{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:01:42Z","timestamp":1781712102019,"version":"3.54.5"},"reference-count":115,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T00:00:00Z","timestamp":1635984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Space Research Institute","award":["31421\/20"],"award-info":[{"award-number":["31421\/20"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019\u20132021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.<\/jats:p>","DOI":"10.3390\/rs13214439","type":"journal-article","created":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T22:25:54Z","timestamp":1636064754000},"page":"4439","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Earth Observation Data-Driven Cropland Soil Monitoring: A Review"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1502-3219","authenticated-orcid":false,"given":"Nikolaos","family":"Tziolas","sequence":"first","affiliation":[{"name":"School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, 54123 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikolaos","family":"Tsakiridis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, 54123 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, Remote Sensing and Geoinformatics, Telegrafenberg, 14473 Potsdam, Germany"},{"name":"Institute of Soil Science, Leibniz University Hannover, Herrenh\u00e4user Stra\u00dfe 2, 30419 Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5328-0323","authenticated-orcid":false,"given":"Jos\u00e9 A. M.","family":"Dematt\u00ea","sequence":"additional","affiliation":[{"name":"Department of Soil Science, Luiz de Queiroz College of Agriculture, University of S\u00e3o Paulo (ESALQ\/USP), Av. P\u00e1dua Dias 11, CP9, Piracicaba 13418-900, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eyal","family":"Ben-Dor","sequence":"additional","affiliation":[{"name":"The Remote Sensing Laboratory, Department of Geography, School of Earth Science, Tel-Aviv University, Tel Aviv-Yafo 39040, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4419-5463","authenticated-orcid":false,"given":"Asa","family":"Gholizadeh","sequence":"additional","affiliation":[{"name":"Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamycka 129, 16500, Prague, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Zalidis","sequence":"additional","affiliation":[{"name":"School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, 54123 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4007-0241","authenticated-orcid":false,"given":"Bas","family":"van Wesemael","sequence":"additional","affiliation":[{"name":"Georges Lema\u00eetre Centre for Earth and Climate Research, Earth and Life Institute, Universit\u00e9 Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"79","DOI":"10.5194\/soil-2-79-2016","article-title":"World\u2019s soils are under threat","volume":"2","author":"Montanarella","year":"2016","journal-title":"SOIL"},{"key":"ref_2","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_3","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_4","doi-asserted-by":"crossref","unstructured":"Yao, X., Li, G., Xia, J., Ben, J., Cao, Q., Zhao, L., Ma, Y., Zhang, L., and Zhu, D. (2020). Enabling the big earth observation data via cloud computing and DGGS: Opportunities and challenges. Remote Sens., 12.","DOI":"10.3390\/rs12010062"},{"key":"ref_5","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_6","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_7","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_8","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1136\/bmj.b2535","article-title":"Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement","volume":"339","author":"Moher","year":"2009","journal-title":"BMJ"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mabkhot, M.M., Ferreira, P., Maffei, A., Podr\u017eaj, P., M\u0105dziel, M., Antonelli, D., Lanzetta, M., Barata, J., Boffa, E., and Fin\u017egar, M. (2021). Mapping industry 4.0 enabling technologies into united nations sustainability development goals. Sustainability, 13.","DOI":"10.3390\/su13052560"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kavvada, A., Metternicht, G., Kerblat, F., Mudau, N., Haldorson, M., Laldaparsad, S., Friedl, L., Held, A., and Chuvieco, E. (2020). Towards delivering on the sustainable development goals using earth observations. Remote Sens. Environ., 247.","DOI":"10.1016\/j.rse.2020.111930"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"135","DOI":"10.5194\/soil-2-135-2016","article-title":"Facing policy challenges with inter- and transdisciplinary soil research focused on the un Sustainable Development Goals","volume":"2","author":"Bouma","year":"2016","journal-title":"SOIL"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.coesh.2018.04.008","article-title":"Soil Thematic Strategy: An important contribution to policy support, research, data development and raising the awareness","volume":"5","author":"Panagos","year":"2018","journal-title":"Curr. Opin. Environ. Sci. Health"},{"key":"ref_13","unstructured":"Panagos, P., Ballabio, C., Scarpa, S., Borelli, P., Lugato, E., and Montanarella, L. (2020). Soil Related Indicators to Support Agro-Environmental Policies, Publications Office of the European Union."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1007\/s11119-015-9414-9","article-title":"Variable rate nitrogen fertilizer response in wheat using remote sensing","volume":"17","author":"Basso","year":"2016","journal-title":"Precis. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1134\/S1064229321020137","article-title":"Detecting Degraded Arable Land on the Basis of Remote Sensing Big Data Analysis","volume":"54","author":"Rukhovich","year":"2021","journal-title":"Eurasian Soil Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.envsci.2017.04.009","article-title":"Motivations and barriers for Western Australian broad-acre farmers to adopt carbon farming","volume":"73","author":"Kragt","year":"2017","journal-title":"Environ. Sci. Policy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1515\/auto-2020-0042","article-title":"Soil monitoring for precision farming using hyperspectral remote sensing and soil sensors","volume":"69","author":"Schreiner","year":"2021","journal-title":"At. Automatisierungstechnik"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"138994","DOI":"10.1016\/j.scitotenv.2020.138994","article-title":"Tracking changes in soil organic carbon across the heterogeneous agricultural landscape of the Lower Fraser Valley of British Columbia","volume":"732","author":"Paul","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hagen, S., Delgado, G., Ingraham, P., Cooke, I., Emery, R., Fisk, J.P., Melendy, L., Olson, T., Patti, S., and Rubin, N. (2020). Mapping Conservation Management Practices and Outcomes in the Corn Belt Using the Operational Tillage Information System (OpTIS) and the Denitrification\u2013Decomposition (DNDC) Model. Land, 9.","DOI":"10.3390\/land9110408"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1111\/gcb.14815","article-title":"How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal","volume":"26","author":"Smith","year":"2019","journal-title":"Glob. Chang. Biol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"115118","DOI":"10.1016\/j.geoderma.2021.115118","article-title":"Mapping soil organic carbon stock by hyperspectral and time-series multispectral remote sensing images in low-relief agricultural areas","volume":"398","author":"Guo","year":"2021","journal-title":"Geoderma"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nanni, M., Dematt\u00ea, J., Rodrigues, M., Santos, G., Reis, A., Oliveira, K., Cezar, E., Furlanetto, R., Crusiol, L., and Sun, L. (2021). Mapping Particle Size and Soil Organic Matter in Tropical Soil Based on Hyperspectral Imaging and Non-Imaging Sensors. Remote Sens., 13.","DOI":"10.3390\/rs13091782"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"120987","DOI":"10.1016\/j.jhazmat.2019.120987","article-title":"Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest","volume":"382","author":"Tan","year":"2019","journal-title":"J. Hazard. Mater."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"123288","DOI":"10.1016\/j.jhazmat.2020.123288","article-title":"Estimating the distribution trend of soil heavy metals in mining area from HyMap airborne hyperspectral imagery based on ensemble learning","volume":"401","author":"Tan","year":"2020","journal-title":"J. Hazard. Mater."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"116281","DOI":"10.1016\/j.envpol.2020.116281","article-title":"Mapping soil pollution by using drone image recognition and machine learning at an arsenic-contaminated agricultural field","volume":"270","author":"Jia","year":"2020","journal-title":"Environ. Pollut."},{"key":"ref_26","first-page":"102420","article-title":"Predicting the abundance of copper in soil using reflectance spectroscopy and GF5 hyperspectral imagery","volume":"102","author":"Yin","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hasab, H.A., Dibs, H., Dawood, A.S., Hadi, W.H., Hussain, H.M., and Al-Ansari, N. (2020). Monitoring and Assessment of Salinity and Chemicals in Agricultural Lands by a Remote Sensing Technique and Soil Moisture with Chemical Index Models. Geosciences, 10.","DOI":"10.3390\/geosciences10060207"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"115316","DOI":"10.1016\/j.geoderma.2021.115316","article-title":"Predictive soil mapping using historic bare soil composite imagery and legacy soil survey data","volume":"401","author":"Sorenson","year":"2021","journal-title":"Geoderma"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Safanelli, J.L., Chabrillat, S., Ben-Dor, E., and Dematt\u00ea, J.A.M. (2020). Multispectral Models from Bare Soil Composites for Mapping Topsoil Properties over Europe. Remote Sens., 12.","DOI":"10.3390\/rs12091369"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"114233","DOI":"10.1016\/j.geoderma.2020.114233","article-title":"Investigation of the spatial and temporal variation of soil salinity using random forests in the central desert of Iran","volume":"365","author":"Fathizad","year":"2020","journal-title":"Geoderma"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"136092","DOI":"10.1016\/j.scitotenv.2019.136092","article-title":"Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI","volume":"707","author":"Wang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1021\/ci034160g","article-title":"Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling","volume":"43","author":"Svetnik","year":"2003","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"35","DOI":"10.5194\/soil-6-35-2020","article-title":"Machine learning and soil sciences: A review aided by machine learning tools","volume":"6","author":"Padarian","year":"2020","journal-title":"SOIL"},{"key":"ref_35","first-page":"102111","article-title":"Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data","volume":"89","author":"Meng","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Veres, M., Lacey, G., and Taylor, G.W. (2015, January 3\u20135). Deep Learning Architectures for Soil Property Prediction. Proceedings of the 2015 12th Conference on Computer and Robot Vision (CRV), Halifax, NS, Canada.","DOI":"10.1109\/CRV.2015.15"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, L., Ji, M., and Buchroithner, M. (2018). Transfer Learning for Soil Spectroscopy Based on Convolutional Neural Networks and Its Application in Soil Clay Content Mapping Using Hyperspectral Imagery. Sensors, 18.","DOI":"10.3390\/s18093169"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1111\/ejss.13071","article-title":"Perspectives on data-driven soil research","volume":"72","author":"Wadoux","year":"2020","journal-title":"Eur. J. Soil Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"115042","DOI":"10.1016\/j.geoderma.2021.115042","article-title":"Leveraging the application of Earth observation data for mapping cropland soils in Brazil","volume":"396","author":"Safanelli","year":"2021","journal-title":"Geoderma"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Arrieta","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Diek, S., Fornallaz, F., Schaepman, M.E., and De Jong, R. (2017). Barest Pixel Composite for Agricultural Areas Using Landsat Time Series. Remote Sens., 9.","DOI":"10.3390\/rs9121245"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.11.004","article-title":"Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984\u20132014)","volume":"205","author":"Rogge","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zepp, S., Heiden, U., Bachmann, M., Wiesmeier, M., Steininger, M., and van Wesemael, B. (2021). Estimation of Soil Organic Carbon Contents in Croplands of Bavaria from SCMaP Soil Reflectance Composites. Remote Sens., 13.","DOI":"10.3390\/rs13163141"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Loiseau, T., Chen, S., Mulder, V., Dobarco, M.R., Richer-De-Forges, A., Lehmann, S., Bourennane, H., Saby, N., Martin, M., and Vaudour, E. (2019). Satellite data integration for soil clay content modelling at a national scale. Int. J. Appl. Earth Obs. Geoinf., 82.","DOI":"10.1016\/j.jag.2019.101905"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"138244","DOI":"10.1016\/j.scitotenv.2020.138244","article-title":"High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms","volume":"729","author":"Zhou","year":"2020","journal-title":"Sci. Total. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"114018","DOI":"10.1016\/j.geoderma.2019.114018","article-title":"Multi-temporal bare surface image associated with transfer functions to support soil classification and mapping in southeastern Brazil","volume":"361","author":"Rizzo","year":"2019","journal-title":"Geoderma"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e00253","DOI":"10.1016\/j.geodrs.2020.e00253","article-title":"Using Landsat and soil clay content to map soil organic carbon of oxisols and Ultisols near S\u00e3o Paulo, Brazil","volume":"21","author":"Padilha","year":"2020","journal-title":"Geoderma Reg."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e00389","DOI":"10.1016\/j.geodrs.2021.e00389","article-title":"Mapping of tank silt application using Sentinel-2 images over the Berambadi catchment (India)","volume":"25","author":"Gomez","year":"2021","journal-title":"Geoderma Reg."},{"key":"ref_49","first-page":"294","article-title":"Generating soil salinity, soil moisture, soil pH from satellite imagery and its analysis","volume":"7","author":"Ghazali","year":"2020","journal-title":"Inf. Process. Agric."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhang, X., Wu, W., and Liu, H. (2021). Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1\/-2 Data in a Small Watershed. Remote Sens., 13.","DOI":"10.3390\/rs13071229"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"112117","DOI":"10.1016\/j.rse.2020.112117","article-title":"Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison","volume":"252","author":"Silvero","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_52","unstructured":"Planet Team (2017). Planet Team. Planet Application Program Interface. Space for Life on Earth, Planet Team."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"115116","DOI":"10.1016\/j.geoderma.2021.115116","article-title":"Clay content prediction using spectra data collected from the ground to space platforms in a smallholder tropical area","volume":"399","author":"Bellinaso","year":"2021","journal-title":"Geoderma"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"\u017d\u00ed\u017eala, D., Mina\u0159\u00edk, R., and Z\u00e1dorov\u00e1, T. (2019). Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions. Remote Sens., 11.","DOI":"10.3390\/rs11242947"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"104477","DOI":"10.1016\/j.still.2019.104477","article-title":"Mapping field-scale soil organic carbon with unmanned aircraft system-acquired time series multispectral images","volume":"196","author":"Guo","year":"2020","journal-title":"Soil Tillage Res."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Biney, J., Saberioon, M., Bor\u016fvka, L., Hou\u0161ka, J., Va\u0161\u00e1t, R., Agyeman, P.C., Coblinski, J., and Klement, A. (2021). Exploring the Suitability of UAS-Based Multispectral Images for Estimating Soil Organic Carbon: Comparison with Proximal Soil Sensing and Spaceborne Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13020308"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1016\/j.scitotenv.2019.02.125","article-title":"Current and emerging methodologies for estimating carbon sequestration in agricultural soils: A review","volume":"665","author":"Nayak","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Transon, J., D\u2019Andrimont, R., Maugnard, A., and Defourny, P. (2018). Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context. Remote Sens., 10.","DOI":"10.3390\/rs10020157"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"111793","DOI":"10.1016\/j.rse.2020.111793","article-title":"An integrated methodology using open soil spectral libraries and Earth Observation data for soil organic carbon estimations in support of soil-related SDGs","volume":"244","author":"Tziolas","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ward, K., 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_61","doi-asserted-by":"crossref","unstructured":"Hong, Y., Guo, L., Chen, S., Linderman, M., Mouazen, A.M., Yu, L., Chen, Y., Liu, Y., Liu, Y., and Cheng, H. (2020). Exploring the potential of airborne hyperspectral image for estimating topsoil organic carbon: Effects of fractional-order derivative and optimal band combination algorithm. Geoderma, 365.","DOI":"10.1016\/j.geoderma.2020.114228"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"104589","DOI":"10.1016\/j.still.2020.104589","article-title":"Comparing laboratory and airborne hyperspectral data for the estimation and mapping of topsoil organic carbon: Feature selection coupled with random forest","volume":"199","author":"Hong","year":"2020","journal-title":"Soil Tillage Res."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Meng, X., Bao, Y., Ye, Q., Liu, H., Zhang, X., Tang, H., and Zhang, X. (2021). Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method. Remote Sens., 13.","DOI":"10.3390\/rs13122273"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Loizzo, R., Guarini, R., Longo, F., Scopa, T., Formaro, R., Facchinetti, C., and Varacalli, G. (2018, January 22\u201327). Prisma: The Italian hyperspectral mission. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518512"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Krutz, D., Venus, H., Eckardt, A., Walter, I., Sebastian, I., Reulke, R., G\u00fcnther, B., Zender, B., Arloth, S., and Williges, C. (2018, January 11\u201312). DESIS -DLR Earth Sensing Imaging Spectrometer. Proceedings of the SPIE Remote Sensing, Berlin, Germany.","DOI":"10.1007\/978-3-319-92753-4_28"},{"key":"ref_66","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_67","first-page":"102277","article-title":"Temporal mosaicking approaches of Sentinel-2 images for extending topsoil organic carbon content mapping in croplands","volume":"96","author":"Vaudour","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Vaudour, E., Gomez, C., Loiseau, T., Baghdadi, N., Loubet, B., Arrouays, D., Ali, L., and Lagacherie, P. (2019). The Impact of Acquisition Date on the Prediction Performance of Topsoil Organic Carbon from Sentinel-2 for Croplands. Remote Sens., 11.","DOI":"10.3390\/rs11182143"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1690","DOI":"10.1111\/ejss.13086","article-title":"Synergistic use of hyperspectral imagery, Sentinel-1 and LiDAR improves mapping of soil physical and geochemical properties at the farm-scale","volume":"72","author":"Zhang","year":"2021","journal-title":"Eur. J. Soil Sci."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Li, X., Ding, J., Liu, J., Ge, X., and Zhang, J. (2021). Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang. Remote Sens., 13.","DOI":"10.3390\/rs13040769"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"217","DOI":"10.5194\/soil-7-217-2021","article-title":"SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty","volume":"7","author":"Poggio","year":"2021","journal-title":"SOIL"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"6130","DOI":"10.1038\/s41598-021-85639-y","article-title":"African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning","volume":"11","author":"Hengl","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"137703","DOI":"10.1016\/j.scitotenv.2020.137703","article-title":"Improved digital soil mapping with multitemporal remotely sensed satellite data fusion: A case study in Iran","volume":"721","author":"Fathololoumi","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.geoderma.2015.07.006","article-title":"Mapping topsoil physical properties at European scale using the LUCAS database","volume":"261","author":"Ballabio","year":"2016","journal-title":"Geoderma"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.catena.2018.01.015","article-title":"Updating a national soil classification with spectroscopic predictions and digital soil mapping","volume":"164","author":"Teng","year":"2018","journal-title":"CATENA"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Dvorakova, K., Heiden, U., and van Wesemael, B. (2021). Sentinel-2 Exposed Soil Composite for Soil Organic Carbon Prediction. Remote Sens., 13.","DOI":"10.3390\/rs13091791"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1007\/s12517-021-06698-z","article-title":"Soil total carbon mapping, in Djerid Arid area, using ASTER multispectral remote sensing data combined with laboratory spectral proximal sensing data","volume":"14","author":"Aichi","year":"2021","journal-title":"Arab. J. Geosci."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.isprsjprs.2021.06.015","article-title":"The influence of vegetation index thresholding on EO-based assessments of exposed soil masks in Germany between 1984 and 2019","volume":"178","author":"Zepp","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_79","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_80","doi-asserted-by":"crossref","unstructured":"Castaldi, F., Chabrillat, S., Don, A., and Van Wesemael, B. (2019). Soil Organic Carbon Mapping Using LUCAS Topsoil Database and Sentinel-2 Data: An Approach to Reduce Soil Moisture and Crop Residue Effects. Remote Sens., 11.","DOI":"10.3390\/rs11182121"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Wang, K., Qi, Y., Guo, W., Zhang, J., and Chang, Q. (2021). Retrieval and Mapping of Soil Organic Carbon Using Sentinel-2A Spectral Images from Bare Cropland in Autumn. Remote Sens., 13.","DOI":"10.3390\/rs13061072"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Prudnikova, E., and Savin, I. (2021). Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data. Remote Sens., 13.","DOI":"10.3390\/rs13122313"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/j.geoderma.2018.09.052","article-title":"Minimizing soil moisture variations in multi-temporal airborne imaging spectrometer data for digital soil mapping","volume":"337","author":"Diek","year":"2018","journal-title":"Geoderma"},{"key":"ref_84","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_85","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2019.09.016","article-title":"Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture","volume":"158","author":"Ienco","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2019.01.011","article-title":"DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn","volume":"149","author":"Interdonato","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_87","unstructured":"Deudon, M., Kalaitzis, A., Goytom, I., Arefin, M.R., Lin, Z., Sankaran, K., Michalski, V., Kahou, S.E., Cornebise, J., and Bengio, Y. (2020). HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery. arxiv."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"107912","DOI":"10.1109\/ACCESS.2020.3000174","article-title":"Spectra-GANs: A New Automated Denoising Method for Low-S\/N Stellar Spectra","volume":"8","author":"Wu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1038\/s41551-021-00751-8","article-title":"Synthetic data in machine learning for medicine and healthcare","volume":"5","author":"Chen","year":"2021","journal-title":"Nat. Biomed. Eng."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"114967","DOI":"10.1016\/j.geoderma.2021.114967","article-title":"Using autoencoders to compress soil VNIR\u2013SWIR spectra for more robust prediction of soil properties","volume":"393","author":"Tsimpouris","year":"2021","journal-title":"Geoderma"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Taghizadeh-Mehrjardi, R., Emadi, M., Cherati, A., Heung, B., Mosavi, A., and Scholten, T. (2021). Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions. Remote Sens., 13.","DOI":"10.3390\/rs13051025"},{"key":"ref_93","unstructured":"Gargiulo, M. (August, January 28). Advances on CNN-Based Super-Resolution of Sentinel-2 Images. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1038\/s41598-020-80486-9","article-title":"Automated spectroscopic modelling with optimised convolutional neural networks","volume":"11","author":"Shen","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Najafi, P., Navid, H., Feizizadeh, B., Eskandari, I., and Blaschke, T. (2019). Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue. Remote Sens., 11.","DOI":"10.3390\/rs11212583"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"89","DOI":"10.5194\/soil-6-89-2020","article-title":"A new model for intra- and inter-institutional soil data sharing","volume":"6","author":"Padarian","year":"2020","journal-title":"SOIL"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.tele.2018.11.006","article-title":"A systematic literature review of blockchain-based applications: Current status, classification and open issues","volume":"36","author":"Casino","year":"2018","journal-title":"Telemat. Inform."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Ajates, R., Hager, G., Georgiadis, P., Coulson, S., Woods, M., and Hemment, D. (2020). Local Action with Global Impact: The Case of the GROW Observatory and the Sustainable Development Goals. Sustainability, 12.","DOI":"10.3390\/su122410518"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.geoderma.2017.02.018","article-title":"Soil color sensor data collection using a GPS-enabled smartphone application","volume":"296","author":"Stiglitz","year":"2017","journal-title":"Geoderma"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"114562","DOI":"10.1016\/j.geoderma.2020.114562","article-title":"Predicting soil texture from smartphone-captured digital images and an application","volume":"376","author":"Swetha","year":"2020","journal-title":"Geoderma"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"114020","DOI":"10.1016\/j.geoderma.2019.114020","article-title":"Predicting soil organic matter from cellular phone images under varying soil moisture","volume":"361","author":"Fu","year":"2019","journal-title":"Geoderma"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1038\/s41378-019-0111-0","article-title":"On-chip parallel Fourier transform spectrometer for broadband selective infrared spectral sensing","volume":"6","author":"Fathy","year":"2020","journal-title":"Microsyst. Nanoeng."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/bs.agron.2021.02.001","article-title":"Current sensor technologies for in situ and on-line measurement of soil nitrogen for variable rate fertilization: A review","volume":"168","author":"Guerrero","year":"2021","journal-title":"Adv. Agron."},{"key":"ref_105","first-page":"422","article-title":"Performance comparison between a miniaturized and a conventional near infrared reflectance (NIR) spectrometer for characterizing soil carbon and nitrogen","volume":"338","author":"Kouakoua","year":"2018","journal-title":"Geoderma"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Karyotis, K., Angelopoulou, T., Tziolas, N., Palaiologou, E., Samarinas, N., and Zalidis, G. (2021). Evaluation of a Micro-Electro Mechanical Systems Spectral Sensor for Soil Properties Estimation. Land, 10.","DOI":"10.3390\/land10010063"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"e00240","DOI":"10.1016\/j.geodrs.2019.e00240","article-title":"Evaluating low-cost portable near infrared sensors for rapid analysis of soils from South Eastern Australia","volume":"20","author":"Tang","year":"2019","journal-title":"Geoderma Reg."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.compag.2018.08.039","article-title":"Soil sampling with drones and augmented reality in precision agriculture","volume":"154","author":"Huuskonen","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"105326","DOI":"10.1016\/j.landusepol.2021.105326","article-title":"Citizen science for sustainable agriculture\u2014A systematic literature review","volume":"103","author":"Ebitu","year":"2021","journal-title":"Land Use Policy"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"111968","DOI":"10.1016\/j.rse.2020.111968","article-title":"Landsat 9: Empowering open science and applications through continuity","volume":"248","author":"Masek","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_111","unstructured":"Nieke, J., and Rast, M. (August, January 28). Status: Copernicus Hyperspectral Imaging Mission for the Environment (CHIME). Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Green, R.O. (2018, January 22\u201327). Global VSWIR imaging spectroscopy and the 2017 decadal survey: Robert O. Green and the imaging spectroscopy community. In Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518744"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Killough, B. (2018, January 22\u201327). Overview of the open data cube initiative. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517694"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_115","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4439\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:25:50Z","timestamp":1760167550000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4439"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,4]]},"references-count":115,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13214439"],"URL":"https:\/\/doi.org\/10.3390\/rs13214439","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,4]]}}}