{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T15:23:41Z","timestamp":1787757821126,"version":"build-2784847793"},"reference-count":51,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T00:00:00Z","timestamp":1694649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009882","name":"PIGNOLETTO-Call HUB Ricerca e Innovazione CUP","doi-asserted-by":"publisher","award":["E41B20000050007"],"award-info":[{"award-number":["E41B20000050007"]}],"id":[{"id":"10.13039\/501100009882","id-type":"DOI","asserted-by":"publisher"}]},{"name":"European Regional Development Fund","award":["E41B20000050007"],"award-info":[{"award-number":["E41B20000050007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, different machine learning methodologies have been evaluated for the estimation of the multiple soil characteristics of a continental-wide area corresponding to the European region, using multispectral Sentinel-3 satellite imagery and digital elevation model (DEM) derivatives. The results confirm the importance of multispectral imagery in the estimation of soil properties and specifically show that the use of DEM derivatives improves the quality of the estimates, in terms of R2, by about 19% on average. In particular, the estimation of soil texture increases by about 43%, and that of cation exchange capacity (CEC) by about 65%. The importance of each input source (multispectral and DEM) in predicting the soil properties using machine learning has been traced back. It has been found that, overall, the use of multispectral features is more important than the use of DEM derivatives with a ration, on average, of 60% versus 40%.<\/jats:p>","DOI":"10.3390\/s23187876","type":"journal-article","created":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T10:09:22Z","timestamp":1694686162000},"page":"7876","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Estimation of Soil Characteristics from Multispectral Sentinel-3 Imagery and DEM Derivatives Using Machine Learning"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7432-4284","authenticated-orcid":false,"given":"Flavio","family":"Piccoli","sequence":"first","affiliation":[{"name":"Department of Informatics, Systems and Communications, Universit\u00e0 degli Studi di Milano-Bicocca, 20126 Milano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3967-8957","authenticated-orcid":false,"given":"Mirko Paolo","family":"Barbato","sequence":"additional","affiliation":[{"name":"Department of Informatics, Systems and Communications, Universit\u00e0 degli Studi di Milano-Bicocca, 20126 Milano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1319-3924","authenticated-orcid":false,"given":"Marco","family":"Peracchi","sequence":"additional","affiliation":[{"name":"Department of Informatics, Systems and Communications, Universit\u00e0 degli Studi di Milano-Bicocca, 20126 Milano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9112-0574","authenticated-orcid":false,"given":"Paolo","family":"Napoletano","sequence":"additional","affiliation":[{"name":"Department of Informatics, Systems and Communications, Universit\u00e0 degli Studi di Milano-Bicocca, 20126 Milano, Italy"},{"name":"Istituto Nazionale di Fisica Nucleare, Sezione di Milano Bicocca, Piazza della Scienza 3, 20126 Milano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1007\/s11119-008-9061-5","article-title":"An on-farm approach to quantify yield variation and to derive decision rules for site-specific weed management","volume":"9","author":"Ritter","year":"2008","journal-title":"Precis. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/0167-8809(92)90081-L","article-title":"A review: Long-term effects of agricultural systems on soil biochemical and microbial parameters","volume":"40","author":"Dick","year":"1992","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","unstructured":"T\u00f3th, G., Jones, A., and Montanarella, L. (2013). The LUCAS topsoil database and derived information on the regional variability of cropland topsoil properties in the European Union. Environ. Monit. Assess., 185.","DOI":"10.1007\/s10661-013-3109-3"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, D., and Shang, Y. (2014, January 6\u201311). A new active labeling method for deep learning. Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China.","DOI":"10.1109\/IJCNN.2014.6889457"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"731","DOI":"10.13031\/2013.5370","article-title":"Multispectral data for mapping soil texture: Possibilities and limitations","volume":"16","author":"Barnes","year":"2000","journal-title":"Appl. Eng. Agric."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Helfer, G.A., Barbosa, J.L.V., Alves, D., da Costa, A.B., Beko, M., and Leithardt, V.R.Q. (2021). Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction Technology. J. Sens. Actuator Netw., 10.","DOI":"10.20944\/preprints202105.0630.v1"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105433","DOI":"10.1016\/j.cageo.2023.105433","article-title":"A deep scalable neural architecture for soil properties estimation from spectral information","volume":"180","author":"Piccoli","year":"2023","journal-title":"Comput. Geosci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2010.12.018","article-title":"The use of remote sensing in soil and terrain mapping\u2014A review","volume":"162","author":"Mulder","year":"2011","journal-title":"Geoderma"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.isprsjprs.2023.05.032","article-title":"A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities","volume":"202","author":"Han","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","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":"2009","journal-title":"Precis. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Forkuor, G., Hounkpatin, O.K.L., Welp, G., and Thiel, M. (2017). High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0170478"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Safanelli, J., Chabrillat, S., Ben-Dor, E., and Dematt\u00ea, J. (2020). Multispectral Models from Bare Soil Composites for Mapping Topsoil Properties over Europe. Remote Sens., 12.","DOI":"10.3390\/rs12091369"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"142661","DOI":"10.1016\/j.scitotenv.2020.142661","article-title":"Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images","volume":"755","author":"Zhou","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.landusepol.2011.07.003","article-title":"European Soil Data Centre: Response to European policy support and public data requirements","volume":"29","author":"Panagos","year":"2012","journal-title":"Land Use Policy"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hu, J., Peng, J., Zhou, Y., Xu, D., Zhao, R., Jiang, Q., Fu, T., Wang, F., and Shi, Z. (2019). Quantitative estimation of soil salinity using UAV-borne hyperspectral and satellite multispectral images. Remote Sens., 11.","DOI":"10.3390\/rs11070736"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.geoderma.2018.09.003","article-title":"Prediction of soil organic carbon stock by laboratory spectral data and airborne hyperspectral images","volume":"337","author":"Guo","year":"2019","journal-title":"Geoderma"},{"key":"ref_19","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_20","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_21","unstructured":"Vapnik, V. (2013). The Nature of Statistical Learning Theory, Springer Science & Business Media."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4208","DOI":"10.3390\/s21124208","article-title":"Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method","volume":"21","author":"Chambers","year":"2021","journal-title":"Sensors"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, R., Yin, B., Cong, Y., and Du, Z. (2020). Simultaneous prediction of soil properties using multi_cnn model. Sensors, 20.","DOI":"10.3390\/s20216271"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Menon, S.V., and Seelamantula, C.S. (2014, January 20\u201323). Robust Savitzky-Golay filters. Proceedings of the 2014 19th International Conference on Digital Signal Processing, Hong Kong, China.","DOI":"10.1109\/ICDSP.2014.6900752"},{"key":"ref_25","first-page":"145","article-title":"Adaptive Savitzky-Golay filtering and its applications","volume":"16","author":"Dombi","year":"2020","journal-title":"Int. J. Adv. Intell. Paradig."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Heil, K., and Schmidhalter, U. (2021). An Evaluation of Different NIR-Spectral Pre-Treatments to Derive the Soil Parameters C and N of a Humus-Clay-Rich Soil. Sensors, 21.","DOI":"10.3390\/s21041423"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e3111","DOI":"10.1002\/cem.3111","article-title":"Study of the scattering effects on NIR data for the prediction of ash content using EMSC correction factors","volume":"33","author":"Mancini","year":"2019","journal-title":"J. Chemom."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"115695","DOI":"10.1016\/j.geoderma.2022.115695","article-title":"Deep learning-based national scale soil organic carbon mapping with Sentinel-3 data","volume":"411","author":"Odebiri","year":"2022","journal-title":"Geoderma"},{"key":"ref_29","unstructured":"Brady, N.C., Weil, R.R., and Weil, R.R. (2008). The Nature and Properties of Soils, Prentice Hall."},{"key":"ref_30","unstructured":"Campbell, J.B., and Wynne, R.H. (2011). Introduction to Remote Sensing, Guilford Press."},{"key":"ref_31","unstructured":"(2023, August 01). Sentinel-3. Available online: sentinel-3 mission."},{"key":"ref_32","unstructured":"(2023, August 01). Copernicus-DEM. Available online: dem description."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.geoderma.2018.12.037","article-title":"Digital mapping of soil carbon fractions with machine learning","volume":"339","author":"Keskin","year":"2019","journal-title":"Geoderma"},{"key":"ref_34","unstructured":"Lee, T.H., Ullah, A., and Wang, R. (2020). Macroeconomic Forecasting in the Era of Big Data: Theory and Practice, Springer Nature Switzerland AG."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (2019, January 15\u201320). Searching for mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Long Beach, CA, USA.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e1424","DOI":"10.1002\/widm.1424","article-title":"Explainable artificial intelligence: An analytical review","volume":"11","author":"Angelov","year":"2021","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_37","first-page":"431","article-title":"Understanding variable importances in forests of randomized trees","volume":"26","author":"Louppe","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_38","unstructured":"(2023, August 01). Copernicus. Available online: scihub-copernicus."},{"key":"ref_39","unstructured":"(2023, August 01). NetCDF Format. Available online: format-description."},{"key":"ref_40","unstructured":"(2023, August 01). QGIS. Available online: qgis-website."},{"key":"ref_41","unstructured":"(2023, August 01). Semi-Automatic Classification. Available online: qgis-plugin."},{"key":"ref_42","first-page":"1025","article-title":"Image-based atmospheric corrections-revisited and improved","volume":"62","author":"Chavez","year":"1996","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/S0016-7061(00)00081-1","article-title":"Digital elevation model resolution: Effects on terrain attribute calculation and quantitative soil-landscape modeling","volume":"100","author":"Thompson","year":"2001","journal-title":"Geoderma"},{"key":"ref_45","unstructured":"(2023, August 01). SAGA-GIS. Available online: SAGA-GIS-Software."},{"key":"ref_46","unstructured":"Travis, M., Elsner, G., Iverson, W., and Johnson, C. (1975). VIEWIT: Computation of Seen Areas, Slope, and Aspect for Land-Use Planning."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.5194\/gmd-8-1991-2015","article-title":"System for automated geoscientific analyses (SAGA) v. 2.1. 4","volume":"8","author":"Conrad","year":"2015","journal-title":"Geosci. Model Dev."},{"key":"ref_48","first-page":"442","article-title":"A normalized least mean squares algorithm with a step-size scaler against impulsive measurement noise","volume":"60","author":"Song","year":"2013","journal-title":"IEEE Trans. Circ. Syst. II Express Briefs"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.compag.2008.07.008","article-title":"Predict soil texture distributions using an artificial neural network model","volume":"65","author":"Zhao","year":"2009","journal-title":"Comput. Electron. Agric."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.envpol.2007.11.006","article-title":"Base cation depletion, eutrophication and acidification of species-rich grasslands in response to long-term simulated nitrogen deposition","volume":"155","author":"Horswill","year":"2008","journal-title":"Environ. Pollut."},{"key":"ref_51","unstructured":"Allen, D. (1975). Compaction of Coarse-Grained Sediments, I, Elsevier."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7876\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:50:43Z","timestamp":1760129443000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7876"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,14]]},"references-count":51,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23187876"],"URL":"https:\/\/doi.org\/10.3390\/s23187876","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,14]]}}}