{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T06:25:14Z","timestamp":1783664714193,"version":"3.55.0"},"reference-count":76,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T00:00:00Z","timestamp":1645833600000},"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>Mapping surface water extent is important for managing water supply for agriculture and the environment. Remote sensing technologies, such as Landsat, provide an affordable means of capturing surface water extent with reasonable spatial and temporal coverage suited to this purpose. Many methods are available for mapping surface water including the modified Normalised Difference Water Index (mNDWI), Fisher\u2019s water index (FWI), Water Observations from Space (WOfS), and the Tasseled Cap Wetness index (TCW). While these methods can discriminate water, they have their strengths and weaknesses, and perform at their best in different environments, and with different threshold values. This study combines the strengths of these indices by developing rules that applies an index to the environment where they perform best. It compares these indices across the Murray-Darling Basin (MDB) in southeast Australia, to assess performance and compile a heuristic rule set for accurate application across the MDB. The results found that all single indices perform well with the Kappa statistic showing strong agreement, ranging from 0.78 for WOfS to 0.84 for TCW (with threshold \u22120.035), with improvement in the overall output when the index best suited for an environment was selected. mNDWI (using a threshold of \u22120.3) works well within river channels, while TCW (with threshold \u22120.035) is best for wetlands and flooded vegetation. FWI and mNDWI (with threshold 0.63 and 0, respectively) work well for remaining areas. Selecting the appropriate index for an environment increases the overall Kappa statistic to 0.88 with a water pixel accuracy of 90.5% and a dry pixel accuracy of 94.8%. An independent assessment illustrates the benefit of using the multi-index approach, making it suitable for regional-scale multi-temporal analysis.<\/jats:p>","DOI":"10.3390\/rs14051158","type":"journal-article","created":{"date-parts":[[2022,2,27]],"date-time":"2022-02-27T20:48:33Z","timestamp":1645994913000},"page":"1158","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Development of a Multi-Index Method Based on Landsat Reflectance Data to Map Open Water in a Complex Environment"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8567-1388","authenticated-orcid":false,"given":"Catherine","family":"Ticehurst","sequence":"first","affiliation":[{"name":"CSIRO Land and Water Business Unit, GPO Box 1700, Canberra, ACT 2601, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Teng","sequence":"additional","affiliation":[{"name":"CSIRO Land and Water Business Unit, GPO Box 1700, Canberra, ACT 2601, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashmita","family":"Sengupta","sequence":"additional","affiliation":[{"name":"CSIRO Land and Water Business Unit, GPO Box 1700, Canberra, ACT 2601, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"931","DOI":"10.5194\/hess-15-931-2011","article-title":"Uncertainty in water resources availability in the Okavango River basin as a result of climate change","volume":"15","author":"Hughes","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.gloplacha.2011.10.012","article-title":"A review of historic and future hydrological changes in the Murray\u2013Darling Basin","volume":"80\u201381","author":"Leblanc","year":"2012","journal-title":"Glob. Planet. Change"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1002\/eco.221","article-title":"Extreme flow variability and the \u2018boom and bust\u2019 ecology of fish in arid-zone floodplain rivers: A case history with implications for environmental flows, conservation and management","volume":"4","author":"Arthington","year":"2011","journal-title":"Ecohydrology"},{"key":"ref_4","unstructured":"(2021, August 04). State of the Climate 2020, Available online: http:\/\/www.bom.gov.au\/state-of-the-climate\/."},{"key":"ref_5","unstructured":"Federal Emergency Management Agency (2003). Guidelines and Specifications for Flood Hazard Mapping Partners."},{"key":"ref_6","unstructured":"World Meteorological Organization (2009). Final Report on Flood Hazard Mapping Project, World Meteorological Organization."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.rse.2015.11.003","article-title":"Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia","volume":"174","author":"Mueller","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.envsoft.2014.09.011","article-title":"Streamflow rating uncertainty: Characterisation and impacts on model calibration and performance","volume":"63","author":"Zhang","year":"2015","journal-title":"Environ. Model. Softw."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.jhydrol.2003.12.024","article-title":"Accounting for heteroscedasticity in rating curve estimates","volume":"292","year":"2004","journal-title":"J. Hydrol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Schumann, G.J.-P., Brakenridge, G.R., Kettner, A.J., Kashif, R., and Niebuhr, E. (2018). Assisting flood disaster response with Earth observation data and products: A critical assessment. Remote Sens., 10.","DOI":"10.3390\/rs10081230"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2019.02.015","article-title":"Current status of Landsat program, science, and applications","volume":"225","author":"Wulder","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V.R., Murayama, Y., and Ranagalage, M. (2020). Sentinel-2 data for land cover\/use mapping: A review. Remote Sens., 12.","DOI":"10.3390\/rs12142291"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kordelas, G.A., Manakos, I., Aragon\u00e9s, D., Diaz-Delgado, R., and Bustamante, J. (2018). Fast and automatic data-driven thresholding for inundation mapping with Sentinel-2 data. Remote Sens., 10.","DOI":"10.3390\/rs10060910"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lefebvre, G., Davranche, A., Willm, L., Campagna, J., Redmond, L., Merle, C., Guelmami, A., and Poulin, B. (2019). Introducing WIW for detecting the presence of water in wetlands with Landsat and Sentinel Satellites. Remote Sens., 11.","DOI":"10.3390\/rs11192210"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., and Li, X. (2016). Water bodies\u2019 mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sens., 8.","DOI":"10.3390\/rs8040354"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/1-4020-4902-1_1","article-title":"MODIS-based flood detection, mapping and measurement: The potential for operational hydrological applications","volume":"Volume 72","author":"Brakenridge","year":"2006","journal-title":"Nato Science Series: IV: Earth and Environmental Sciences"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1007\/s13157-013-0439-4","article-title":"An evaluation of MODIS daily and 8-day composite products for floodplain and wetland inundation mapping","volume":"33","author":"Chen","year":"2013","journal-title":"Wetlands"},{"key":"ref_18","unstructured":"Guerschman, J.P., Warren, G., Byrne, G., Lymburner, L., Mueller, N., and Van-Dijk, A. (2011). MODIS-based standing water detection for flood and large reservoir mapping: Algorithm development and applications for the Australian continent, Water for a Healthy Country National Research Flagship Report."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2015.10.014","article-title":"Development of a global ~90 m water body map using multi-temporal Landsat images","volume":"171","author":"Yamazaki","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1080\/17538947.2015.1026420","article-title":"A global, high-resolution (30-m) inland water body dataset for 2000: First results of a topographic\u2013spectral classification algorithm","volume":"9","author":"Feng","year":"2015","journal-title":"Int. J. Digit. Earth"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.rse.2017.02.016","article-title":"Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds","volume":"193","author":"Swenson","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Soulard, C.E., Walker, J.J., and Petrakis, R.E. (2020). Implementation of a surface water extent model in Cambodia using cloud-based remote sensing. Remote Sens., 12.","DOI":"10.3390\/rs12060984"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2016.02.034","article-title":"Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region","volume":"178","author":"Tulbure","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"13763","DOI":"10.3390\/s150613763","article-title":"Classification of potential water bodies using Landsat 8 OLI and a combination of two boosted random forest classifiers","volume":"15","author":"Ko","year":"2015","journal-title":"Sensors"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6854","DOI":"10.1080\/01431161.2012.692829","article-title":"Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery","volume":"33","author":"Sun","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","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_29","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_30","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_31","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_32","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1080\/10106049.2014.965757","article-title":"Evaluation of NDWI and MNDWI for assessment of water logging by integrating digital elevation model and groundwater level","volume":"30","author":"Singh","year":"2015","journal-title":"Geocarto Int."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Dunn, B., Lymburner, L., Newey, V., Hicks, A., and Carey, H. (August, January 28). Developing a tool for wetland characterization using fractional cover, Tasseled Cap Wetness and Water Observations from Space. Proceedings of the IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8897806"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Donchyts, G., Schellekens, J., Winsemius, H., Eisemann, E., and Van de Giesen, N. (2016). A 30 m resolution surface water mask including estimation of positional and thematic differences using Landsat 8, SRTM and OpenStreetMap: A case study in the Murray-Darling Basin, Australia. Remote Sens., 8.","DOI":"10.3390\/rs8050386"},{"key":"ref_36","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_37","doi-asserted-by":"crossref","unstructured":"Acharya, T.D., Subedi, A., and Lee, D.H. (2018). Evaluation of water indices for surface water extraction in a Landsat 8 scene of Nepal. Sensors, 18.","DOI":"10.3390\/s18082580"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.14358\/PERS.75.11.1307","article-title":"Analysis of dynamic thresholds for the Normalized Difference Water Index","volume":"75","author":"Ji","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_39","unstructured":"Sims, N.C., Warren, G., Overton, I.C., Austin, J., Gallant, J., King, D.J., Merrin, L.E., Donohue, R., McVicar, T.R., and Hodgen, M.J. (2014). RiM-FIM Floodplain Inundation Modelling for the Edward-Wakool, Lower Murrumbidgee and Lower Darling River Systems. Report prepared for the Murray-Darling Basin Authority, Water for a Healthy Country Flagship."},{"key":"ref_40","unstructured":"Sims, N., Anstee, J., Barron, O., Botha, E., Lehmann, E., Li, L., McVicar, T., Paget, M., Ticehurst, C., and Van Niel, T. (2016). Earth observation remote sensing, A Technical Report to the Australian Government from the CSIRO Northern Australia Water Resource Assessment, Part of the National Water Infrastructure Development Fund: Water Resource Assessments."},{"key":"ref_41","unstructured":"Karim, F., Pe\u00f1a-Arancibia, J., Ticehurst, C., Marvanek, S., Gallant, J., Hughes, J., Dutta, D., Vaze, J., Petheram, C., and Seo, L. (2018). Floodplain inundation mapping and modelling for the Fitzroy, Darwin and Mitchell catchments, A Technical Report to the Australian Government from the CSIRO Northern Australia Water Resource Assessment, Part of the National Water Infrastructure Development Fund: Water Resource Assessments."},{"key":"ref_42","unstructured":"Dutta, D., Vaze, J., Karim, F., Kim, S., Mateo, C., Ticehurst, C., Teng, J., Marvanek, S., Gallant, J., and Austin, J. (2016). Floodplain Inundation Mapping and Modelling in the Northern Regions, the Murray Darling Basin, Land and Water."},{"key":"ref_43","unstructured":"Vaze, J., Mateo, C.M., Kim, S., Marvanek, S., Ticehurst, C., Wang, B., Gallant, J., Crosbie, R.S., and Holland, K.L. (2021). Floodplain inundation modelling for the Cooper basin, Australia, Geological and Bioregional Assessment Program: Stage 3. Department of the Environment and Energy, Bureau of Meteorology, CSIRO and Geoscience Australia."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"12503","DOI":"10.3390\/rs70912503","article-title":"Efficient wetland surface water detection and monitoring via Landsat: Comparison with in situ data from the Everglades Depth Estimation Network","volume":"7","author":"Jones","year":"2015","journal-title":"Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1080\/10095020.2015.1017911","article-title":"Comparison of surface water extraction performances of different classic water indices using OLI and TM imageries in different situations","volume":"18","author":"Zhai","year":"2015","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_46","unstructured":"Ticehurst, C., Dutta, D., Karim, F., and Vaze, J. (2017, January 3\u20138). Validation of surface water maps in selected Australian floodplains derived from Landsat imagery using hydrodynamic modelling. Proceedings of the 22nd International Congress on Modelling and Simulation, Hobart, Australia. Available online: https:\/\/www.mssanz.org.au\/modsim2017."},{"key":"ref_47","unstructured":"National Water Account (2021, August 05). Murray\u2013Darling Basin: Geographic Information, Available online: http:\/\/www.bom.gov.au\/water\/nwa\/2020\/mdb\/regiondescription\/geographicinformation.shtml#geographic_information."},{"key":"ref_48","unstructured":"(2022, January 09). Ramsar 2022 Ramsar. Available online: https:\/\/ramsar.org\/."},{"key":"ref_49","unstructured":"(2021, August 06). The Murray\u2013Darling Basin and Why Its Important, Available online: https:\/\/www.mdba.gov.au\/importance-murray-darling-basin."},{"key":"ref_50","unstructured":"CSIRO (2008). Water Availability in the Murray-Darling Basin. A report to the Australian Government from the CSIRO Murray-Darling Basin Sustainable Yields Project."},{"key":"ref_51","unstructured":"(2022, January 09). Issues Facing the Murray\u2013Darling Basin, Available online: https:\/\/www.mdba.gov.au\/issues-murray-darling-basin."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1080\/20964471.2017.1402490","article-title":"Digital earth Australia\u2014Unlocking new value from earth observation data","volume":"1","author":"Dhu","year":"2017","journal-title":"Big Earth Data"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.rse.2017.03.015","article-title":"The Australian Geoscience Data Cube\u2014Foundations and lessons learned","volume":"202","author":"Lewis","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_54","unstructured":"(2021, August 05). Digital Earth Australia, Available online: https:\/\/www.ga.gov.au\/dea\/home."},{"key":"ref_55","unstructured":"(2021, August 05). National Computational Infrastructure Australia. Available online: https:\/\/nci.org.au\/."},{"key":"ref_56","unstructured":"Australian Government, Bureau of Meteorology (2015). Australia Hydrological Geospatial Fabric (Geofabric) Product Guide, Version 3, Bureau of Meteorology Report."},{"key":"ref_57","unstructured":"Atkinson, R., Power, R., Lemon, D., O\u2019Hagan, R., Dovey, D., and Kinny, D. (2008). The Australian hydrological geospatial fabric\u2014Development methodology and conceptual architecture, Water for a Healthy Country."},{"key":"ref_58","unstructured":"Brooks, S., Cottingham, P., Butcher, R., and Hale, J. (2014). Murray-Darling Basin Aquatic Ecosystem Classification: Stage 2 Report, Peter Cottingham & Associates Report to the Commonwealth Environmental Water Office and Murray-Darling Basin Authority."},{"key":"ref_59","unstructured":"Brooks, S. (2017). ANAE Classification of the Murray-Darling Basin v2.0."},{"key":"ref_60","unstructured":"Vaze, J., Mateo, C.M., Kim, S., Marvanek, S., Keogh, A., Ticehurst, C., Teng, J., Gallant, J., Austin, J., and Karim, F. (2018). Floodplain Inundation Modelling for the Edward-Wakool Region, Land and Water."},{"key":"ref_61","unstructured":"(2021, August 05). Interim Classification of Aquatic Ecosystems in the Murray Darling Basin Based on the Australian National Aquatic Ecosystems (ANAE) Classification Framework\u2014Wetlands, Available online: http:\/\/www.environment.gov.au\/fed\/catalog\/search\/resource\/details.page?uuid=%7B20B5D7C5-E3D1-47EB-888E-F23940374393%7D."},{"key":"ref_62","first-page":"397","article-title":"Accuracy Assessment: A User\u2019s perspective","volume":"52","author":"Story","year":"1986","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The measurement of observer agreement for categorical data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"key":"ref_64","unstructured":"Jensen, J.R. (2005). An Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice Hall."},{"key":"ref_65","unstructured":"MDBA (Murray Darling Basin Authority) (2018). A Case Study for Compliance Monitoring Using Satellite Imagery."},{"key":"ref_66","unstructured":"Parks Victoria (2018). Strategic Action Plan: Protection of Floodplain Marshes in Barmah National Park and Barmah Forest Ramsar Site."},{"key":"ref_67","unstructured":"Department of the Environment Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) (2019, March 13). Bioregional Assessment Source Dataset, Available online: http:\/\/data.bioregionalassessments.gov.au\/dataset\/6636846e-e330-4110-afbb-7b89491fe567."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"5053","DOI":"10.1029\/2019WR024873","article-title":"MERIT Hydro: A high-resolution global hydrography map based on latest topography dataset","volume":"55","author":"Yamazaki","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1029\/2008EO100001","article-title":"New global hydrography derived from spaceborne elevation data","volume":"89","author":"Lehner","year":"2008","journal-title":"Eos Trans. Am. Geophys. Union"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1038\/s41597-019-0243-y","article-title":"A data set of global river networks and corresponding water resources zones divisions","volume":"6","author":"Yan","year":"2019","journal-title":"Sci. Data"},{"key":"ref_71","unstructured":"(2022, February 15). Global Wetlands. Available online: https:\/\/www2.cifor.org\/global-wetlands\/."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"189","DOI":"10.5194\/essd-11-189-2019","article-title":"Multi-source global wetland maps combining surface water imagery and groundwater constraints","volume":"11","author":"Tootchi","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_73","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_74","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1016\/j.rse.2007.08.011","article-title":"The availability of cloud-free Landsat ETM plus data over the conterminous United States and globally","volume":"112","author":"Ju","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_75","unstructured":"Ticehurst, C., Dutta, D., and Vaze, J. (December, January 29). A comparison of Landsat and MODIS flood inundation maps for hydrodynamic modelling in the Murray Darling Basin. Proceedings of the 21st International Congress on Modelling and Simulation, Gold Coast, Australia. Available online: https:\/\/www.mssanz.org.au\/modsim2015."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Tsyganskaya, V., Martinis, S., Marzahn, P., and Ludwig, R. (2018). Detection of temporary flooded vegetation using Sentinel-1 time series data. Remote Sens., 10.","DOI":"10.3390\/rs10081286"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1158\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:28:03Z","timestamp":1760135283000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1158"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,26]]},"references-count":76,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14051158"],"URL":"https:\/\/doi.org\/10.3390\/rs14051158","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,26]]}}}