{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T23:33:18Z","timestamp":1776814398321,"version":"3.51.2"},"reference-count":65,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T00:00:00Z","timestamp":1594166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In wetland environments, vegetation has an important role in ecological functioning. The main goal of this work was to identify an optimal combination of Sentinel-1 (S1), Sentinel-2 (S2), and Pleiades data using ground-reference data to accurately map wetland macrophytes in the Danube Delta. We tested several combinations of optical and Synthetic Aperture Radar (SAR) data rigorously at two levels. First, in order to reduce the confusion between reed (Phragmites australis (Cav.) Trin. ex Steud.) and other macrophyte communities, a time series analysis of S1 data was performed. The potential of S1 for detection of compact reed on plaur, compact reed on plaur\/reed cut, open reed on plaur, pure reed, and reed on salinized soil was evaluated through time series of backscatter coefficient and coherence ratio images, calculated mainly according to the phenology of the reed. The analysis of backscattering coefficients allowed separation of reed classes that strongly overlapped. The coherence coefficient showed that C-band SAR repeat pass interferometric coherence for cut reed detection is feasible. In the second section, random forest (RF) classification was applied to the S2, Pleiades, and S1 data and in situ observations to discriminate and map reed against other aquatic macrophytes (submerged aquatic vegetation (SAV), emergent macrophytes, some floating broad-leaved and floating vegetation of delta lakes). In addition, different optical indices were included in the RF. A total of 67 classification models were made in several sensor combinations with two series of validation samples (with the reed and without reed) using both a simple and more detailed classification schema. The results showed that reed is completely discriminable compared to other macrophyte communities with all sensor combinations. In all combinations, the model-based producer\u2019s accuracy (PA) and user\u2019s accuracy (UA) for reed with both nomenclatures were over 90%. The diverse combinations of sensors were valuable for improving the overall classification accuracy of all of the communities of aquatic macrophytes except Myriophyllum spicatum L.<\/jats:p>","DOI":"10.3390\/rs12142188","type":"journal-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T11:47:46Z","timestamp":1594208866000},"page":"2188","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta"],"prefix":"10.3390","volume":"12","author":[{"given":"Simona","family":"Niculescu","sequence":"first","affiliation":[{"name":"Department of Geography, University of Western Brittany, 3 Rue des Archives, 29238 Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Baptiste","family":"Boissonnat","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Rennes 2, Place Recteur Henri le Moal, 35000 Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C\u00e9dric","family":"Lardeux","sequence":"additional","affiliation":[{"name":"ONF International, 45 bis avenue de la Belle Gabrielle, 94736 Nogent-sur-Marne, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3555-4842","authenticated-orcid":false,"given":"Dar","family":"Roberts","sequence":"additional","affiliation":[{"name":"Department of Geography, University Santa Barbara, Santa Barbara, CA 93106, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jenica","family":"Hanganu","sequence":"additional","affiliation":[{"name":"Danube Delta National Institute for Research and Development, Strada Babadag nr. 165, Tulcea 820112, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antoine","family":"Billey","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Western Brittany, 3 Rue des Archives, 29238 Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adrian","family":"Constantinescu","sequence":"additional","affiliation":[{"name":"Danube Delta National Institute for Research and Development, Strada Babadag nr. 165, Tulcea 820112, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mihai","family":"Doroftei","sequence":"additional","affiliation":[{"name":"Danube Delta National Institute for Research and Development, Strada Babadag nr. 165, Tulcea 820112, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,8]]},"reference":[{"key":"ref_1","unstructured":"Mitsch, W.J., and Gosselink, J.G. (1986). Wetlands, Van Nostrand Reinhold."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/S0304-3770(97)00060-0","article-title":"Die back of Phragmites australis in European wetlands: An overview of the European Research Programme on reed die-back and progression (1993\u20131994)","volume":"59","year":"1997","journal-title":"Aquat. Bot."},{"key":"ref_3","first-page":"333","article-title":"The role of macrophytes in wetland ecosystems","volume":"34","year":"2011","journal-title":"Ecol. Field Biol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2637","DOI":"10.1111\/j.1365-2427.2011.02680.x","article-title":"Using water plant functional groups to investigate environmental water requirements","volume":"56","author":"Casanova","year":"2011","journal-title":"Freshw. Biol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111467","DOI":"10.1016\/j.rse.2019.111467","article-title":"Spectral vegetation indices of wetland greenness: Responses to vegetation structure, composition, and spatial distribution","volume":"234","author":"Taddeo","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jensen, D., Cavanaugh, K.C., Simard, M., Okin, G.S., Castaneda-Moya, E., McCall, A., and Twilley, R.R. (2019). Integrating imaging spectrometer and synthetic aperture radar data for estimating wetland vegetation aboveground biomass in coastal louisiana. Remote Sens., 11.","DOI":"10.3390\/rs11212533"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.isprsjprs.2019.09.007","article-title":"Multi-season RapidEye imagery improves the classification of wetland and dryland communities in a subtropical coastal region","volume":"157","author":"Cho","year":"2019","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1016\/j.jenvman.2019.06.098","article-title":"Mapping potential, existing and efficient wetlands using free remote sensing data","volume":"247","author":"Rapinel","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s11273-019-09675-2","article-title":"Identification of most spectrally distinguishable phenological stage of invasive Phramites australis in Lake Erie wetlands (Canada) for accurate mapping using multispectral satellite imagery","volume":"27","author":"Rupasinghe","year":"2019","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Abeysinghe, T., Milas, A.S., Arend, K., Hohman, B., Reil, P., Gregory, A., and Vazquez-Ortega, A. (2019). Mapping invasive phragmites australis in the old woman creek estuary using UAV remote sensing and machine learning classifiers. Remote Sens., 11.","DOI":"10.3390\/rs11111380"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, H., and Ma, M. (2016). Impacts of climate change and anthropogenic activities on the ecological restoration of wetlands in the arid regions of china. Energies, 9.","DOI":"10.3390\/en9030166"},{"key":"ref_12","first-page":"1078305","article-title":"Random Forest Classification using Sentinel-1 and Sentinel-2 series for vegetation monitoring in the Pays de Brest (France)","volume":"10783","author":"Niculescu","year":"2018","journal-title":"SPIE DIGITAL LIBRARY SPIE Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Guo, M., Li, J., Sheng, C., Xu, J., and Wu, L. (2017). A review of wetland remote sensing. Sensors (Basel), 17.","DOI":"10.3390\/s17040777"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1080\/01431161.2010.532826","article-title":"Mapping the irrigated rice cropping patterns of the Mekong Delta, Vietnam, through hyper-temporal SPOT NDVI image analysis","volume":"33","author":"Nguyen","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1080\/17445647.2019.1644545","article-title":"Vegetation patterns in a South American coastal wetland using high-resolution imagery","volume":"15","author":"Gonzalez","year":"2019","journal-title":"J. Maps"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Proenca, B., Frappart, F., Lubac, B., Marieu, V., Ygorra, B., Bombrun, L., Michalet, R., and Sottolichio, A. (2019). Potential of High-Resolution Pleiades Imagery to Monitor Salt Marsh Evolution After Spartina Invasion. Remote Sens., 11.","DOI":"10.3390\/rs11080968"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.rse.2018.02.021","article-title":"Enabling efficient, large-scale high-spatial resolution wetland mapping using satellites","volume":"208","author":"McCarthy","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/S0034-4257(02)00196-7","article-title":"Spectral discrimination of vegetation types in a coastal wetland","volume":"85","author":"Schmidt","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s10661-007-9855-3","article-title":"Remote sensing of aquatic vegetation: Theory and applications","volume":"140","author":"Silva","year":"2008","journal-title":"Environ. Monit. Assess."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Morandeira, N.S., Grings, F., Facchinetti, C., and Kandus, P. (2016). Mapping plant functional types in floodplain wetlands: An analysis of C-band polarimetric SAR data from RADARSAT-2. Remote Sens., 8.","DOI":"10.3390\/rs8030174"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s11273-009-9169-z","article-title":"Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review","volume":"18","author":"Adam","year":"2010","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2170","DOI":"10.1016\/j.jenvman.2007.06.028","article-title":"Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing","volume":"90","author":"Zomer","year":"2009","journal-title":"J. Environ. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1023\/A:1020908432489","article-title":"Satellite remote sensing of wetlands","volume":"10","author":"Ozesmi","year":"2002","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5809","DOI":"10.1080\/01431160801958405","article-title":"Radar detection of wetland ecosystems: A review","volume":"29","author":"Henderson","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3519","DOI":"10.1080\/01431161.2015.1060647","article-title":"Comparing four operational SAR-based water and flood detection approaches","volume":"36","author":"Martinis","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7615","DOI":"10.3390\/rs70607615","article-title":"A collection of SAR methodologies for monitoring wetlands","volume":"7","author":"White","year":"2015","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3513","DOI":"10.1109\/TGRS.2016.2519842","article-title":"Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval","volume":"54","author":"Vreugdenhil","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1109\/36.158869","article-title":"Sensitivity of microwave measurements to vegetation biomass and soil moisture content: A case study","volume":"30","author":"Ferrazzoli","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.1109\/36.774723","article-title":"The potential of C- and L-band SAR in estimating vegetation biomass: The ERS-1 and JERS-1 experiments","volume":"37","author":"Paloscia","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/S0034-4257(96)00151-4","article-title":"Detecting seasonal flooding cycles in marshes of the Yucat\u00e1n peninsula with SIR-C polarimetric radar imagery","volume":"59","author":"Pope","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3651","DOI":"10.1109\/JSTARS.2016.2545242","article-title":"Synergy between LiDAR, RADARSAT-2 and SPOT-5 images for the detection and mapping of wetland vegetation in the Danube Delta","volume":"9","author":"Niculescu","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/978-3-319-56179-0_17","article-title":"Alteration and Remediation of Coastal Wetland Ecosystems in the Danube Delta: A Remote-Sensing Approach","volume":"Volume 21","author":"Niculescu","year":"2017","journal-title":"Coastal Research Library"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.5194\/isprs-archives-XLII-3-1311-2018","article-title":"Application of Deep Learning of multi-temporal Sentinel-1 images for the classification of coastal vegetation zone of the Danube Delta","volume":"42","author":"Niculescu","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.ecolind.2016.09.029","article-title":"Comparison of object-based and pixel-based Random Forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data","volume":"73","author":"Fu","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tian, S., Zhang, X., Tian, J., and Sun, Q. (2016). Random Forest Classification of Wetland Land covers from Multi-Sensor Data in the Arid Region of Xinjiang, China. Remote Sens., 8.","DOI":"10.3390\/rs8110954"},{"key":"ref_36","first-page":"399","article-title":"High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm","volume":"18","author":"Mutanga","year":"2012","journal-title":"Int. J Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2017.05.010","article-title":"Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery","volume":"130","author":"Mahdianpari","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Berhane, T.M., Lane, C.R., Wu, Q., Autrey, B.C., Anenkhonov, O.A., Chepinoga, V.V., and Liu, H. (2018). Decision-tree, rule-based, and random forest classification of high-resolution multispectral imagery for wetland mapping and inventory. Remote Sens (Basel), 10.","DOI":"10.3390\/rs10040580"},{"key":"ref_39","unstructured":"Kholodny, M.G. (2002). Vegetation of the Biosphere Reserve Danube Delta\u2014With Transboundary Vegetation Map on a 1:150.000 Scale, Danube Delta National Institute, Romania, Institute of Botany and Danube Delta Biosphre Reserve, Ukraine and RIZA."},{"key":"ref_40","unstructured":"Oosterberg, W., Buijse, A.D., Coops, H., Ibelings, B.W., and Menting, G.A.M. (2000). Ecological Gradients in the Danube Delta lakes: Present State and Man-Induced Changes, RIZA."},{"key":"ref_41","unstructured":"Vollenweider, R.A., and Kerekes, J. (1982). Eutrophication of Waters. Monitoring, Assessment and Control. Methoden der Kartierung von Flora und Vegetation von S\u00fc\u00dfwasserbiotopen. Cooperative Programme on Monitoring of Inland Waters (Eutrophication Control), Environment Directorate OECD."},{"key":"ref_42","first-page":"73","article-title":"Methoden der Kartierung von Flora und Vegetation von S\u00fc\u00dfwasserbiotopen","volume":"10","author":"Kohler","year":"1978","journal-title":"Landschaft"},{"key":"ref_43","unstructured":"Hanganu, J., and Doroftei, M. (2016). Physical landscape\u2014Danube delta reed beds. The Biopolitics of the Danube Delta: Nature, History, Policies, Lexington Books."},{"key":"ref_44","unstructured":"ESA (2020, July 07). TOPS Interferometry Tutorial; Sentinel 1 Toolbox; Array Systems Computing: 2015. Available online: http:\/\/step.esa.int\/docs\/tutorials\/S1TBX%20TOPSAR%20Interferometry%20with%20Sentinel-1%20Tutorial_v2.pdf."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.isprsjprs.2017.10.004","article-title":"A hybrid training approach for leaf area index estimation via cubist and random forests machine learning","volume":"135","author":"Houborg","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","unstructured":"Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., and Wu, X. (2018). Artificial mangrove species mapping using pl\u00e9iades-1: An evaluation of pixel-based and object-based classifications with selected machine learning algorithms. Remote Sens., 10.","DOI":"10.3390\/rs10020294"},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2006.09.012","article-title":"Validation of a largearea land cover product using purpose-acquired airborne video","volume":"106","author":"Wulder","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_51","unstructured":"***, 2019 \u2013 Fundamentarea m\u0103surilor de reconstruc\u021bie ecologic\u0103 a lacurilor din Delta Dun\u0103rii pe baza studiului dinamicii habitatelor de macrofite acvatice, 19 pagini. Raport Faza 4 \/ Decembrie\/2019, al proiectului nr. PN 19 12 02 01 04 (coord. Jenic\u0103 Hanganu) al contractului nr. 41N\/2019\/MCI, executant: INCDDD\u2014Tulcea. Rom\u00e2nia (publication in progress)."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rse.2012.10.031","article-title":"Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation","volume":"129","author":"Olofsson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"63","DOI":"10.52638\/rfpt.2012.83","article-title":"Utilisation de l\u2019imagerie radar Terrasar-X THRS pour le suivi de la coupe de canne \u00e0 sucre \u00e0 l\u2019Ile de la R\u00e9union","volume":"197","author":"Baghdadi","year":"2014","journal-title":"Revue Fr. Photogramm. T\u00e9l\u00e9d\u00e9tect."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Tamm, T., Zalite, K., Voormansik, K., and Talgre, L. (2016). Relating Sentinel-1 Interferometric Coherence to Mowing Events on Grasslands. Remote Sens., 8.","DOI":"10.3390\/rs8100802"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.envsoft.2018.01.023","article-title":"A new synergistic approach for monitoring wetlands using Sentinels-1 and 2 data with object-based machine learning algorithms","volume":"104","author":"Whyte","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_56","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_57","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 (Basel), 19.","DOI":"10.3390\/s19051140"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.rse.2018.07.006","article-title":"Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery","volume":"216","author":"Erinjery","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Chatziantoniou, A., Petropoulos, G.P., and Psomiadis, E. (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_60","unstructured":"Frison, P.-L., Kmiha, S., Fruneau, B., Soudani, K., Dufr\u00eane, E., Koleck, T., Villard, L., Lepage, M., Dejoux, J.-F., and Rudant, J.-P. (2020, June 07). Contribution of Sentinel-1 data for the monitoring of seasonal variations of the vegetation. MULTITEMP 2017, Bruges, Belgium. Available online: https:\/\/multitemp2017.vito.be\/sites\/multitemp2017.vito.be\/files\/1600-1-for_websitemultitemp_27jun17_plf.pdf."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Talab Ou Ali, H., Niculescu, S., Sellin, V., and Bougault, C. (2017, January 2). Contribution of the new satellites (Sentinel-1, Sentinel-2 and SPOT-6) to the coastal vegetation monitoring in the Pays de Brest (France). Proceedings of the SPIE, Warsaw, Poland.","DOI":"10.1117\/12.2277320"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Marbouti, M., Praks, J., Antropov, O., Rinne, E., and Lepp\u00e4ranta, M. (2017). A study of landfast ice with Sentinel-1 repeat-pass interferometry over the Baltic Sea. Remote Sens., 9.","DOI":"10.3390\/rs9080833"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Dubeau, P., King, D.J., Unbushe, D.G., and Rebelo, L.-M. (2017). Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data. Remote Sens., 9.","DOI":"10.3390\/rs9101056"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"12187","DOI":"10.3390\/rs61212187","article-title":"Improved Wetland Classification Using Eight-Band High Resolution Satellite Imagery and a Hybrid Approach","volume":"6","author":"Lane","year":"2014","journal-title":"Remote Sens."},{"key":"ref_65","unstructured":"Kim, Y., and van Zyl, J. (2004, January 20\u201324). Vegetation effects on soil moisture estimation. Proceedings of the Geoscience and Remote Sensing Symposium, Anchorage, AK, USA."}],"updated-by":[{"DOI":"10.3390\/rs12162529","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T00:00:00Z","timestamp":1594166400000}}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/14\/2188\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T22:33:49Z","timestamp":1754260429000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/14\/2188"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,8]]},"references-count":65,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["rs12142188"],"URL":"https:\/\/doi.org\/10.3390\/rs12142188","relation":{"correction":[{"id-type":"doi","id":"10.3390\/rs12162529","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,8]]}}}