{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T15:17:16Z","timestamp":1780413436394,"version":"3.54.1"},"reference-count":65,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T00:00:00Z","timestamp":1637020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"This research was funded by European Commission for grant called Sino-EU Soil Observatory for intelligent Land Use Management\u201d (SIEUSOIL)","award":["818346"],"award-info":[{"award-number":["818346"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil contamination by heavy metals is of particular concern, due to the direct negative impact on crop yield, food quality and human health. Although the conventional approach to monitor heavy metals relies on field sampling and lab analysis, the proliferation in the use of portable spectrometers has reduced the cost and time of investigation. However, discrepancies in spectral data from different spectrometers increase the modeling time and undermine the model accuracy for spatial mapping. This study, therefore, took advantage of the readily accessible Landsat 7 data to predict and map the spatiotemporal distribution of ten heavy metals (i.e., Sb, Pb, Ni, Mn, Hg, Cu, Cr, Co, Cd and As) over a 640 km2 area in Belgium. The Land Use\/Cover Area Frame Survey (LUCAS) database of a region in north-eastern Belgium was used to retrieve variation in heavy metals concentrations over time and space, using the Landsat 7 imagery for four single dates in 2009, 2013, 2016 and 2020. Three regression methods, namely, partial least squares regression (PLSR), random forest (RF) and support vector machine (SVM) were used to model and predict the heavy metal concentrations for 2009. By comparing these models unbiasedly, the best model was selected for predicting and mapping the heavy metal distributions for 2013, 2016 and 2020. RF turned out to be the optimal model for 2009 with a coefficient of determination of prediction (R2P) and residual prediction deviation of prediction (RPDP) ranging from 0.62 to 0.92, and 1.23 to 2.79, respectively. The measured heavy metal distributions along the river floodplains, at the highlands and in the lowlands, were generally high, compared to their RF spatiotemporal predictions, which decreased over time. Increasing moisture contents in the floodplains adjacent to the river channels and the lowlands were the primary contributors to the reduction in the satellite reflectance spectra. However, topsoil erosion from rainfall, snowmelt as well as wind into the lowlands could have influenced the reduction in heavy metal spatiotemporal predicted values over time in the highlands. The spatiotemporal prediction maps produced for the heavy metals for the four different years revealed a good spatial similarity and consistency with the measured maps for 2009, which indicates their stability over the years.<\/jats:p>","DOI":"10.3390\/rs13224615","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T02:42:28Z","timestamp":1637116948000},"page":"4615","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Spatiotemporal Prediction and Mapping of Heavy Metals at Regional Scale Using Regression Methods and Landsat 7"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0354-0067","authenticated-orcid":false,"given":"Abdul M.","family":"Mouazen","sequence":"first","affiliation":[{"name":"Department of Environment, Ghent University, Coupure Links 653, 9000 Gent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9784-1636","authenticated-orcid":false,"given":"Felix","family":"Nyarko","sequence":"additional","affiliation":[{"name":"Department of Environment, Ghent University, Coupure Links 653, 9000 Gent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Qaswar","sequence":"additional","affiliation":[{"name":"Department of Environment, Ghent University, Coupure Links 653, 9000 Gent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gergely","family":"T\u00f3th","sequence":"additional","affiliation":[{"name":"Institute of Advanced Studies, 9730 K\u0151szeg, Hungary"},{"name":"Agricultural Research Centre, Institute for Soil Sciences, 1022 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3742-7062","authenticated-orcid":false,"given":"Anne","family":"Gobin","sequence":"additional","affiliation":[{"name":"Remote Sensing Unit, VITO NV, Boeretang 200, 2400 Mol, Belgium"},{"name":"Department of Earth and Environmental Sciences, Faculty of Bioscience Engineering, Katholieke Universiteit Leuven, Celestijnenlaan 200E, 3001 Heverlee, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7270-5307","authenticated-orcid":false,"given":"Dimitrios","family":"Moshou","sequence":"additional","affiliation":[{"name":"Laboratory of Agricultural Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1016\/j.scitotenv.2016.05.115","article-title":"Maps of heavy metals in the soils of the European Union and proposed priority areas for detailed assessment","volume":"565","author":"Hermann","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.scitotenv.2007.01.010","article-title":"Baseline values for heavy metals in agricultural soils in an European Mediterranean region","volume":"378","author":"Peris","year":"2007","journal-title":"Sci. Total Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104","DOI":"10.17221\/36\/2018-SWR","article-title":"Present restrictions of sewage sludge application in agriculture within the European Union","volume":"14","author":"Vymazal","year":"2019","journal-title":"Soil Water Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alengebawy, A., Abdelkhalek, S.T., Qureshi, S.R., and Wang, M.-Q. (2021). Heavy metals and pesticides toxicity in agricultural soil and plants: Ecological risks and human health implications. Toxics, 9.","DOI":"10.3390\/toxics9030042"},{"key":"ref_5","unstructured":"Van Liedekerke, M., Prokop, G., Rabl-Berger, S., Kibblewhite, M., and Louwagie, G. (2014). Progress in Management of Contaminated Sites in Europe, Publications Office of the European Union. JRC Technical Reports."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1016\/j.chemosphere.2014.05.027","article-title":"Evaporation as the transport mechanism of metals in arid regions","volume":"111","author":"Lima","year":"2014","journal-title":"Chemosphere"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s11806-011-0424-0","article-title":"Feasibility of estimating heavy metal contaminations in floodplain soils using laboratory-based hyperspectral data\u2014A case study along Le\u2019an River, China","volume":"14","author":"Liu","year":"2011","journal-title":"Geo-Spatial Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2474","DOI":"10.3390\/rs2112474","article-title":"Visible and infrared remote imaging of hazardous waste: A review","volume":"2","author":"Slonecker","year":"2010","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/S0269-7491(96)00060-7","article-title":"Mapping heavy metals in polluted soil by disjunctive kriging","volume":"94","author":"Webster","year":"1996","journal-title":"Environ. Pollut."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2742","DOI":"10.1021\/es015747j","article-title":"Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy","volume":"36","author":"Kemper","year":"2002","journal-title":"Environ. Sci. Technol."},{"key":"ref_11","unstructured":"Ji, J., Song, Y., Yuan, X., and Yang, Z. (2010, January 1\u20136). Diffuse reflectance spectroscopy study of heavy metals in agricultural soils of the Changjiand River Delta, China. Proceedings of the 19th World Congress of Soil Science, Soil Solutions for a Changing World, Brisbane, Australia."},{"key":"ref_12","first-page":"1","article-title":"Capability of vis-NIR spectroscopy and Landsat 8 spectral data to predict soil heavy metals in polluted agricultural land (Iran)","volume":"9","author":"Fard","year":"2016","journal-title":"Arab. J. Geosci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"335","DOI":"10.5194\/isprs-archives-XLII-3-335-2018","article-title":"Retrieval and mapping of heavy metal concentration in soil using time series landsat 8 imagery","volume":"42","author":"Fang","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch."},{"key":"ref_14","first-page":"407","article-title":"Review of retrieving soil heavy metal content by hyperspectral remote sensing","volume":"30","author":"Junliang","year":"2015","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.geoderma.2017.11.027","article-title":"Prediction of soil parameters using the spectral range between 350 and 15,000 nm: A case study based on the Permanent Soil Monitoring Program in Saxony, Germany","volume":"315","author":"Riedel","year":"2018","journal-title":"Geoderma"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2018.08.010","article-title":"Estimating heavy metal concentrations in suburban soils with reflectance spectroscopy","volume":"336","author":"Cheng","year":"2019","journal-title":"Geoderma"},{"key":"ref_17","first-page":"55","article-title":"Capabilities of remote sensing hyperspectral images for the detection of lead contamination: A review","volume":"1","author":"Owens","year":"2012","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.geoderma.2007.04.023","article-title":"Total carbon mapping in glacial till soils using near-infrared spectroscopy, Landsat imagery and topographical information","volume":"141","author":"Huang","year":"2007","journal-title":"Geoderma"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"16398","DOI":"10.3390\/rs71215841","article-title":"Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data","volume":"7","author":"Ali","year":"2015","journal-title":"Remote Sens."},{"key":"ref_21","unstructured":"T\u00f3th, G., Jones, A., and Montanarella, L. (2013). LUCAS Topsoil Survey: Methodology, Data, and Results, Publications Office. JRC Technical Reports."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2005.03.007","article-title":"Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties","volume":"131","author":"Walvoort","year":"2006","journal-title":"Geoderma"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.catena.2013.03.009","article-title":"The use of reflectance visible-NIR spectroscopy to predict seasonal change of trace metals in suspended solids of Changjiang River","volume":"109","author":"Song","year":"2013","journal-title":"Catena"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1255\/jnirs.614","article-title":"Effect of wavelength range on the measurement accuracy of some selected soil constituents using visual-near infrared spectroscopy","volume":"14","author":"Mouazen","year":"2006","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_25","unstructured":"Martens, H., and Naes, T. (1989). Assessment, validation and choice of calibration method. Multivariate Calibration, John Wiley & Sons."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Efron, B., and Tibshiran, R. (1993). An Introduction to the Bootstrap, Chapman &Hall, Inc.","DOI":"10.1007\/978-1-4899-4541-9"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support vector machines","volume":"20","author":"Vapnik","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.csda.2009.09.023","article-title":"Kernel-based machine learning for fast text mining in R","volume":"54","author":"Karatzoglou","year":"2010","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_29","unstructured":"Meyer, D., and Wien, F.H.T. (2015). Support vector machines. Interface Libsvm Package E1071, FH Technikum Wien."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1137\/1021092","article-title":"Computers and the theory of statistics: Thinking the unthinkable","volume":"21","author":"Efron","year":"1979","journal-title":"SIAM Rev."},{"key":"ref_33","unstructured":"R Foundation for Statistical Computing (2020). R Core Team A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_34","unstructured":"Stevens, A., and Ramirez-Lopez, L. (2020). Package Vignette. An Introduction to the Prospectr Package, University of Liege. R Package Version 0.2.0."},{"key":"ref_35","unstructured":"Wehrens, R. (2021, March 30). pls: Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR). R Package Ver. 2.0-0. Available online: http\/\/mevik.net\/work\/software\/pls.html."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1023\/A:1018996712442","article-title":"Optimal portfolio choice under a liability constraint","volume":"97","author":"Zaremba","year":"2000","journal-title":"Ann. Oper. Res."},{"key":"ref_37","unstructured":"Garcia, H., and Filzmoser, P. (2011). Multivariate Statistical Analysis Using the R Package Chemometrics, Vienna University of Technology."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2721","DOI":"10.5194\/hess-20-2721-2016","article-title":"Ordinary kriging as a tool to estimate historical daily streamflow records","volume":"20","author":"Farmer","year":"2016","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hu, B., Chen, S., Hu, J., Xia, F., Xu, J., Li, Y., and Shi, Z. (2017). Application of portable XRF and VNIR sensors for rapid assessment of soil heavy metal pollution. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0172438"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s11368-014-0937-x","article-title":"Identifying the origins and spatial distributions of heavy metals in soils of Ju country (Eastern China) using multivariate and geostatistical approach","volume":"15","author":"Lv","year":"2015","journal-title":"J. Soils Sediments"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.scitotenv.2018.04.268","article-title":"Copper distribution in European topsoils: An assessment based on LUCAS soil survey","volume":"636","author":"Ballabio","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"144755","DOI":"10.1016\/j.scitotenv.2020.144755","article-title":"A spatial assessment of mercury content in the European Union topsoil","volume":"769","author":"Ballabio","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1023\/A:1025498629671","article-title":"Heavy metal content of arable soils in Northern Belgium","volume":"148","author":"Vanongeval","year":"2003","journal-title":"Water Air Soil Pollut."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.ecoenv.2011.07.004","article-title":"Source identification of eight hazardous heavy metals in agricultural soils of Huizhou, Guangdong Province, China","volume":"78","author":"Cai","year":"2012","journal-title":"Ecotoxicol. Environ. Saf."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1016\/j.chemosphere.2006.03.016","article-title":"Assessing heavy metal sources in agricultural soils of an European Mediterranean area by multivariate analysis","volume":"65","author":"Peris","year":"2006","journal-title":"Chemosphere"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Alloway, B.J. (2012). Heavy Metals in Soils: Trace Metals and Metalloids in Soils and Their Bioavailability, Springer.","DOI":"10.1007\/978-94-007-4470-7"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1016\/j.envpol.2006.01.045","article-title":"Heavy metals contents in agricultural topsoils in the Ebro basin (Spain). Application of the multivariate geoestatistical methods to study spatial variations","volume":"144","author":"Arias","year":"2006","journal-title":"Environ. Pollut."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v015.i09","article-title":"Support vector algorithm in R","volume":"15","author":"Karatzoglou","year":"2006","journal-title":"J. Stat. Softw."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1007\/s10661-019-7510-4","article-title":"Random forest\u2013based estimation of heavy metal concentration in agricultural soils with hyperspectral sensor data","volume":"191","author":"Tan","year":"2019","journal-title":"Environ. Monit. Assess."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/S0169-7439(01)00152-6","article-title":"Personal memories of the early PLS development","volume":"58","author":"Wold","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_51","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_52","doi-asserted-by":"crossref","unstructured":"Wenjun, J., Zhou, S., Jingyi, H., and Shuo, L. (2014). In situ measurement of some soil properties in paddy soil using visible and near-infrared spectroscopy. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0105708"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.rse.2015.12.040","article-title":"A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery","volume":"175","author":"Fu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1081\/AL-200054096","article-title":"Methyl mercury and heavy metal content in soils of rivers Saale and Elbe (Germany)","volume":"38","author":"Devai","year":"2005","journal-title":"Anal. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.scitotenv.2014.03.109","article-title":"Future trends in soil cadmium concentration under current cadmium fluxes to European agricultural soils","volume":"485\u2013486","author":"Six","year":"2014","journal-title":"Sci. Total Environ."},{"key":"ref_56","first-page":"1","article-title":"Long-term changes of heavy metal transboundary pollution of the environment (1990\u20132010)","volume":"2","author":"Travnikov","year":"2012","journal-title":"EMEP Status Rep."},{"key":"ref_57","unstructured":"Gentile, A.R., Barcel\u00f3-Cord\u00f3n, S., and Van Liedekerke, M. (2009). Soil Country Analyses-Belgium, JRC."},{"key":"ref_58","first-page":"66","article-title":"Numerical modelling of heavy metals transport processes in riverine basins","volume":"6","author":"Kashefipour","year":"2012","journal-title":"Numer. Model."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/S1003-6326(21)65494-8","article-title":"Simulation on release of heavy metals Cd and Pb in sediments","volume":"31","author":"Yan","year":"2021","journal-title":"Trans. Nonferrous Met. Soc. China"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0016-7185(73)90006-7","article-title":"Heavy metal accumulation in river sediments: A response to environmental pollution","volume":"4","year":"1973","journal-title":"Geoforum"},{"key":"ref_61","first-page":"102447","article-title":"Long-term Landsat monitoring of mining subsidence based on spatiotemporal variations in soil moisture: A case study of Shanxi Province, China","volume":"102","author":"Yi","year":"2021","journal-title":"Int. J. Appl. Earth Observ. Geoinf."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2093","DOI":"10.1007\/s11368-020-02576-5","article-title":"Soil nutrients and heavy metal availability under long-term combined application of swine manure and synthetic fertilizers in acidic paddy soil","volume":"20","author":"Qaswar","year":"2020","journal-title":"J. Soils Sediments"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"137305","DOI":"10.1016\/j.scitotenv.2020.137305","article-title":"The impact of land use changes and erosion process on heavy metal distribution in the hilly area of the Loess Plateau, China","volume":"718","author":"Zhang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"131638","DOI":"10.1016\/j.chemosphere.2021.131638","article-title":"Interactions among heavy metal bioaccessibility, soil properties and microbial community in phyto-remediated soils nearby an abandoned realgar mine","volume":"286","author":"Xiao","year":"2022","journal-title":"Chemosphere"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Mohamed, E.S., Baroudy, A., El-beshbeshy, T., Emam, M., Belal, A.A., Elfadaly, A., Aldosari, A.A., Ali, A., and Lasaponara, R. (2020). Vis-NIR Spectroscopy and Satellite Landsat-8 OLI Data to Map Soil Nutrients in Arid Conditions: A Case Study of the Northwest Coast of Egypt. Remote Sens., 12.","DOI":"10.3390\/rs12223716"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4615\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:31:15Z","timestamp":1760167875000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4615"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,16]]},"references-count":65,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13224615"],"URL":"https:\/\/doi.org\/10.3390\/rs13224615","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,16]]}}}