{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T16:47:50Z","timestamp":1792169270269,"version":"4.4.0"},"reference-count":83,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Wetlands are one of the most important ecosystems due to their critical services to both humans and the environment. Therefore, wetland mapping and monitoring are essential for their conservation. In this regard, remote sensing offers efficient solutions due to the availability of cost-efficient archived images over different spatial scales. However, a lack of sufficient consistent training samples at different times is a significant limitation of multi-temporal wetland monitoring. In this study, a new training sample migration method was developed to identify unchanged training samples to be used in wetland classification and change analyses over the International Shadegan Wetland (ISW) areas of southwestern Iran. To this end, we first produced the wetland map of a reference year (2020), for which we had training samples, by combining Sentinel-1 and Sentinel-2 images and the Random Forest (RF) classifier in Google Earth Engine (GEE). The Overall Accuracy (OA) and Kappa coefficient (KC) of this reference map were 97.93% and 0.97, respectively. Then, an automatic change detection method was developed to migrate unchanged training samples from the reference year to the target years of 2018, 2019, and 2021. Within the proposed method, three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the mean Standard Deviation (SD) of the spectral bands, along with two similarity measures of the Euclidean Distance (ED) and Spectral Angle Distance (SAD), were computed for each pair of reference\u2013target years. The optimum threshold for unchanged samples was also derived using a histogram thresholding approach, which led to selecting the samples that were most likely unchanged based on the highest OA and KC for classifying the test dataset. The proposed migration sample method resulted in high OAs of 95.89%, 96.83%, and 97.06% and KCs of 0.95, 0.96, and 0.96 for the target years of 2018, 2019, and 2021, respectively. Finally, the migrated samples were used to generate the wetland map for the target years. Overall, our proposed method showed high potential for wetland mapping and monitoring when no training samples existed for a target year.<\/jats:p>","DOI":"10.3390\/rs13204169","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T21:31:26Z","timestamp":1634765486000},"page":"4169","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["A Training Sample Migration Method for Wetland Mapping and Monitoring Using Sentinel Data in Google Earth Engine"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8870-359X","authenticated-orcid":false,"given":"Erfan","family":"Fekri","sequence":"first","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology (KNTU), Tehran 19967-15433, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1054-889X","authenticated-orcid":false,"given":"Hooman","family":"Latifi","sequence":"additional","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology (KNTU), Tehran 19967-15433, Iran"},{"name":"Department of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg, 97074 Wuerzburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9495-4010","authenticated-orcid":false,"given":"Meisam","family":"Amani","sequence":"additional","affiliation":[{"name":"Wood Environment & Infrastructure Solutions, Ottawa, ON K2E 7L5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdolkarim","family":"Zobeidinezhad","sequence":"additional","affiliation":[{"name":"Khuzestan Provincial Department of Environment, Ahvaz 61347-88417, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1016\/j.scitotenv.2017.09.103","article-title":"Potential for using remote sensing to estimate carbon fluxes across northern peatlands\u2014A review","volume":"615","author":"Lees","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_2","first-page":"2189","article-title":"Exploring the capacity of radar remote sensing to estimate wetland marshes water storage","volume":"90","author":"Grings","year":"2009","journal-title":"Environ. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.rse.2018.09.020","article-title":"New methods for improving the remote sensing estimation of soil organic matter content (SOMC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in northwest China","volume":"218","author":"Wang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.rse.2005.08.016","article-title":"Floodplain water storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels","volume":"99","author":"Frappart","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1007\/s10661-006-9486-0","article-title":"Determination of impact of urbanization on agricultural land and wetland land use in balcovas\u2019 delta by remote sensing and GIS technique","volume":"131","author":"Bolca","year":"2007","journal-title":"Environ. Monit. Assess."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.ecolind.2014.06.026","article-title":"Analysis of landscape fragmentation processes and driving forces in wetlands in arid areas: A case study of the middle reaches of the Heihe River, China","volume":"46","author":"Jiang","year":"2014","journal-title":"Ecol. Indic."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.scitotenv.2017.02.001","article-title":"Global wetlands: Potential distribution, wetland loss, and status","volume":"586","author":"Hu","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1080\/15481603.2017.1331510","article-title":"Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration","volume":"54","author":"Amani","year":"2017","journal-title":"Gisci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1080\/15481603.2017.1419602","article-title":"Remote sensing for wetland classification: A comprehensive review","volume":"55","author":"Mahdavi","year":"2018","journal-title":"Gisci Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kaplan, G., and Avdan, U. (2018). Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7100411"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1016\/j.isprsjprs.2009.04.004","article-title":"Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry","volume":"64","author":"Adam","year":"2009","journal-title":"ISPRS J. Photogramm."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/JSTARS.2020.3036802","article-title":"Evaluation of the Landsat-Based Canadian Wetland Inventory Map Using Multiple Sources: Challenges of Large-Scale Wetland Classification Using Remote Sensing","volume":"14","author":"Amani","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. 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, 17.","DOI":"10.3390\/s17040777"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2018.07.005","article-title":"Spectral analysis of wetlands using multi-source optical satellite imagery","volume":"144","author":"Amani","year":"2018","journal-title":"ISPRS J. Photogramm."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7370","DOI":"10.1080\/01431161.2018.1468117","article-title":"A Multiple Classifier System to improve mapping complex land covers: A case study of wetland classification using SAR data in Newfoundland, Canada","volume":"39","author":"Amani","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1007\/s13157-010-0085-z","article-title":"Calculating the Ecosystem Service of Water Storage in Isolated Wetlands using LiDAR in North Central Florida, USA","volume":"30","author":"Lane","year":"2010","journal-title":"Wetlands"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"024502","DOI":"10.1117\/1.JRS.14.024502","article-title":"Supervised wetland classification using high spatial resolution optical, SAR, and LiDAR imagery","volume":"14","author":"Amani","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4048","DOI":"10.1016\/j.rse.2008.05.020","article-title":"Integrating multi-temporal spectral and structural information to map wetland vegetation in a lower Connecticut River tidal marsh","volume":"112","author":"Gilmore","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ex18Rapinel, S., Mony, C., Lecoq, L., Clement, B., Thomas, A., and Hubert-Moy, L. (2019). Evaluation of Sentinel-2 time-series for mapping floodplain grassland plant communities. Remote Sens. Environ., 223, 115\u2013129.","DOI":"10.1016\/j.rse.2019.01.018"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Adeli, S., Salehi, B., Mahdianpari, M., Quackenbush, L.J., Brisco, B., Tamiminia, H., and Shaw, S. (2020). Wetland Monitoring Using SAR Data: A Meta-Analysis and Comprehensive Review. Remote Sens., 12.","DOI":"10.3390\/rs12142190"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Homayouni, S., and Gill, E. (2019). The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform. Remote Sens., 11.","DOI":"10.3390\/rs11010043"},{"key":"ref_22","first-page":"102009","article-title":"Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands, South Africa","volume":"86","author":"Slagter","year":"2020","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.catena.2019.03.011","article-title":"Evaluating the utilization of the red edge and radar bands from Sentinel sensors for wetland classification","volume":"178","author":"Kaplan","year":"2019","journal-title":"Catena"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ghorbanian, A., Zaghian, S., Mohammadi Asiyabi, R., Amani, M., Mohammadzadeh, A., and Jamali, S. (2011). Mangrove Ecosystem Mapping Using Sentinel-1 and Sentinel-2 Satellite Images and Random Forest Algorithm in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13132565"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"10938","DOI":"10.3390\/rs70810938","article-title":"The Challenges of Remote Monitoring of Wetlands","volume":"7","author":"Gallant","year":"2015","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5326","DOI":"10.1109\/JSTARS.2020.3021052","article-title":"Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review","volume":"13","author":"Amani","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1080\/20964471.2019.1690404","article-title":"A generalized supervised classification scheme to produce provincial wetland inventory maps: An application of Google Earth Engine for big geo data processing","volume":"3","author":"Amani","year":"2019","journal-title":"Big Earth Data"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hird, J., DeLancey, E., McDermid, G., and Kariyeva, J. (2017). Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping. Remote Sens., 9.","DOI":"10.3390\/rs9121315"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1080\/07038992.2017.1346468","article-title":"Wetland Classification Using Multi-Source and Multi-Temporal Optical Remote Sensing Data in Newfoundland and Labrador, Canada","volume":"43","author":"Amani","year":"2017","journal-title":"Can. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chatziantoniou, A., Petropoulos, G., 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_32","doi-asserted-by":"crossref","unstructured":"Berhane, T., Lane, C.R., Wu, Q., Autrey, B.C., Anenkhonov, O., 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., 10.","DOI":"10.3390\/rs10040580"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1109\/TGRS.2012.2195727","article-title":"Updating Land-Cover Maps by Classification of Image Time Series: A Novel Change-Detection-Driven Transfer Learning Approach","volume":"51","author":"Demir","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"16024","DOI":"10.3390\/rs71215819","article-title":"Automatic Labelling and Selection of Training Samples for High-Resolution Remote Sensing Image Classification over Urban Areas","volume":"7","author":"Huang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2012.04.006","article-title":"Bi-scale analysis of multitemporal land cover fractions for wetland vegetation mapping","volume":"72","author":"Michishita","year":"2012","journal-title":"ISPRS J. Photogramm."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3822","DOI":"10.1109\/TGRS.2016.2528583","article-title":"A Superresolution Land-Cover Change Detection Method Using Remotely Sensed Images with Different Spatial Resolutions","volume":"54","author":"Li","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change detection techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1080\/014311600209742","article-title":"Wetland change detection on the Kafue Flats, Zambia, by classification of a multitemporal remote sensing image dataset","volume":"21","author":"Munyati","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Seydi, S.T., Akhoondzadeh, M., Amani, M., and Mahdavi, S. (2021). Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform. Remote Sens., 13.","DOI":"10.3390\/rs13020220"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.isprsjprs.2020.01.010","article-title":"The migration of training samples towards dynamic global land cover mapping","volume":"161","author":"Huang","year":"2020","journal-title":"ISPRS J. Photogramm."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.isprsjprs.2020.07.013","article-title":"Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples","volume":"167","author":"Ghorbanian","year":"2020","journal-title":"ISPRS J. Photogramm."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Wang, Y., Liu, J., Li, X., Pan, H., and Jia, M. (2021). Tracking Historical Wetland Changes in the China Side of the Amur River Basin Based on Landsat Imagery and Training Samples Migration. Remote Sens., 13.","DOI":"10.3390\/rs13112161"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"9979","DOI":"10.1038\/s41598-021-89034-5","article-title":"First comprehensive quantification of annual land use\/cover from 1990 to 2020 across mainland Vietnam","volume":"11","author":"Phan","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1007\/s10661-015-4426-5","article-title":"Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features","volume":"187","author":"Szantoi","year":"2015","journal-title":"Environ. Monit. Assess."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.isprsjprs.2015.08.005","article-title":"An object-based approach to delineate wetlands across landscapes of varied disturbance with high spatial resolution satellite imagery","volume":"109","author":"Mui","year":"2015","journal-title":"ISPRS J. Photogramm."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1080\/07038992.2017.1342206","article-title":"Object-Based Classification of Wetlands in Newfoundland and Labrador Using Multi-Temporal PolSAR Data","volume":"43","author":"Mahdavi","year":"2017","journal-title":"Can. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Hu, T., and Smith, R.B. (2018). The Impact of Hurricane Maria on the Vegetation of Dominica and Puerto Rico Using Multispectral Remote Sensing. Remote Sens., 10.","DOI":"10.3390\/rs10060827"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4173","DOI":"10.3390\/rs6054173","article-title":"Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery","volume":"6","author":"Rokni","year":"2014","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3220","DOI":"10.1016\/j.rse.2011.07.006","article-title":"Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China","volume":"115","author":"Dronova","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hakdaoui, S., Emran, A., Pradhan, B., Lee, C., and Fils, S.C.N. (2019). A Collaborative Change Detection Approach on Multi-Sensor Spatial Imagery for Desert Wetland Monitoring after a Flash Flood in Southern Morocco. Remote Sens., 9.","DOI":"10.3390\/rs11091042"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.2112\/JCOASTRES-D-12-00237.1","article-title":"Remote Sensing of Coastal Wetland Biomass: An Overview","volume":"29","author":"Klemas","year":"2013","journal-title":"J. Coast. Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.isprsjprs.2017.06.013","article-title":"Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications","volume":"130","author":"Zhu","year":"2017","journal-title":"ISPRS J. Photogramm."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1080\/0143116031000101675","article-title":"Digital change detection methods in ecosystem monitoring: A review","volume":"25","author":"Coppin","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"6071","DOI":"10.1080\/01431161.2010.507793","article-title":"Histogram thresholding for unsupervised change detection of remote sensing images","volume":"32","author":"Patra","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Behnamian, A., Banks, S., White, L., Brisco, B., Millard, K., Pasher, J., Chen, Z., Duffe, J., Bourgeau-Chavez, L., and Battaglia, M. (2017). Semi-Automated Surface Water Detection with Synthetic Aperture Radar Data: A Wetland Case Study. Remote Sens., 9.","DOI":"10.3390\/rs9121209"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.2112\/JCOASTRES-D-14-00159.1","article-title":"Spatiotemporal Analysis of Shoreline Changes of the Kizilirmak Delta","volume":"31","author":"Ozturk","year":"2015","journal-title":"J. Coast. Res."},{"key":"ref_58","unstructured":"Lotfi, A. (2016). Shadegan Wetland (Islamic Republic of Iran). The Wetland Book, Springer. Available online: https:\/\/link.springer.com\/content\/pdf\/bfm%3A978-94-007-4001-3%2F1.pdf."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"111569","DOI":"10.1016\/j.marpolbul.2020.111569","article-title":"Spatial distribution, ecological and health risk assessment and source identification of atrazine in Shadegan international wetland, Iran","volume":"160","author":"Almasi","year":"2020","journal-title":"Mar. Pollut. Bull."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2011.09.026","article-title":"Sentinels for science: Potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land","volume":"120","author":"Rott","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_61","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_62","doi-asserted-by":"crossref","unstructured":"Stromann, O., Nascetti, A., Yousif, O., and Ban, Y. (2020). Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12010076"},{"key":"ref_63","first-page":"103","article-title":"SAR polarimetric change detection for flooded vegetation","volume":"6","author":"Brisco","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_64","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."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s10661-017-6399-z","article-title":"An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme","volume":"190","author":"Kovacs","year":"2018","journal-title":"Environ. Monit. Assess."},{"key":"ref_66","first-page":"102083","article-title":"Mapping vegetation communities inside wetlands using Sentinel-2 imagery in ireland","volume":"88","author":"Bhatnagar","year":"2020","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.ecolind.2013.04.009","article-title":"Monitoring temporal dynamics of Great Artesian Basin wetland vegetation, Australia, using MODIS NDVI","volume":"34","author":"Petus","year":"2013","journal-title":"Ecol. Indic."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"14853","DOI":"10.3390\/rs71114853","article-title":"Detection and Delineation of Localized Flooding from World View-2 Multispectral Data","volume":"7","author":"Malinowski","year":"2015","journal-title":"Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2014.10.027","article-title":"Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification","volume":"158","author":"Dronova","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"794","DOI":"10.3390\/w7020794","article-title":"Target Detection Method for Water Mapping Using Landsat 8 OLI\/TIRS Imagery","volume":"7","author":"Ji","year":"2015","journal-title":"Water"},{"key":"ref_71","first-page":"1054","article-title":"Presence of polycyclic aromatic hydrocarbons in sediments and surface water from Shadegan wetland\u2014Iran: A focus on source apportionment, human and ecological risk assessment and Sediment-Water Exchange","volume":"148","author":"Ashayeri","year":"2018","journal-title":"Ecotoxicology"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s10661-016-5117-6","article-title":"Assessment of bed sediment metal contamination in the Shadegan and Hawr Al Azim wetlands, Iran","volume":"188","author":"Nasirian","year":"2016","journal-title":"Environ. Monit. Assess."},{"key":"ref_73","first-page":"294","article-title":"Concentration of polychlorinated biphenyls and organochlorine pesticides in some edible fish species from the Shadegan Marshes (Iran)","volume":"74","author":"Davodi","year":"2011","journal-title":"Ecotoxicology"},{"key":"ref_74","first-page":"457","article-title":"Biological Characteristics, Growth Parameters and Mortality Rate of Carassius auratus in the Shadegan Wetland (Iran)","volume":"13","author":"Hashemi","year":"2019","journal-title":"Int. J. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1007\/s00128-010-9933-z","article-title":"Mercury Pollution in Three Species of Waders from Shadegan Wetlands at the Head of the Persian Gulf","volume":"84","author":"Savabieasfahani","year":"2010","journal-title":"Bull. Environ. Contam. Toxicol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1007\/s10646-008-0284-z","article-title":"Mercury levels in selected tissues of three kingfisher species; Ceryle rudis, Alcedo atthis, and Halcyon smyrnensi, from Shadegan Marshes of Iran","volume":"18","author":"Ghasempouri","year":"2009","journal-title":"Ecotoxicology"},{"key":"ref_77","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_78","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_79","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.ecolind.2018.10.049","article-title":"Application of the water-related spectral reflectance indices: A review","volume":"98","author":"Ma","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_81","first-page":"204","article-title":"Analyzing fine-scale wetland composition using high resolution imagery and texture features","volume":"23","author":"Szantoi","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Wang, M., Fei, X., Zhang, Y., Chen, Z., Wang, X., Tsou, J.Y., Liu, D., and Lu, X. (2018). Assessing Texture Features to Classify Coastal Wetland Vegetation from High Spatial Resolution Imagery Using Completed Local Binary Patterns (CLBP). Remote Sens., 10.","DOI":"10.3390\/rs10050778"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.3390\/w5031036","article-title":"Wetland Monitoring Using the Curvelet-Based Change Detection Method on Polarimetric SAR Imagery","volume":"5","author":"Schmitt","year":"2013","journal-title":"Water"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4169\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:17:15Z","timestamp":1760167035000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4169"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,18]]},"references-count":83,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13204169"],"URL":"https:\/\/doi.org\/10.3390\/rs13204169","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,18]]}}}