matter is well studied, calcium carbonates require more investigation. In this study, we validated the performance of laboratory soil spectroscopy for estimating the aforementioned properties with referenced in situ data. We also examined the performance of imaging spectroscopy sensors, such as the airborne HySpex and the spaceborne PRISMA. For this purpose, we applied four commonly used machine learning algorithms and six preprocessing methods for the evaluation of the best fitting algorithm.. The study took place over crop areas of Amyntaio in Northern Greece, where extensive soil sampling was conducted. This is an area with a very variable mineralogical environment (from lignite mine to mountainous area). The SOM results were very good at the laboratory scale and for both remote sensing sensors with R2 = 0.79 for HySpex and R2 = 0.76 for PRISMA. Regarding the calcium carbonate estimations, the remote sensing accuracy was R2 = 0.82 for HySpex and R2 = 0.36 for PRISMA. PRISMA was still in the commissioning phase at the time of the study, and therefore, the acquired image did not cover the whole study area. Accuracies for calcium carbonates may be lower due to the smaller sample size used for the modeling procedure. The results show the potential for using quantitative predictions of SOM and the carbonate content based on soil and imaging spectroscopy at the air and spaceborne scales and for future applications using larger datasets.<\/jats:p>","DOI":"10.3390\/rs15041106","type":"journal-article","created":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T01:36:37Z","timestamp":1676856997000},"page":"1106","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Evaluation of Airborne HySpex and Spaceborne PRISMA Hyperspectral Remote Sensing Data for Soil Organic Matter and Carbonates Estimation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6124-7048","authenticated-orcid":false,"given":"Theodora","family":"Angelopoulou","sequence":"first","affiliation":[{"name":"Laboratory of Remote Sensing, Spectroscopy, and GIS, Department of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8600-5168","authenticated-orcid":false,"given":"Sabine","family":"Chabrillat","sequence":"additional","affiliation":[{"name":"Helmholtz Center, Potsdam GFZ German Research Centre for Geosciences, Section Remote Sensing and Geoinformatics, 14473 Potsdam, Germany"},{"name":"Institute of Soil Science, Leibniz University Hannover, Herrenh\u00e4user Str. 2, 30419 Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0587-8926","authenticated-orcid":false,"given":"Stefano","family":"Pignatti","sequence":"additional","affiliation":[{"name":"Institute of Methodologies for Environmental Analysis (IMAA), National Council of Research (CNR), C. da S. Loja, Tito Scalo, 85050 Potenza, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7835-7689","authenticated-orcid":false,"given":"Robert","family":"Milewski","sequence":"additional","affiliation":[{"name":"Helmholtz Center, Potsdam GFZ German Research Centre for Geosciences, Section Remote Sensing and Geoinformatics, 14473 Potsdam, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1190-5772","authenticated-orcid":false,"given":"Konstantinos","family":"Karyotis","sequence":"additional","affiliation":[{"name":"Laboratory of Remote Sensing, Spectroscopy, and GIS, Department of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"},{"name":"School of Science and Technology, International Hellenic University, 14th km Thessaloniki\u2014N. Moudania, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximilian","family":"Brell","sequence":"additional","affiliation":[{"name":"Helmholtz Center, Potsdam GFZ German Research Centre for Geosciences, Section Remote Sensing and Geoinformatics, 14473 Potsdam, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4646-3791","authenticated-orcid":false,"given":"Thomas","family":"Ruhtz","sequence":"additional","affiliation":[{"name":"Institute for Space Sciences, Free University Berlin, 12165 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7058-5986","authenticated-orcid":false,"given":"Dionysis","family":"Bochtis","sequence":"additional","affiliation":[{"name":"Centre of Research and Technology\u2014Hellas (CERTH), Institute for Bio\u2014Economy and Agri\u2014Technology (iBO), 57001 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Zalidis","sequence":"additional","affiliation":[{"name":"Laboratory of Remote Sensing, Spectroscopy, and GIS, Department of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,17]]},"reference":[{"key":"ref_1","unstructured":"FAO (2017). Soil Organic Carbon the Hidden Potential, Food and Agriculture Organization."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111","DOI":"10.5194\/soil-2-111-2016","article-title":"The significance of soils and soil science towards realization of the United Nations sustainable development goals","volume":"2","author":"Keesstra","year":"2016","journal-title":"Soil"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5361","DOI":"10.1111\/gcb.14376","article-title":"Climate and plant controls on soil organic matter in coastal wetlands","volume":"24","author":"Osland","year":"2018","journal-title":"Glob. Chang. Biol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"81","DOI":"10.4155\/cmt.13.77","article-title":"Global soil carbon: Understanding and managing the largest terrestrial carbon pool","volume":"5","author":"Scharlemann","year":"2014","journal-title":"Carbon Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"12735","DOI":"10.1038\/srep12735","article-title":"An invisible soil acidification: Critical role of soil carbonate and its impact on heavy metal bioavailability","volume":"5","author":"Wang","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.chemgeo.2016.11.029","article-title":"Carbonate minerals in the global carbon cycle","volume":"449","author":"Martin","year":"2017","journal-title":"Chem. Geol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/S0016-7061(00)00037-9","article-title":"Estimation or simulation of soil properties? An optimization problem with conflicting criteria","volume":"97","author":"Goovaerts","year":"2000","journal-title":"Geoderma"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1007\/s11119-009-9123-3","article-title":"Estimating soil organic carbon from soil reflectance: A review","volume":"11","author":"Ladoni","year":"2010","journal-title":"Precis. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2013\/616578","article-title":"Quantitative soil spectroscopy","volume":"2013","author":"Chabrillat","year":"2013","journal-title":"Appl. Environ. Soil Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/S0065-2113(10)07005-7","article-title":"Visible and near infrared spectroscopy in soil science","volume":"107","author":"Stenberg","year":"2010","journal-title":"Adv. Agron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/bs.agron.2015.02.002","article-title":"Soil Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring","volume":"132","author":"Nocita","year":"2015","journal-title":"Adv. Agron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.geoderma.2015.04.017","article-title":"Spectral libraries for quantitative analyses of tropical Brazilian soils: Comparing vis-NIR and mid-IR reflectance data","volume":"255","author":"Terra","year":"2015","journal-title":"Geoderma"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10712-019-09524-0","article-title":"Imaging Spectroscopy for Soil Mapping and Monitoring","volume":"40","author":"Chabrillat","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Stevens, A., Nocita, M., T\u00f3th, G., Montanarella, L., and van Wesemael, B. (2013). Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0066409"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.geoderma.2009.12.025","article-title":"Using data mining to model and interpret soil diffuse reflectance spectra","volume":"158","author":"Rossel","year":"2010","journal-title":"Geoderma"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.isprsjprs.2018.11.026","article-title":"Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands","volume":"147","author":"Castaldi","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Castaldi, F., Chabrillat, S., and van Wesemael, B. (2019). Sampling Strategies for Soil Property Mapping Using Multispectral Sentinel-2 and Hyperspectral EnMAP Satellite Data. Remote Sens., 11.","DOI":"10.3390\/rs11030309"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Angelopoulou, T., Tziolas, N., Balafoutis, A., Zalidis, G., and Bochtis, D. (2019). Remote sensing techniques for soil organic carbon estimation: A review. Remote Sens., 11.","DOI":"10.3390\/rs11060676"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Angelopoulou, T., Balafoutis, A., Zalidis, G., and Bochtis, D. (2020). From laboratory to proximal sensing spectroscopy for soil organic carbon estimation-A review. Sustainability, 12.","DOI":"10.3390\/su12020443"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1080\/01431160010006962","article-title":"Mapping of several soil properties using DAIS-7915 hyperspectral scanner data\u2014A case study over soils in Israel","volume":"23","author":"Patkin","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vohland, M., Ludwig, M., Thiele-Bruhn, S., and Ludwig, B. (2017). Quantification of Soil Properties with Hyperspectral Data: Selecting Spectral Variables with Different Methods to Improve Accuracies and Analyze Prediction Mechanisms. Remote Sens., 9.","DOI":"10.3390\/rs9111103"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Steinberg, A., Chabrillat, S., Stevens, A., Segl, K., and Foerster, S. (2016). Prediction of common surface soil properties based on Vis-NIR airborne and simulated EnMAP imaging spectroscopy data: Prediction accuracy and influence of spatial resolution. Remote Sens., 8.","DOI":"10.3390\/rs8070613"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.rse.2016.03.025","article-title":"Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon","volume":"179","author":"Castaldi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ward, K.J., Chabrillat, S., Brell, M., Castaldi, F., Spengler, D., and Foerster, S. (2020). Mapping Soil Organic Carbon for Airborne and Simulated EnMAP Imagery Using the LUCAS Soil Database and a Local PLSR. Remote Sens., 12.","DOI":"10.5194\/egusphere-egu2020-3013"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1080\/02757259509532297","article-title":"Spectral reflectance of carbonate mineral mixtures and bidirectional reflectance theory: Quantitative analysis techniques for application in remote sensing","volume":"13","year":"1995","journal-title":"Remote Sens. Rev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1366\/0003702904086821","article-title":"Near-Infrared Reflectance Analysis of Carbonate Concentration in Soils","volume":"44","author":"Banin","year":"1990","journal-title":"Appl. Spectrosc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.ecolind.2009.05.001","article-title":"Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties","volume":"11","author":"Summers","year":"2011","journal-title":"Ecol. Indic."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.geoderma.2017.11.014","article-title":"Towards prediction of soil erodibility, SOM and CaCO3 using laboratory Vis-NIR spectra: A case study in a semi-arid region of Iran","volume":"314","author":"Ostovari","year":"2018","journal-title":"Geoderma"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1255\/jnirs.1157","article-title":"Using visible and near infrared spectroscopy to estimate carbonates and gypsum in soils in arid and subhumid regions of Isfahan, Iran","volume":"23","author":"Khayamim","year":"2015","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.geoderma.2018.06.006","article-title":"Importance of the spatial extent for using soil properties estimated by laboratory VNIR\/SWIR spectroscopy: Examples of the clay and calcium carbonate content","volume":"330","author":"Gomez","year":"2018","journal-title":"Geoderma"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1016\/j.rse.2007.06.014","article-title":"Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements","volume":"112","author":"Lagacherie","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"218","DOI":"10.17221\/113\/2015-SWR","article-title":"Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features","volume":"10","author":"Gholizadeh","year":"2015","journal-title":"Soil Water Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.earscirev.2016.01.012","article-title":"A global spectral library to characterize the world\u2019s soil","volume":"155","author":"Behrens","year":"2016","journal-title":"Earth-Sci. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Samarinas, N., Tziolas, N., and Zalidis, G. (2020). Improved Estimations of Nitrate and Sediment Concentrations Based on SWAT Simulations and Annual Updated Land Cover Products from a Deep Learning Classification Algorithm. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9100576"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tziolas, N., Tsakiridis, N., Ben-Dor, E., Theocharis, J., and Zalidis, G. (2020). Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data. Remote Sens., 12.","DOI":"10.3390\/rs12091389"},{"key":"ref_36","first-page":"1367","article-title":"Methods of soil analysis. Part 2. Chemical and microbiological properties","volume":"9","author":"Allison","year":"1965","journal-title":"Agron. Monogr."},{"key":"ref_37","first-page":"96","article-title":"Soil chemical analysis-Advanced course, published by the author","volume":"991","author":"Jackson","year":"1956","journal-title":"Madison Wis."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"464","DOI":"10.2134\/agronj1962.00021962005400050028x","article-title":"Hydrometer method improved for making particle size analyses of soils 1","volume":"54","author":"Bouyoucos","year":"1962","journal-title":"Agron. J."},{"key":"ref_39","first-page":"112","article-title":"Reflectance measurements of soils in the laboratory: Standards and protocols","volume":"245","author":"Ong","year":"2015","journal-title":"Geoderma"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3460","DOI":"10.1109\/TGRS.2016.2518930","article-title":"Improving Sensor Fusion: A Parametric Method for the Geometric Coalignment of Airborne Hyperspectral and Lidar Data","volume":"54","author":"Brell","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.1080\/01431160110115834","article-title":"Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric\/topographic correction","volume":"23","author":"Richter","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6888","DOI":"10.1364\/AO.389485","article-title":"Leonardo spaceborne infrared payloads for Earth observation: SLSTRs for Copernicus Sentinel 3 and PRISMA hyperspectral camera for PRISMA satellite","volume":"59","author":"Coppo","year":"2020","journal-title":"Appl. Opt."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"112499","DOI":"10.1016\/j.rse.2021.112499","article-title":"The PRISMA imaging spectroscopy mission: Overview and first performance analysis","volume":"262","author":"Cogliati","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Pignatti, S., Amodeo, A., Carfora, M.F., Casa, R., Mona, L., Palombo, A., Pascucci, S., Rosoldi, M., Santini, F., and Laneve, G. (2022). PRISMA L1 and L2 Performances within the PRISCAV Project: The Pignola Test Site in Southern Italy. Remote Sens., 14.","DOI":"10.3390\/rs14091985"},{"key":"ref_45","unstructured":"Rogass, C., Guanter, L., Mielke, C., Scheffler, D., Boesche, N.K., Lubitz, C., Brell, M., Spengler, D., and Segl, K. (2014, January 16\u201320). An automated processing chain for the retrieval of georeferenced reflectance data from hyperspectral EO-1 HYPERION acquisitions. Proceedings of the 34th EARSeL Symposium 2014, Warsaw, Poland."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Milewski, R., Chabrillat, S., and Behling, R. (2017). Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data. Remote Sens., 9.","DOI":"10.3390\/rs9020170"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1080\/01431160802438555","article-title":"On the application of the MODTRAN4 atmospheric radiative transfer code to optical remote sensing","volume":"30","author":"Guanter","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"11249","DOI":"10.3390\/rs70911249","article-title":"The EnMAP-Box\u2014A Toolbox and Application Programming Interface for EnMAP Data Processing","volume":"7","author":"Rabe","year":"2015","journal-title":"Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"11594","DOI":"10.1364\/OE.17.011594","article-title":"Scene-based spectral calibration assessment of high spectral resolution imaging spectrometers","volume":"17","author":"Guanter","year":"2009","journal-title":"Opt. Express"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1016\/j.trac.2009.07.007","article-title":"Review of the most common pre-processing techniques for near-infrared spectra","volume":"28","author":"Rinnan","year":"2009","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","article-title":"PLS-regression: A basic tool of chemometrics","volume":"58","author":"Wold","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1016\/j.cageo.2005.12.009","article-title":"A conditioned Latin hypercube method for sampling in the presence of ancillary information","volume":"32","author":"Minasny","year":"2006","journal-title":"Comput. Geosci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1080\/05704928.2013.811081","article-title":"The Performance of Visible, Near-, and Mid-Infrared Reflectance Spectroscopy for Prediction of Soil Physical, Chemical, and Biological Properties","volume":"49","author":"Janik","year":"2014","journal-title":"Appl. Spectrosc. Rev."},{"key":"ref_55","unstructured":"Williams, P. (2004). Near-Infrared Technology: Getting the Best out of Light: A Short Course in the Practical Implementation of Near-Infrared Spectroscopy for the User, PDK Projects, Incorporated."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.biosystemseng.2005.05.001","article-title":"AE\u2014Automation and Emerging Technologies Potential for Onsite and Online Analysis of Pig Manure using Visible and Near Infrared Reflectance Spectroscopy","volume":"91","author":"Saeys","year":"2005","journal-title":"Biosyst. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v028.i05","article-title":"Building predictive models in R using the caret package","volume":"28","author":"Kuhn","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_58","unstructured":"Chabrillat, S., Guillaso, S., Rabe, A., Foerster, S., and Guanter, L. (2016). From HYSOMA to ENSOMAP\u2014A New Open Source Tool for Quantitative Soil Properties Mapping Based on Hyperspectral Imagery from Airborne to Spaceborne Applications, EGU."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.ecoleng.2013.10.025","article-title":"Near infrared spectroscopy\u2014A tool for chemical properties and organic matter assessment of afforested mine soils","volume":"62","author":"Pietrzykowski","year":"2014","journal-title":"Ecol. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.still.2015.07.008","article-title":"Do we really need large spectral libraries for local scale SOC assessment with NIR spectroscopy?","volume":"155","author":"Guerrero","year":"2016","journal-title":"Soil Tillage Res."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.geoderma.2017.01.030","article-title":"Estimation of soil organic carbon and total nitrogen in different soil layers using VNIR spectroscopy: Effects of spiking on model applicability","volume":"293","author":"Jiang","year":"2017","journal-title":"Geoderma"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.compag.2018.08.036","article-title":"Robust Vis-NIRS models for rapid assessment of soil organic carbon and nitrogen in Feralsols Haplic soils from different tillage management practices","volume":"153","author":"Sithole","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.geoderma.2017.09.013","article-title":"Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis\u2013NIR spectroscopy","volume":"310","author":"Xu","year":"2018","journal-title":"Geoderma"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Liu, Y., Shi, Z., Zhang, G., Chen, Y., Li, S., Hong, Y., Shi, T., Wang, J., Liu, Y., and Liu, Y. (2018). Application of Spectrally Derived Soil Type as Ancillary Data to Improve the Estimation of Soil Organic Carbon by Using the Chinese Soil Vis-NIR Spectral Library. Remote Sens., 10.","DOI":"10.3390\/rs10111747"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"e5722","DOI":"10.7717\/peerj.5722","article-title":"In search of an optimum sampling algorithm for prediction of soil properties from infrared spectra","volume":"2018","author":"Ng","year":"2018","journal-title":"PeerJ"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.saa.2017.10.052","article-title":"Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters","volume":"191","author":"Poppi","year":"2018","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.geoderma.2011.08.001","article-title":"Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy","volume":"166","author":"Vohland","year":"2011","journal-title":"Geoderma"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3808","DOI":"10.1016\/j.measurement.2013.07.007","article-title":"Measurement of soil properties using visible and short wave-near infrared spectroscopy and multivariate calibration","volume":"46","author":"Xuemei","year":"2013","journal-title":"Meas. J. Int. Meas. Confed."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Jiang, G., Zhou, S., Cui, S., Chen, T., Wang, J., Chen, X., Liao, S., and Zhou, K. (2020). Exploring the Potential of HySpex Hyperspectral Imagery for Extraction of Copper Content. Sensors, 20.","DOI":"10.3390\/s20216325"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Hong, Y., Chen, Y., Yu, L., Liu, Y., Liu, Y., Zhang, Y., Liu, Y., and Cheng, H. (2018). Combining fractional order derivative and spectral variable selection for organic matter estimation of homogeneous soil samples by VIS-NIR spectroscopy. Remote Sens., 10.","DOI":"10.3390\/rs10030479"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.geoderma.2009.11.032","article-title":"Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy","volume":"158","author":"Stevens","year":"2010","journal-title":"Geoderma"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Mzid, N., Castaldi, F., Tolomio, M., Pascucci, S., Casa, R., and Pignatti, S. (2022). Evaluation of Agricultural Bare Soil Properties Retrieval from Landsat 8, Sentinel-2 and PRISMA Satellite Data. Remote Sens., 14.","DOI":"10.3390\/rs14030714"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.geoderma.2008.06.011","article-title":"Soil organic carbon prediction by hyperspectral remote sensing and fi eld vis-NIR spectroscopy: An Australian case study","volume":"146","author":"Gomez","year":"2008","journal-title":"Geoderma"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.catena.2016.05.023","article-title":"Spatial variability of soil organic matter using remote sensing data","volume":"145","author":"Mirzaee","year":"2016","journal-title":"Catena"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2019.01.006","article-title":"Sentinel-2 image capacities to predict common topsoil properties of temperate and Mediterranean agroecosystems","volume":"223","author":"Vaudour","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.1007\/s11629-019-5789-9","article-title":"Carbonates and organic matter in soils characterized by reflected energy from 350\u201325000 nm wavelength","volume":"17","author":"Asgari","year":"2020","journal-title":"J. Mt. Sci."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"(2014). Massimo Conforti; Gabriele Buttafuoco Vis-NIR Spectroscopy for Determining Physical and Chemical Soil Properties: An Application to an Area of Southern Italy. Glob. J. Agric. Innov. Res. Dev., 1, 17\u201326.","DOI":"10.15377\/2409-9813.2014.01.01.3"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.1080\/00103624.2020.1820027","article-title":"Visible and Near-Infrared Reflectance Spectroscopy for Assessment of Soil Properties in the Caucasus Mountains, Azerbaijan","volume":"51","author":"Mammadov","year":"2020","journal-title":"Commun. Soil Sci. Plant Anal."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2502","DOI":"10.1080\/01431161.2020.1854892","article-title":"Prediction of soil calcium carbonate with soil visible-near-infrared reflection (Vis-NIR) spectral in Shaanxi province, China: Soil groups vs. spectral groups","volume":"42","author":"Qi","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.geoderma.2008.09.016","article-title":"Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements","volume":"148","author":"Gomez","year":"2008","journal-title":"Geoderma"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"2611","DOI":"10.1007\/s12524-021-01415-5","article-title":"Spatial Prediction of Calcium Carbonate and Clay Content in Soils using Airborne Hyperspectral Data","volume":"49","author":"Mitran","year":"2021","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Castaldi, F. (2021). Sentinel-2 and landsat-8 multi-temporal series to estimate topsoil properties on croplands. Remote Sens., 13.","DOI":"10.3390\/rs13173345"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1029\/JB092iB02p01429","article-title":"Spectral reflectance of carbonate minerals in the visible and near infrared (0.35\u20132.55 um). Anhydrous carbonate minerals","volume":"92","author":"Gaffey","year":"1987","journal-title":"J. Geophys. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1106\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:39:55Z","timestamp":1760121595000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,17]]},"references-count":83,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15041106"],"URL":"https:\/\/doi.org\/10.3390\/rs15041106","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,17]]}}}