{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T04:12:06Z","timestamp":1776399126913,"version":"3.51.2"},"reference-count":96,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,13]],"date-time":"2021-06-13T00:00:00Z","timestamp":1623542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008993","name":"Sveu\u010dili\u0161te u Zagrebu","doi-asserted-by":"publisher","award":["RS4ENVIRO"],"award-info":[{"award-number":["RS4ENVIRO"]}],"id":[{"id":"10.13039\/100008993","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004488","name":"Hrvatska Zaklada za Znanost","doi-asserted-by":"publisher","award":["HRZZIP-2016-06-5621"],"award-info":[{"award-number":["HRZZIP-2016-06-5621"]}],"id":[{"id":"10.13039\/501100004488","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land-cover (LC) mapping in a morphologically heterogeneous landscape area is a challenging task since various LC classes (e.g., crop types in agricultural areas) are spectrally similar. Most research is still mostly relying on optical satellite imagery for these tasks, whereas synthetic aperture radar (SAR) imagery is often neglected. Therefore, this research assessed the classification accuracy using the recent Sentinel-1 (S1) SAR and Sentinel-2 (S2) time-series data for LC mapping, especially vegetation classes. Additionally, ancillary data, such as texture features, spectral indices from S1 and S2, respectively, as well as digital elevation model (DEM), were used in different classification scenarios. Random Forest (RF) was used for classification tasks using a proposed hybrid reference dataset derived from European Land Use and Coverage Area Frame Survey (LUCAS), CORINE, and Land Parcel Identification Systems (LPIS) LC database. Based on the RF variable selection using Mean Decrease Accuracy (MDA), the combination of S1 and S2 data yielded the highest overall accuracy (OA) of 91.78%, with a total disagreement of 8.22%. The most pertinent features for vegetation mapping were GLCM Mean and Variance for S1, NDVI, along with Red and SWIR band for S2, whereas the digital elevation model produced major classification enhancement as an input feature. The results of this study demonstrated that the aforementioned approach (i.e., RF using a hybrid reference dataset) is well-suited for vegetation mapping using Sentinel imagery, which can be applied for large-scale LC classifications.<\/jats:p>","DOI":"10.3390\/rs13122321","type":"journal-article","created":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T22:25:46Z","timestamp":1623709546000},"page":"2321","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":84,"title":["Sentinel-1 and 2 Time-Series for Vegetation Mapping Using Random Forest Classification: A Case Study of Northern Croatia"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3941-4943","authenticated-orcid":false,"given":"Dino","family":"Dobrini\u0107","sequence":"first","affiliation":[{"name":"Chair of Geoinformatics, Faculty of Geodesy, University of Zagreb, 10000 Zagreb, Croatia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2345-7882","authenticated-orcid":false,"given":"Mateo","family":"Ga\u0161parovi\u0107","sequence":"additional","affiliation":[{"name":"Chair of Photogrammetry and Remote Sensing, Faculty of Geodesy, University of Zagreb, 10000 Zagreb, Croatia"}]},{"given":"Damir","family":"Medak","sequence":"additional","affiliation":[{"name":"Chair of Geoinformatics, Faculty of Geodesy, University of Zagreb, 10000 Zagreb, Croatia"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1080\/01431161.2012.748992","article-title":"Finer Resolution Observation and Monitoring of Global Land Cover: First Mapping Results with Landsat TM and ETM+ Data","volume":"34","author":"Gong","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.landurbplan.2017.09.019","article-title":"A Future Land Use Simulation Model (FLUS) for Simulating Multiple Land Use Scenarios by Coupling Human and Natural Effects","volume":"168","author":"Liu","year":"2017","journal-title":"Landsc. Urban Plan."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mercier, A., Betbeder, J., Rumiano, F., Baudry, J., Gond, V., Blanc, L., Bourgoin, C., Cornu, G., Ciudad, C., and Marchamalo, M. (2019). Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest\u2013Agriculture Mosaics in Temperate and Tropical Landscapes. Remote Sens., 11.","DOI":"10.3390\/rs11080979"},{"key":"ref_4","first-page":"91","article-title":"Integration Of Multitemporal Sentinel-1 And Sentinel-2 Imagery For Land-Cover Classification Using Machine Learning Methods. Int. Arch. Photogramm","volume":"43","author":"Medak","year":"2020","journal-title":"Remote Sens. Spat. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0034-4257(02)00135-9","article-title":"Monitoring Vegetation Phenology Using MODIS","volume":"84","author":"Zhang","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_6","first-page":"47","article-title":"Fusing Landsat and MODIS Data for Vegetation Monitoring. IEEE Geosci","volume":"3","author":"Gao","year":"2015","journal-title":"Remote Sens. Mag."},{"key":"ref_7","first-page":"318","article-title":"Performance of Vegetation Indices from Landsat Time Series in Deforestation Monitoring","volume":"52","author":"Schultz","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.3390\/rs4061856","article-title":"Preparing Landsat Image Time Series (LITS) for Monitoring Changes in Vegetation Phenology in Queensland, Australia","volume":"4","author":"Bhandari","year":"2012","journal-title":"Remote Sens."},{"key":"ref_9","first-page":"122","article-title":"How Much Does Multi-Temporal Sentinel-2 Data Improve Crop Type Classification?","volume":"72","author":"Vuolo","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1080\/15481603.2017.1351149","article-title":"Assessing the Suitability of Data from Sentinel-1A and 2A for Crop Classification","volume":"54","author":"Sonobe","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Inglada, J., Vincent, A., Arias, M., and Marais-Sicre, C. (2016). Improved Early Crop Type Identification by Joint Use of High Temporal Resolution Sar and Optical Image Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8050362"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ga\u0161parovi\u0107, M., and Klobu\u010dar, D. (2021). Mapping Floods in Lowland Forest Using Sentinel-1 and Sentinel-2 Data and an Object-Based Approach. Forests, 12.","DOI":"10.3390\/f12050553"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/PROC.1978.10961","article-title":"Tutorial Review of Synthetic-Aperture Radar (SAR) with Applications to Imaging of the Ocean Surface","volume":"66","author":"Tomiyasu","year":"1978","journal-title":"Proc. IEEE"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2248301","article-title":"A tutorial on synthetic aperture radar","volume":"1","author":"Moreira","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ga\u0161parovi\u0107, M., and Dobrini\u0107, D. (2020). Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12121952"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111804","DOI":"10.1016\/j.rse.2020.111804","article-title":"Understanding Wheat Lodging Using Multi-Temporal Sentinel-1 and Sentinel-2 Data","volume":"243","author":"Chauhan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"112128","DOI":"10.1016\/j.rse.2020.112128","article-title":"National-Scale Mapping of Building Height Using Sentinel-1 and Sentinel-2 Time Series","volume":"252","author":"Frantz","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"111465","DOI":"10.1016\/j.rse.2019.111465","article-title":"From Woody Cover to Woody Canopies: How Sentinel-1 and Sentinel-2 Data Advance the Mapping of Woody Plants in Savannas","volume":"234","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8703","DOI":"10.1080\/01431161.2018.1490976","article-title":"Land-Cover Mapping Using Random Forest Classification and Incorporating NDVI Time-Series and Texture: A Case Study of Central Shandong","volume":"39","author":"Jin","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","first-page":"1","article-title":"Green Infrastructure Mapping in Urban Areas Using Sentinel-1 Imagery","volume":"42","year":"2021","journal-title":"Croat. J. For. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1080\/2150704X.2016.1274443","article-title":"Urban Landcover Classification from Multispectral Image Data Using Optimized AdaBoosted Random Forests","volume":"8","author":"Isaac","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Feng, Q., Yang, J., Zhu, D., Liu, J., Guo, H., Bayartungalag, B., and Li, B. (2019). Integrating Multitemporal Sentinel-1\/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta. Remote Sens., 11.","DOI":"10.3390\/rs11091006"},{"key":"ref_23","first-page":"115330K","article-title":"Monitoring of Agricultural Areas by Using Sentinel 2 Image Time Series and Deep Learning Techniques","volume":"11533","author":"Paris","year":"2020","journal-title":"Proc. SPIE."},{"key":"ref_24","unstructured":"Han, H., Guo, X., and Yu, H. (2016, January 26\u201328). Variable Selection Using Mean Decrease Accuracy and Mean Decrease Gini Based on Random Forest. Proceedings of the 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A Review of Feature Selection Techniques in Bioinformatics","volume":"23","author":"Saeys","year":"2007","journal-title":"Bioinformatics"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jovi\u0107, A., Brki\u0107, K., and Bogunovi\u0107, N. (2015, January 25\u201329). A Review of Feature Selection Methods with Applications. Proceedings of the 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2015-Proceedings, Opatija, Croatia.","DOI":"10.1109\/MIPRO.2015.7160458"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","unstructured":"Baudoux, L., Inglada, J., and Mallet, C. (2021). Toward a Yearly Country-Scale CORINE Land-Cover Map without Using Images: A Map Translation Approach. Remote Sens., 13.","DOI":"10.3390\/rs13061060"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Van Tricht, K., Gobin, A., Gilliams, S., and Piccard, I. (2018). Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium. Remote Sens., 10.","DOI":"10.20944\/preprints201808.0066.v1"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rse.2017.10.005","article-title":"Sentinel-2 Cropland Mapping Using Pixel-Based and Object-Based Time-Weighted Dynamic Time Warping Analysis","volume":"204","author":"Belgiu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_31","first-page":"102065","article-title":"Spatial and Semantic Effects of LUCAS Samples on Fully Automated Land Use\/Land Cover Classification in High-Resolution Sentinel-2 Data","volume":"88","author":"Weigand","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"14876","DOI":"10.3390\/rs71114876","article-title":"Mapping CORINE Land Cover from Sentinel-1A SAR and SRTM Digital Elevation Model Data Using Random Forests","volume":"7","author":"Balzter","year":"2015","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good Practices for Estimating Area and Assessing Accuracy of Land Change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5243","DOI":"10.1080\/01431160903131000","article-title":"Sampling Designs for Accuracy Assessment of Land Cover","volume":"30","author":"Stehman","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2018.214","article-title":"Present and Future K\u00f6ppen-Geiger Climate Classification Maps at 1-Km Resolution","volume":"5","author":"Beck","year":"2018","journal-title":"Sci. Data"},{"key":"ref_36","unstructured":"(2021, January 15). World Weather Online. Available online: https:\/\/www.worldweatheronline.com\/cakovec-weather-history\/medimurska\/hr.aspx."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 Mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TPAMI.1980.4766994","article-title":"Digital Image Enhancement and Noise Filtering by Use of Local Statistics","volume":"2","author":"Lee","year":"1980","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Osgouei, P.E., Kaya, S., Sertel, E., and Alganci, U. (2019). Separating Built-up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11030345"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1080\/17445647.2017.1372316","article-title":"Fusion of Sentinel-1a and Sentinel-2A Data for Land Cover Mapping: A Case Study in the Lower Magdalena Region, Colombia","volume":"13","author":"Clerici","year":"2017","journal-title":"J. Maps"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and Photographic Infrared Linear Combinations for Monitoring Vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A Soil-Adjusted Vegetation Index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"7063","DOI":"10.3390\/s110707063","article-title":"Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content","volume":"11","author":"Delegido","year":"2011","journal-title":"Sensors"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/S0273-1177(97)01133-2","article-title":"Remote Sensing of Chlorophyll Concentration in Higher Plant Leaves","volume":"22","author":"Gitelson","year":"1998","journal-title":"Adv. Sp. Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A Modified Soil Adjusted Vegetation Index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1080\/014311698215919","article-title":"Spectral Indices for Estimating Photosynthetic Pigment Concentrations: A Test Using Senescent Tree Leaves","volume":"19","author":"Blackburn","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2013.04.007","article-title":"Evaluating the Capabilities of Sentinel-2 for Quantitative Estimation of Biophysical Variables in Vegetation","volume":"82","author":"Frampton","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1117\/12.511317","article-title":"Estimation of crop coefficients by means of optimized vegetation indices for corn","volume":"5232","author":"Calera","year":"2004","journal-title":"Remote Sens. Agric. Ecosyst. Hydrol. V"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chatziantoniou, A., Psomiadis, E., and Petropoulos, G. (2017). Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning. Remote Sens., 9.","DOI":"10.3390\/rs9121259"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1080\/10106048709354084","article-title":"Introductory Digital Image Processing: A Remote Sensing Perspective","volume":"2","author":"Jensen","year":"1987","journal-title":"Geocarto Int."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A Review of Supervised Object-Based Land-Cover Image Classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"204","DOI":"10.3103\/S1060992X19030093","article-title":"Agriculture Phenology Monitoring Using NDVI Time Series Based on Remote Sensing Satellites: A Case Study of Guangdong, China","volume":"28","author":"Choudhary","year":"2019","journal-title":"Opt. Mem. Neural Netw."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4651","DOI":"10.3390\/rs70404651","article-title":"Optimal Combination of Classification Algorithms and Feature Ranking Methods for Object-Based Classification of Submeter Resolution Z\/I-Imaging DMC Imagery","volume":"7","year":"2015","journal-title":"Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of Hyperspectral Remote Sensing Images With Support Vector Machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1007\/s41976-019-00023-9","article-title":"The Performance of Random Forest Classification Based on Phenological Metrics Derived from Sentinel-2 and Landsat 8 to Map Crop Cover in an Irrigated Semi-Arid Region","volume":"2","author":"Htitiou","year":"2019","journal-title":"Remote Sens. Earth Syst. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1109\/LGRS.2017.2745049","article-title":"A Systematic Approach for Variable Selection with Random Forests: Achieving Stable Variable Importance Values","volume":"14","author":"Behnamian","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.csda.2015.10.005","article-title":"Random Forest for Ordinal Responses: Prediction and Variable Selection","volume":"96","author":"Janitza","year":"2016","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random Forest in Remote Sensing: A Review of Applications and Future Directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4407","DOI":"10.1080\/01431161.2011.552923","article-title":"Death to Kappa: Birth of Quantity Disagreement and Allocation Disagreement for Accuracy Assessment","volume":"32","author":"Pontius","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"111199","DOI":"10.1016\/j.rse.2019.05.018","article-title":"Key Issues in Rigorous Accuracy Assessment of Land Cover Products","volume":"231","author":"Stehman","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"643","DOI":"10.5721\/EuJRS20164934","article-title":"Assessing the Effectiveness of RapidEye Multispectral Imagery for Vegetation Mapping in Madeira Island (Portugal)","volume":"49","author":"Massetti","year":"2016","journal-title":"Eur. J. Remote Sens."},{"key":"ref_66","first-page":"397","article-title":"Remote Sensing Brief Accuracy Assessment: A User\u2019s Perspective","volume":"52","author":"Story","year":"1986","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_67","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_68","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_69","doi-asserted-by":"crossref","unstructured":"Abdullah, A.Y.M., Masrur, A., Gani Adnan, M.S., Al Baky, M.A., Hassan, Q.K., and Dewan, A. (2019). Spatio-Temporal Patterns of Land Use\/Land Cover Change in the Heterogeneous Coastal Region of Bangladesh between 1990 and 2017. Remote Sens., 11.","DOI":"10.3390\/rs11070790"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"339","DOI":"10.5194\/essd-6-339-2014","article-title":"Deriving a Per-Field Land Use and Land Cover Map in an Agricultural Mosaic Catchment","volume":"6","author":"Seo","year":"2014","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Huang, W., DeVries, B., Huang, C., Lang, M., Jones, J., Creed, I., and Carroll, M. (2018). Automated Extraction of Surface Water Extent from Sentinel-1 Data. Remote Sens., 10.","DOI":"10.3390\/rs10050797"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"6298","DOI":"10.1080\/01431161.2017.1353160","article-title":"Sensitivity of Sentinel-1 Backscatter to Characteristics of Buildings","volume":"38","author":"Koppel","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2343","DOI":"10.3390\/rs6032343","article-title":"Agricultural Monitoring in Northeastern Ontario, Canada, Using Multi-Temporal Polarimetric RADARSAT-2 Data","volume":"6","author":"Cable","year":"2014","journal-title":"Remote Sens."},{"key":"ref_74","first-page":"6859","article-title":"The Jeffries\u2013Matusita Distance for the Case of Complex Wishart Distribution as a Separability Criterion for Fully Polarimetric SAR Data","volume":"35","author":"Dabboor","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_75","unstructured":"Klein, D., Moll, A., and Menz, G. (2004, January 6\u201310). Land Cover\/Use Classification in a Semiarid Environment in East Africa Using Multi-Temporal Alternating Polarization ENVISAT ASAR Data. Proceedings of the 2004 Envisat & ERS Symposium (ESA SP-572), Salzburg, Austria."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Harfenmeister, K., and Spengler, D. (2019). Analyzing Temporal and Spatial Characteristics of Crop Parameters Using Sentinel-1 Backscatter Data. Remote Sens., 11.","DOI":"10.3390\/rs11131569"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Tavares, P.A., Beltr\u00e3o, N.E.S., Guimar\u00e3es, U.S., and Teodoro, A.C. (2019). Integration of Sentinel-1 and Sentinel-2 for Classification and LULC Mapping in the Urban Area of Bel\u00e9m, Eastern Brazilian Amazon. Sensors, 19.","DOI":"10.3390\/s19051140"},{"key":"ref_78","first-page":"1","article-title":"Land Cover and Land Use Classification Performance of Machine Learning Algorithms in a Boreal Landscape Using Sentinel-2 Data. GISci","volume":"57","author":"Abdi","year":"2019","journal-title":"Remote Sens."},{"key":"ref_79","first-page":"331","article-title":"Landsat-8 vs. Sentinel-2: Examining the Added Value of Sentinel-2\u2019s Red-Edge Bands to Land-Use and Land-Cover Mapping in Burkina Faso. GISci","volume":"55","author":"Forkuor","year":"2018","journal-title":"Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/S0034-4257(01)00222-X","article-title":"Investigating Relationships between Landsat ETM+ Sensor Data and Leaf Area Index in a Boreal Conifer Forest","volume":"78","author":"Eklundh","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.3390\/rs4092661","article-title":"Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data","volume":"4","author":"Immitzer","year":"2012","journal-title":"Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2017.07.015","article-title":"Understanding the Temporal Behavior of Crops Using Sentinel-1 and Sentinel-2-like Data for Agricultural Applications","volume":"199","author":"Veloso","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_83","first-page":"83","article-title":"Assessing Landsat Classification Accuracy Using Discrete Multivariate Analysis Statistical Techniques","volume":"27","author":"Congalton","year":"1983","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Sun, C., Bian, Y., Zhou, T., and Pan, J. (2019). Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region. Sensors, 19.","DOI":"10.3390\/s19102401"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Zakeri, H., Yamazaki, F., and Liu, W. (2017). Texture Analysis and Land Cover Classification of Tehran Using Polarimetric Synthetic Aperture Radar Imagery. Appl. Sci., 7.","DOI":"10.3390\/app7050452"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Holtgrave, A., R\u00f6der, N., Ackermann, A., Erasmi, S., and Kleinschmit, B. (2020). Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring. Remote Sens., 12.","DOI":"10.3390\/rs12182919"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Bouvet, A., Mermoz, S., Ball\u00e8re, M., Koleck, T., and Le Toan, T. (2018). Use of the SAR Shadowing Effect for Deforestation Detection with Sentinel-1 Time Series. Remote Sens., 10.","DOI":"10.3390\/rs10081250"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Xiang, D., Tang, T., Hu, C., Fan, Q., and Su, Y. (2016). Built-up Area Extraction from Polsar Imagery with Model-Based Decomposition and Polarimetric Coherence. Remote Sens., 8.","DOI":"10.3390\/rs8080685"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1080\/13658810500072020","article-title":"Comparison of Land Cover Maps Using Fuzzy Agreement","volume":"19","author":"Fritz","year":"2005","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_90","first-page":"102064","article-title":"Integrating Multiple Land Cover Maps through a Multi-Criteria Analysis to Improve Agricultural Monitoring in Africa","volume":"88","author":"Rembold","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"28","DOI":"10.2151\/sola.2019-006","article-title":"How Does Land Use\/Land Cover Map\u2019s Accuracy Depend on Number of Classification Classes?","volume":"15","author":"Thinh","year":"2019","journal-title":"SOLA"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2015.03.014","article-title":"Exploring Issues of Training Data Imbalance and Mislabelling on Random Forest Performance for Large Area Land Cover Classification Using the Ensemble Margin","volume":"105","author":"Mellor","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Pelletier, C., Valero, S., Inglada, J., Champion, N., Sicre, C.M., and Dedieu, G. (2017). Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series. Remote Sens., 9.","DOI":"10.3390\/rs9020173"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Dabija, A., Kluczek, M., Zagajewski, B., Raczko, E., Kycko, M., Al-Sulttani, A.H., Tard\u00e0, A., Pineda, L., and Corbera, J. (2021). Comparison of Support Vector Machines and Random Forests for Corine Land Cover Mapping. Remote Sens., 13.","DOI":"10.3390\/rs13040777"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Close, O., Benjamin, B., Petit, S., Fripiat, X., and Hallot, E. (2018). Use of Sentinel-2 and LUCAS Database for the Inventory of Land Use, Land Use Change, and Forestry in Wallonia, Belgium. Land, 7.","DOI":"10.3390\/land7040154"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V., Murayama, Y., and Ranagalage, M. (2020). Sentinel-2 Data for Land Cover\/Use Mapping: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12142291"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2321\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:13:54Z","timestamp":1760163234000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2321"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,13]]},"references-count":96,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13122321"],"URL":"https:\/\/doi.org\/10.3390\/rs13122321","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,13]]}}}