{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T02:17:30Z","timestamp":1774664250568,"version":"3.50.1"},"reference-count":108,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T00:00:00Z","timestamp":1662595200000},"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>Within water resources management, surface water area (SWA) variation plays a vital role in hydrological processes as well as in agriculture, environmental ecosystems, and ecological processes. The monitoring of long-term spatiotemporal SWA changes is even more critical within highly populated regions that have an arid or semi-arid climate, such as Iran. This paper examined variations in SWA in Iran from 1990 to 2021 using about 18,000 Landsat 5, 7, and 8 satellite images through the Google Earth Engine (GEE) cloud processing platform. To this end, the performance of twelve water mapping rules (WMRs) within remotely-sensed imagery was also evaluated. Our findings revealed that (1) methods which provide a higher separation (derived from transformed divergence (TD) and Jefferies\u2013Matusita (JM) distances) between the two target classes (water and non-water) result in higher classification accuracy (overall accuracy (OA) and user accuracy (UA) of each class). (2) Near-infrared (NIR)-based WMRs are more accurate than short-wave infrared (SWIR)-based methods for arid regions. (3) The SWA in Iran has an overall downward trend (observed by linear regression (LR) and sequential Mann\u2013Kendall (SQMK) tests). (4) Of the five major water basins, only the Persian Gulf Basin had an upward trend. (5) While temperature has trended upward, the precipitation and normalized difference vegetation index (NDVI), a measure of the country\u2019s greenness, have experienced a downward trend. (6) Precipitation showed the highest correlation with changes in SWA (r = 0.69). (7) Long-term changes in SWA were highly correlated (r = 0.98) with variations in the JRC world water map.<\/jats:p>","DOI":"10.3390\/rs14184491","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T20:50:27Z","timestamp":1662670227000},"page":"4491","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Monitoring Long-Term Spatiotemporal Changes in Iran Surface Waters Using Landsat Imagery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2588-5429","authenticated-orcid":false,"given":"Alireza","family":"Taheri Dehkordi","sequence":"first","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, K. N. Toosi University of Technology, Tehran 19967-15433, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4325-8741","authenticated-orcid":false,"given":"Mohammad Javad","family":"Valadan Zoej","sequence":"additional","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, K. N. Toosi University of Technology, Tehran 19967-15433, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4742-2628","authenticated-orcid":false,"given":"Hani","family":"Ghasemi","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran"}]},{"given":"Mohsen","family":"Jafari","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Shiraz University, Shiraz 71496-84334, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4187-5669","authenticated-orcid":false,"given":"Ali","family":"Mehran","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, San Jose State University, San Jose, CA 95192, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2015.10.005","article-title":"Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach","volume":"171","author":"Yang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Dong, J., Xiao, X., Xiao, T., Yang, Z., Zhao, G., Zou, Z., and Qin, Y. (2017). Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors. Water, 9.","DOI":"10.3390\/w9040256"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dehkordi, A.T., Zoej, M.J.V., Ghasemi, H., Ghaderpour, E., and Hassan, Q.K. (2022). A New Clustering Method to Generate Training Samples for Supervised Monitoring of Long-Term Water Surface Dynamics Using Landsat Data through Google Earth Engine. Sustainability, 14.","DOI":"10.3390\/su14138046"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1029\/2018EF001091","article-title":"Adaptation to Future Water Shortages in the United States Caused by Population Growth and Climate Change","volume":"7","author":"Brown","year":"2019","journal-title":"Earth\u2019s Future"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105030","DOI":"10.1016\/j.envsoft.2021.105030","article-title":"Augmented Normalized Difference Water Index for improved surface water monitoring","volume":"140","author":"Rad","year":"2021","journal-title":"Environ. Model. Softw."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, J., Kang, T., Yang, S., Bu, J., Cao, K., and Gao, Y. (2020). Open-Surface Water Bodies Dynamics Analysis in the Tarim River Basin (North-Western China), Based on Google Earth Engine Cloud Platform. Water, 12.","DOI":"10.3390\/w12102822"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Deng, Y., Jiang, W., Tang, Z., Ling, Z., and Wu, Z. (2019). Long-Term Changes of Open-Surface Water Bodies in the Yangtze River Basin Based on the Google Earth Engine Cloud Platform. Remote Sens., 11.","DOI":"10.3390\/rs11192213"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"115057","DOI":"10.1016\/j.jenvman.2022.115057","article-title":"A review on the research progress of lake water volume estimation methods","volume":"314","author":"An","year":"2022","journal-title":"J. Environ. Manag."},{"key":"ref_9","first-page":"135","article-title":"Monitoring the dynamics of surface water fraction from MODIS time series in a Mediterranean environment","volume":"66","author":"Li","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf. ITC J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1080\/02626668909491360","article-title":"Role of satellite remote sensing for monitoring of surface water resources in an arid environment","volume":"34","author":"Sharma","year":"1989","journal-title":"Hydrol. Sci. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.scitotenv.2019.06.341","article-title":"Continuous monitoring of lake dynamics on the Mongolian Plateau using all available Landsat imagery and Google Earth Engine","volume":"689","author":"Zhou","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.isprsjprs.2014.03.001","article-title":"Remote sensing of alpine lake water environment changes on the Tibetan Plateau and surroundings: A review","volume":"92","author":"Song","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gholizadeh, M.H., Melesse, A.M., and Reddi, L. (2016). A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques. Sensors, 16.","DOI":"10.3390\/s16081298"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Dehkordi, A.T., Ghasemi, H., and Zoej, M.J.V. (2021, January 29\u201330). Machine Learning-Based Estimation of Suspended Sediment Concentration along Missouri River using Remote Sensing Imageries in Google Earth Engine. Proceedings of the 2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS), Online.","DOI":"10.1109\/ICSPIS54653.2021.9729382"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"9724","DOI":"10.1029\/2017WR022437","article-title":"Satellite remote sensing for water resources management: Potential for supporting sustainable development in data-poor regions","volume":"54","author":"Sheffield","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Toure, S., Diop, O., Kpalma, K., and Maiga, A.S. (2019). Shoreline Detection using Optical Remote Sensing: A Review. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8020075"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Domeneghetti, A., Schumann, G.J.-P., and Tarpanelli, A. (2019). Preface: Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics. Remote Sens., 11.","DOI":"10.3390\/rs11080943"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dietz, A.J., Klein, I., Gessner, U., Frey, C.M., Kuenzer, C., and Dech, S. (2017). Detection of Water Bodies from AVHRR Data\u2014A TIMELINE Thematic Processor. Remote Sens., 9.","DOI":"10.3390\/rs9010057"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Boschetti, M., Nutini, F., Manfron, G., Brivio, P.A., and Nelson, A. (2014). Comparative Analysis of Normalised Difference Spectral Indices Derived from MODIS for Detecting Surface Water in Flooded Rice Cropping Systems. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0088741"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bioresita, F., Puissant, A., Stumpf, A., and Malet, J.-P. (2018). A Method for Automatic and Rapid Mapping of Water Surfaces from Sentinel-1 Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10020217"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111803","DOI":"10.1016\/j.rse.2020.111803","article-title":"Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data","volume":"244","author":"Yang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ou, C., Yang, J., Du, Z., Liu, Y., Feng, Q., and Zhu, D. (2019). Long-Term Mapping of a Greenhouse in a Typical Protected Agricultural Region Using Landsat Imagery and the Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12010055"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2017.02.021","article-title":"Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine","volume":"202","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xu, H., Wei, Y., Liu, C., Li, X., and Fang, H. (2019). A Scheme for the Long-Term Monitoring of Impervious\u2212Relevant Land Disturbances Using High Frequency Landsat Archives and the Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11161891"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2754","DOI":"10.1109\/JSTARS.2021.3058421","article-title":"Improved Mapping of Long-Term Forest Disturbance and Recovery Dynamics in the Subtropical China Using All Available Landsat Time-Series Imagery on Google Earth Engine Platform","volume":"14","author":"Hua","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. 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":"111559","DOI":"10.1016\/j.jenvman.2020.111559","article-title":"Visualisation of flooding along an unvegetated, ephemeral river using Google Earth Engine: Implications for assessment of channel-floodplain dynamics in a time of rapid environmental change","volume":"278","author":"Li","year":"2020","journal-title":"J. Environ. Manag."},{"key":"ref_28","first-page":"111617","article-title":"Extrapolating canopy phenology information using Sentinel-2 data and the Google Earth Engine platform to identify the optimal dates for remotely sensed image acquisition of semiarid mangroves","volume":"279","author":"Kovacs","year":"2020","journal-title":"J. Environ. Manag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/LGRS.2019.2920225","article-title":"RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction From Remotely Sensed Imagery","volume":"17","author":"Yang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, Q., Qiu, C., Ma, L., Schmitt, M., and Zhu, X.X. (2020). Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12040602"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xia, H., Zhao, J., Qin, Y., Yang, J., Cui, Y., Song, H., Ma, L., Jin, N., and Meng, Q. (2019). Changes in Water Surface Area during 1989\u20132017 in the Huai River Basin using Landsat Data and Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11151824"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dehkordi, A.T., Beirami, B.A., Zoej, M.J.V., and Mokhtarzade, M. (2021, January 28\u201329). Performance Evaluation of Temporal and Spatial-Temporal Convolutional Neural Networks for Land-Cover Classification (A Case Study in Shahrekord, Iran). Proceedings of the 2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA), Kashan, Iran.","DOI":"10.1109\/IPRIA53572.2021.9483498"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5530","DOI":"10.3390\/rs5115530","article-title":"A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI","volume":"5","author":"Li","year":"2013","journal-title":"Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, R., Xia, H., Qin, Y., Niu, W., Pan, L., Li, R., Zhao, X., Bian, X., and Fu, P. (2020). Dynamic Monitoring of Surface Water Area during 1989\u20132019 in the Hetao Plain Using Landsat Data in Google Earth Engine. Water, 12.","DOI":"10.3390\/w12113010"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3153","DOI":"10.1080\/01431160500309934","article-title":"A water index for rapid mapping of shoreline changes of five East African Rift Valley lakes: An empirical analysis using Landsat TM and ETM+ data","volume":"27","author":"Ouma","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.rse.2013.08.029","article-title":"Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery","volume":"140","author":"Feyisa","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s10661-019-7355-x","article-title":"An automatic water detection approach using Landsat 8 OLI and Google Earth Engine cloud computing to map lakes and reservoirs in New Zealand","volume":"191","author":"Nguyen","year":"2019","journal-title":"Environ. Monit. Assess."},{"key":"ref_40","unstructured":"Danaher, T., and Collett, L. (2006, January 20\u201324). Development, optimisation and multi-temporal application of a simple Landsat based water index. Proceedings of the 13th Australasian Remote Sensing and Photogrammetry Conference, Canberra, ACT, Australia."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.rse.2015.12.055","article-title":"Comparing Landsat water index methods for automated water classification in eastern Australia","volume":"175","author":"Fisher","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_42","unstructured":"Menarguez, M. (2015). Global Water Body Mapping from 1984 to 2015 Using Global High Resolution Multispectral Satellite Imagery, University of Oklahoma."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/0034-4257(85)90102-6","article-title":"A TM Tasseled Cap equivalent transformation for reflectance factor data","volume":"17","author":"Crist","year":"1985","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1109\/TGRS.1984.350619","article-title":"A Physically-Based Transformation of Thematic Mapper Data\u2014The TM Tasseled Cap","volume":"GE-22","author":"Crist","year":"1984","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1080\/01431160110106113","article-title":"Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance","volume":"23","author":"Huang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","unstructured":"Liu, Q., Liu, G., Huang, C., Liu, S., and Zhao, J. (2014, January 13\u201318). A tasseled cap transformation for Landsat 8 OLI TOA reflectance images. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1080\/2150704X.2014.915434","article-title":"Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance","volume":"5","author":"Baig","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1080\/01431161.2014.995274","article-title":"Comparison of tasselled cap transformations based on the selective bands of Landsat 8 OLI TOA reflectance images","volume":"36","author":"Liu","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4417","DOI":"10.1109\/JSTARS.2017.2719029","article-title":"Surface water extraction from Landsat 8 OLI imagery using the LBV transformation","volume":"10","author":"Zhang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4095","DOI":"10.1080\/01431160601028912","article-title":"A new method of data transformation for satellite images: I. Methodology and transformation equations for TM images","volume":"28","author":"Zeng","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Labuzzetta, C., Zhu, Z., Chang, X., and Zhou, Y. (2021). A Submonthly Surface Water Classification Framework via Gap-Fill Imputation and Random Forest Classifiers of Landsat Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13091742"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4349","DOI":"10.5194\/hess-22-4349-2018","article-title":"Surface water monitoring in small water bodies: Potential and limits of multi-sensor Landsat time series","volume":"22","author":"Ogilvie","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"149348","DOI":"10.1016\/j.scitotenv.2021.149348","article-title":"Retrieving dynamics of the surface water extent in the upper reach of Yellow River","volume":"800","author":"Zhou","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Herndon, K., Muench, R., Cherrington, E., and Griffin, R. (2020). An Assessment of Surface Water Detection Methods for Water Resource Management in the Nigerien Sahel. Sensors, 20.","DOI":"10.3390\/s20020431"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s10661-010-1686-y","article-title":"Changes in the area of inland lakes in arid regions of central Asia during the past 30 years","volume":"178","author":"Bai","year":"2010","journal-title":"Environ. Monit. Assess."},{"key":"ref_57","unstructured":"Tosan System Company TSCO (2019). Iran Statistical Yearbook 1397 (2018\u20132019), TSCO."},{"key":"ref_58","unstructured":"Jarvis, A., Reuter, H.I., Nelson, A., and Guevara, E. (2022, May 01). Hole-Filled SRTM for the Globe Version 4. The CGIAR-CSI SRTM 90m Database. Available online: http:\/\/srtm.csi.cgiar.org."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s13412-014-0182-z","article-title":"Water management in Iran: What is causing the looming crisis?","volume":"4","author":"Madani","year":"2014","journal-title":"J. Environ. Stud. Sci."},{"key":"ref_60","unstructured":"McNally, A. (2020, May 01). FLDAS noah land surface model L4 global monthly 0.1 \u00d7 0.1 degree (MERRA-2 and CHIRPS), Atmos. Compos. Water Energy Cycles Clim. Var., Available online: https:\/\/disc.gsfc.nasa.gov\/datasets\/FLDAS_NOAH01_C_GL_M_001\/summary."},{"key":"ref_61","unstructured":"Vermote, E., Justice, C., Csiszar, I., Eidenshink, J., Myneni, R.B., Baret, F., Masuoka, E., Wolfe, R.E., and Claverie, M. (2022, May 01). NOAA Climate Data Record (CDR) of Normalized Difference Vegetation Index (NDVI), Version 4. NOAA National Centers for Environmental Information. Available online: https:\/\/doi.org\/10.7289\/v5pz56r6."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1109\/JSTARS.2020.2971783","article-title":"An urban water extraction method combining deep learning and Google Earth engine","volume":"13","author":"Wang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8\/OLI land surface reflectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat surface reflectance dataset for North America, 1990\u20132000","volume":"3","author":"Masek","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Deng, Y., Jiang, W., Tang, Z., Li, J., Lv, J., Chen, Z., and Jia, K. (2017). Spatio-Temporal Change of Lake Water Extent in Wuhan Urban Agglomeration Based on Landsat Images from 1987 to 2015. Remote Sens., 9.","DOI":"10.3390\/rs9030270"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2018.09.016","article-title":"Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery","volume":"219","author":"Yang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_67","unstructured":"Commission, E., Centre, J.R., Soille, P., Halkia, M., Freire, S., Ferri, S., Julea, A., Pesaresi, M., Kemper, T., and Ehrlich, D. (2016). Operating Procedure for the Production of the Global Human Settlement Layer from Landsat Data of the Epochs 1975, 1990, 2000, and 2014, Publications Office of the European Union."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"5194","DOI":"10.1080\/01431161.2012.657370","article-title":"An automated scheme for glacial lake dynamics mapping using Landsat imagery and digital elevation models: A case study in the Himalayas","volume":"33","author":"Li","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., and Roth, L. (2007). The shuttle radar topography mission. Rev. Geophys., 45.","DOI":"10.1029\/2005RG000183"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"8979","DOI":"10.1080\/01431161.2019.1624867","article-title":"SRTM DEM enhancement using a single set of PolSAR data based on the polarimetry-clinometry model","volume":"40","author":"Jafari","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Kokaly, R., Clark, R., Swayze, G., Livo, K., Hoefen, T., Pearson, N., Wise, R., Benzel, W., Lowers, H., and Driscoll, R. (2017). Usgs Spectral Library Version 7 Data: Us Geological Survey Data Release, United States Geological Survey (USGS).","DOI":"10.3133\/ds1035"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4\u20137, 8, and Sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J.C., Mathis, M., and Brumby, S.P. (2021, January 11\u201316). Global land use\/land cover with Sentinel 2 and deep learning. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553499"},{"key":"ref_74","unstructured":"Van De Kerchove, R., Zanaga, D., De Keersmaecker, W., Souverijns, N., Wevers, J., Brockmann, C., Grosu, A., Paccini, A., Cartus, O., and Santoro, M. (2021, January 13\u201317). ESA WorldCover: Global land cover mapping at 10 m resolution for 2020 based on Sentinel-1 and 2 data. Proceedings of the AGU Fall Meeting 2021, New Orleans, LA, USA."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Carrasco, L., O\u2019Neil, A.W., Morton, R.D., and Rowland, C.S. (2019). Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11030288"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Safanelli, J.L., Poppiel, R.R., Ruiz, L.F.C., Bonfatti, B.R., Mello, F.A.D.O., Rizzo, R., and Dematt\u00ea, J.A.M. (2020). Terrain Analysis in Google Earth Engine: A Method Adapted for High-Performance Global-Scale Analysis. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9060400"},{"key":"ref_77","unstructured":"Jensen, J.R. (1986). Introductory Digital Image processing: A Remote Sensing Perspective, University of South Carolina."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Richards, J.A., and Richards, J. (1999). Remote Sensing Digital Image Analysis, Springer.","DOI":"10.1007\/978-3-662-03978-6"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1080\/2150704X.2019.1708501","article-title":"Surface water map of China for 2015 (SWMC-2015) derived from Landsat 8 satellite imagery","volume":"11","author":"Jiang","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"238","DOI":"10.2166\/wcc.2019.078","article-title":"A simple, robust, and automatic approach to extract water body from Landsat images (case study: Lake Urmia, Iran)","volume":"12","author":"Babaei","year":"2019","journal-title":"J. Water Clim. Chang."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1080\/2150704X.2020.1757780","article-title":"Combined use of Sentinel-2 and Landsat 8 to monitor water surface area dynamics using Google Earth Engine","volume":"11","author":"Yang","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/BF02295996","article-title":"Note on the sampling error of the difference between correlated proportions or percentages","volume":"12","author":"McNemar","year":"1947","journal-title":"Psychometrika"},{"key":"ref_84","unstructured":"Sneyers, R. (1990). On the Statistical Analysis of Series of Observations, World Meteorological Society."},{"key":"ref_85","first-page":"4","article-title":"A review of the Environmental Impact of Large Dams in Iran","volume":"1","author":"Heydari","year":"2013","journal-title":"Int. J. Adv. Civ. Struct. Environ. Eng. IJACSE"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"139857","DOI":"10.1016\/j.scitotenv.2020.139857","article-title":"Analyzing the Lake Urmia restoration progress using ground-based and spaceborne observations","volume":"739","author":"Saemian","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s40068-019-0135-3","article-title":"An overview of climate change in Iran: Facts and statistics","volume":"8","author":"Daneshvar","year":"2019","journal-title":"Environ. Syst. Res."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1002\/hyp.6355","article-title":"An assessment of water reserve changes in Salt Lake, Turkey, through multi-temporal Landsat imagery and real-time ground surveys","volume":"21","author":"Ormeci","year":"2007","journal-title":"Hydrol. Process. Int. J."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"106052","DOI":"10.1016\/j.atmosres.2022.106052","article-title":"Detecting drought events over Iran during 1983\u20132017 using satellite and ground-based precipitation observations","volume":"269","author":"Kazemzadeh","year":"2022","journal-title":"Atmos. Res."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Hu, Q., Li, C., Wang, Z., Liu, Y., and Liu, W. (2022). Continuous Monitoring of the Surface Water Area in the Yellow River Basin during 1986\u20132019 Using Available Landsat Imagery and the Google Earth Engine. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11050305"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1007\/s00704-021-03853-0","article-title":"Spatial assessment of drought features over different climates and seasons across Iran","volume":"147","author":"Sharafi","year":"2021","journal-title":"Theor. Appl. Climatol."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.agwat.2018.06.003","article-title":"Irrigation water management in Iran: Implications for water use efficiency improvement","volume":"208","author":"Nazari","year":"2018","journal-title":"Agric. Water Manag."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"113683","DOI":"10.1016\/j.jenvman.2021.113683","article-title":"Application of remote sensing and geographic information systems in irrigation water management under water scarcity conditions in Fayoum, Egypt","volume":"299","author":"Abdelhaleem","year":"2021","journal-title":"J. Environ. Manag."},{"key":"ref_94","unstructured":"Abrishamchi, A., and Tajrishi, M. (2005). Interbasin water transfer in Iran. Water Conservation, Reuse, and Recycling: Proceeding of an Iranian American Workshop, National Academies Press."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1016\/j.rser.2015.04.009","article-title":"Solar desalination: A sustainable solution to water crisis in Iran","volume":"48","author":"Gorjian","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Bates, B., Kundzewicz, Z., and Wu, S. (2008). Climate Change and Water, Intergovernmental Panel on Climate Change Secretariat.","DOI":"10.1017\/CBO9780511546013"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"7450","DOI":"10.1038\/s41598-020-64089-y","article-title":"Variability and change in the hydro-climate and water resources of Iran over a recent 30-year period","volume":"10","author":"Panahi","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"2166","DOI":"10.1007\/s42452-020-03964-9","article-title":"Extreme weather events related to climate change: Widespread flooding in Iran, March\u2013April 2019","volume":"2","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_99","unstructured":"AghaKouchak, A., Mehran, A., and Mazdiyasni, O. (2016, January 17\u201322). Socioeconomic Drought in a Changing Climate: Modeling and Management. Proceedings of the EGU General Assembly Conference Abstracts, Vienna, Austria."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"7520","DOI":"10.1002\/2015JD023147","article-title":"A hybrid framework for assessing socioeconomic drought: Linking climate variability, local resilience, and demand","volume":"120","author":"Mehran","year":"2015","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"3485","DOI":"10.1007\/s11269-011-9867-1","article-title":"Drought Monitoring by Reconnaissance Drought Index (RDI) in Iran","volume":"25","author":"Zarch","year":"2011","journal-title":"Water Resour. Manag."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1080\/00210862.2016.1259286","article-title":"Iran\u2019s Socio-economic Drought: Challenges of a Water-Bankrupt Nation","volume":"49","author":"Madani","year":"2016","journal-title":"Iran. Stud."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s12524-015-0501-1","article-title":"A New Component Scattering Model Using Polarimetric Signatures Based Pattern Recognition on Polarimetric SAR Data","volume":"44","author":"Jafari","year":"2016","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"5430","DOI":"10.1080\/01431161.2017.1341667","article-title":"A weighted normalized difference water index for water extraction using Landsat imagery","volume":"38","author":"Guo","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1080\/22797254.2017.1297540","article-title":"Object-based water body extraction model using Sentinel-2 satellite imagery","volume":"50","author":"Kaplan","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_107","unstructured":"Esfahani, M.M., and Sadati, H. (2022, January 2\u20133). Application of NSGA-II in Channel Selection of Motor Imagery EEG Signals with Common Spatio-Spectral Patterns in BCI Systems. Proceedings of the 8th International Conference on Control, Instrumentation and Automation (ICCIA), Tehran, Iran."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1016\/j.asr.2022.05.038","article-title":"Early identification of crop types using Sentinel-2 satellite images and an incremental multi-feature ensemble method (Case study: Shahriar, Iran)","volume":"70","author":"Rahmati","year":"2022","journal-title":"Adv. Space Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4491\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:27:57Z","timestamp":1760142477000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4491"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,8]]},"references-count":108,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14184491"],"URL":"https:\/\/doi.org\/10.3390\/rs14184491","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,8]]}}}