{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T08:21:22Z","timestamp":1776327682220,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,12]],"date-time":"2019-12-12T00:00:00Z","timestamp":1576108800000},"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>Accurately mapping the boundary between land and water (the \u2018waterline\u2019) is critical for tracking change in vulnerable coastal zones, and managing increasingly threatened water resources. Previous studies have largely relied on mapping waterlines at the pixel scale, or employed computationally intensive sub-pixel waterline extraction methods that are impractical to implement at scale. There is a pressing need for operational methods for extracting information from freely available medium resolution satellite imagery at spatial scales relevant to coastal and environmental management. In this study, we present a comprehensive evaluation of a promising method for mapping waterlines at sub-pixel accuracy from satellite remote sensing data. By combining a synthetic landscape approach with high resolution WorldView-2 satellite imagery, it was possible to rapidly assess the performance of the method across multiple coastal environments with contrasting spectral characteristics (sandy beaches, artificial shorelines, rocky shorelines, wetland vegetation and tidal mudflats), and under a range of water indices (Normalised Difference Water Index, Modified Normalised Difference Water Index, and the Automated Water Extraction Index) and thresholding approaches (optimal, zero and automated Otsu\u2019s method). The sub-pixel extraction method shows a strong ability to reproduce both absolute waterline positions and relative shape at a resolution that far exceeds that of traditional whole-pixel methods, particularly in environments without extreme contrast between the water and land (e.g., accuracies of up to 1.50\u20133.28 m at 30 m Landsat resolution using optimal water index thresholds). We discuss key challenges and limitations associated with selecting appropriate water indices and thresholds for sub-pixel waterline extraction, and suggest future directions for improving the accuracy and reliability of extracted waterlines. The sub-pixel waterline extraction method has a low computational overhead and is made available as an open-source tool, making it suitable for operational continental-scale or full time-depth analyses aimed at accurately mapping and monitoring dynamic waterlines through time and space.<\/jats:p>","DOI":"10.3390\/rs11242984","type":"journal-article","created":{"date-parts":[[2019,12,12]],"date-time":"2019-12-12T11:06:41Z","timestamp":1576148801000},"page":"2984","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1533-2599","authenticated-orcid":false,"given":"Robbi","family":"Bishop-Taylor","sequence":"first","affiliation":[{"name":"Geoscience Australia, Cnr Jerrabomberra Ave and Hindmarsh Drive, Symonston ACT 2609, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9568-9661","authenticated-orcid":false,"given":"Stephen","family":"Sagar","sequence":"additional","affiliation":[{"name":"Geoscience Australia, Cnr Jerrabomberra Ave and Hindmarsh Drive, Symonston ACT 2609, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leo","family":"Lymburner","sequence":"additional","affiliation":[{"name":"Geoscience Australia, Cnr Jerrabomberra Ave and Hindmarsh Drive, Symonston ACT 2609, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Imam","family":"Alam","sequence":"additional","affiliation":[{"name":"Geoscience Australia, Cnr Jerrabomberra Ave and Hindmarsh Drive, Symonston ACT 2609, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joshua","family":"Sixsmith","sequence":"additional","affiliation":[{"name":"Geoscience Australia, Cnr Jerrabomberra Ave and Hindmarsh Drive, Symonston ACT 2609, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,12]]},"reference":[{"key":"ref_1","first-page":"92","article-title":"Using GPS-surveyed intertidal zones to determine the validity of shorelines automatically mapped by Landsat water indices","volume":"65","author":"Kelly","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kopp, S., Becker, P., Doshi, A., Wright, D.J., Zhang, K., and Xu, H. (2019). Achieving the Full Vision of Earth Observation Data Cubes. Data, 4.","DOI":"10.3390\/data4030094"},{"key":"ref_3","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_4","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_5","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_6","doi-asserted-by":"crossref","first-page":"6641","DOI":"10.1038\/s41598-018-24630-6","article-title":"The state of the world\u2019s beaches","volume":"8","author":"Luijendijk","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.rse.2017.04.009","article-title":"Extracting the intertidal extent and topography of the Australian coastline from a 28 year time series of Landsat observations","volume":"195","author":"Sagar","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1038\/s41586-018-0805-8","article-title":"The global distribution and trajectory of tidal flats","volume":"565","author":"Murray","year":"2019","journal-title":"Nature"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lymburner, L., Bunting, P., Lucas, R., Scarth, P., Alam, I., Phillips, C., Ticehurst, C., and Held, A. (2019). Mapping the multi-decadal mangrove dynamics of the Australian coastline. Remote Sens. Environ.","DOI":"10.1016\/j.rse.2019.05.004"},{"key":"ref_10","first-page":"115","article-title":"Between the tides: Modelling the elevation of Australia\u2019s exposed intertidal zone at continental scale","volume":"23","author":"Sagar","year":"2019","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_11","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_12","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_13","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1002\/esp.2264","article-title":"Beach erosion and recovery during consecutive storms at a steep-sloping, meso-tidal beach","volume":"37","author":"Vousdoukas","year":"2012","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Long, N., Millescamps, B., Guillot, B., Pouget, F., and Bertin, X. (2016). Monitoring the Topography of a Dynamic Tidal Inlet Using UAV Imagery. Remote Sens., 8.","DOI":"10.3390\/rs8050387"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Almeida, L.P., Almar, R., Bergsma, E.W.J., Berthier, E., Baptista, P., Garel, E., Dada, O.A., and Alves, B. (2019). Deriving High Spatial-Resolution Coastal Topography From Sub-meter Satellite Stereo Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11050590"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1080\/02626667.2019.1566727","article-title":"Assessment of the geometry and volumes of small surface water reservoirs by remote sensing in a semi-arid region with high reservoir density","volume":"64","author":"Pereira","year":"2019","journal-title":"Hydrol. Sci. J."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/S0034-4257(02)00059-7","article-title":"Waterline extraction from Landsat TM data in a tidal flat: A case study in Gomso Bay, Korea","volume":"83","author":"Ryu","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1016\/j.ecss.2008.01.020","article-title":"Detecting the intertidal morphologic change using satellite data","volume":"78","author":"Ryu","year":"2008","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1080\/2150704X.2013.791955","article-title":"A tidal correction model for near-infrared (NIR) reflectance over tidal flats","volume":"4","author":"Park","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_22","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_23","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":"2011","journal-title":"Environ. Monit. Assess."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7522","DOI":"10.3390\/rs6087522","article-title":"Estimation of reservoir discharges from Lake Nasser and Roseires Reservoir in the Nile Basin using satellite altimetry and imagery data","volume":"6","author":"Muala","year":"2014","journal-title":"Remote Sens."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"6041","DOI":"10.1038\/srep06041","article-title":"Drastic change in China\u2019s lakes and reservoirs over the past decades","volume":"4","author":"Yang","year":"2014","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Schwatke, C., Scherer, D., and Dettmering, D. (2019). Automated Extraction of Consistent Time-Variable Water Surfaces of Lakes and Reservoirs Based on Landsat and Sentinel-2. Remote Sens., 11.","DOI":"10.3390\/rs11091010"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, Q., and Trinder, J.C. (2018). Sub-pixel technique for time series analysis of shoreline changes based on multispectral satellite imagery. Advanced Remote Sens. Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure, IntechOpen.","DOI":"10.5772\/intechopen.81789"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.rse.2019.04.010","article-title":"Coastline extraction from repeat high resolution satellite imagery","volume":"229","author":"Dai","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, J., Knapp, D.E., Schill, S.R., Roelfsema, C., Phinn, S., Silman, M., Mascaro, J., and Asner, G.P. (2019). Adaptive bathymetry estimation for shallow coastal waters using Planet Dove satellites. Remote Sens. Environ., 232.","DOI":"10.1016\/j.rse.2019.111302"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1126\/science.320.5879.1011a","article-title":"Free access to Landsat imagery","volume":"320","author":"Woodcock","year":"2008","journal-title":"Science"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2012.02.024","article-title":"Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision","volume":"123","author":"Ruiz","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pardo-Pascual, J.E., S\u00e1nchez-Garc\u00eda, E., Almonacid-Caballer, J., Palomar-V\u00e1zquez, J.M., Priego de los Santos, E., Fern\u00e1ndez-Sarr\u00eda, A., and Balaguer-Beser, \u00c1. (2018). Assessing the accuracy of automatically extracted shorelines on microtidal beaches from Landsat 7, Landsat 8 and Sentinel-2 imagery. Remote Sens., 10.","DOI":"10.3390\/rs10020326"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.margeo.2015.12.015","article-title":"Evaluation of annual mean shoreline position deduced from Landsat imagery as a mid-term coastal evolution indicator","volume":"372","year":"2016","journal-title":"Mar. Geol."},{"key":"ref_35","unstructured":"Foody, G.M., Muslim, A.M., and Atkinson, P.M. (2003, January 21\u201325). Super-resolution mapping of the shoreline through soft classification analyses. Proceedings of the IGARSS 2003, 2003 IEEE International Geoscience and Remote Sens. Symposium. Proceedings (IEEE Cat. No.03CH37477), Toulouse, France."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5381","DOI":"10.1080\/01431160500213292","article-title":"Super-resolution mapping of the waterline from remotely sensed data","volume":"26","author":"Foody","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.2112\/04-0421.1","article-title":"Shoreline Mapping from Coarse-Spatial Resolution Remote Sens. Imagery of Seberang Takir, Malaysia","volume":"23","author":"Muslim","year":"2007","journal-title":"J. Coast. Res."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, Q., Trinder, J.C., and Turner, I.L. (2017). Automatic super-resolution shoreline change monitoring using Landsat archival data: A case study at Narrabeen\u2013Collaroy Beach, Australia. J. Appl. Remote Sens., 11.","DOI":"10.1117\/1.JRS.11.016036"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.coastaleng.2019.04.004","article-title":"Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery","volume":"150","author":"Vos","year":"2019","journal-title":"Coast. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Liu, X., Deng, R., Xu, J., and Zhang, F. (2017). Coupling the modified linear spectral mixture analysis and pixel-swapping methods for improving subpixel water mapping: Application to the Pearl River Delta, China. Water, 9.","DOI":"10.3390\/w9090658"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Niroumand-Jadidi, M., and Vitti, A. (2017). Reconstruction of river boundaries at sub-pixel resolution: estimation and spatial allocation of water fractions. ISPRS Int. J. Geo Inf., 6.","DOI":"10.3390\/ijgi6120383"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.coastaleng.2017.12.011","article-title":"On the accuracy of automated shoreline detection derived from satellite imagery: A case study of the Sand Motor mega-scale nourishment","volume":"133","author":"Hagenaars","year":"2018","journal-title":"Coast. Eng."},{"key":"ref_43","unstructured":"Moreno, L.J., and Kraus, N.C. (1999). Equilibrium shape of headland-bay beaches for engineering design. Proc. Coastal Sediments, 860\u2013875."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1016\/j.rse.2012.06.018","article-title":"A physics-based atmospheric and BRDF correction for Landsat data over mountainous terrain","volume":"124","author":"Li","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1080\/2150704X.2017.1420928","article-title":"Fine spatial resolution coastline extraction from Landsat-8 OLI imagery by integrating downscaling and pansharpening approaches","volume":"9","author":"Wang","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.ocecoaman.2006.04.005","article-title":"Using mangroves as a geological indicator of coastal changes in the Bragan\u00e7a macrotidal flat, Brazilian Amazon: A remote sensing data approach","volume":"49","year":"2006","journal-title":"Ocean Coast. Manag."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.ocecoaman.2013.01.003","article-title":"The relationship of spatial\u2013temporal changes in fringe mangrove extent and adjacent land-use: Case study of Kien Giang coast, Vietnam","volume":"76","author":"Nguyen","year":"2013","journal-title":"Ocean Coast. Manag."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2024","DOI":"10.1002\/esp.3968","article-title":"Dynamics of a fringe mangrove forest detected by Landsat images in the Mekong River Delta, Vietnam","volume":"41","author":"Nardin","year":"2016","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.ecss.2007.09.022","article-title":"A simple waterline approach for tidelands using multi-temporal satellite images: A case study in the Yangtze Delta","volume":"77","author":"Zhao","year":"2008","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"3417","DOI":"10.3390\/rs4113417","article-title":"Continental scale mapping of tidal flats across East Asia using the Landsat archive","volume":"4","author":"Murray","year":"2012","journal-title":"Remote Sens."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cageo.2011.07.015","article-title":"Superresolution border segmentation and measurement in remote sensing images","volume":"40","author":"Cipolletti","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e453","DOI":"10.7717\/peerj.453","article-title":"scikit-image: image processing in Python","volume":"2","author":"Boulogne","year":"2014","journal-title":"PeerJ"},{"key":"ref_55","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_56","unstructured":"Gilles, S. (2019, December 10). Shapely: Manipulation and Analysis of Geometric Objects. Available online: https:\/\/toblerity.org."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Hoyer, S., and Hamman, J. (2017). xarray: ND labeled arrays and datasets in Python. J. Open Res. Software, 5.","DOI":"10.5334\/jors.148"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MCSE.2011.37","article-title":"The NumPy array: A structure for efficient numerical computation","volume":"13","author":"Colbert","year":"2011","journal-title":"Comput. Sci. Eng."},{"key":"ref_59","unstructured":"Jones, E., Oliphant, T., and Peterson, P. (2019, December 10). {SciPy}: Open Source Scientific Tools for Python. Available online: http:\/\/www.scipy.org\/."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"McKinney, W. (July, January 28). Data structures for statistical computing in Python. Proceedings of the 9th Python in Science Conference (SciPy 2010), Austin, TX, USA.","DOI":"10.25080\/Majora-92bf1922-00a"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_62","unstructured":"Waskom, M. (2019, December 10). Seaborn: Statistical Data Visualization using Matplotlib. Available online: https:\/\/seaborn.pydata.org."},{"key":"ref_63","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_64","doi-asserted-by":"crossref","first-page":"105971","DOI":"10.1016\/j.margeo.2019.105971","article-title":"Bimodal climate control of shoreline change influenced by Interdecadal Pacific Oscillation variability along the Cooloola Sand Mass, Queensland, Australia","volume":"415","author":"Kelly","year":"2019","journal-title":"Mar. Geol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2197","DOI":"10.1080\/01431161003667455","article-title":"A self-trained classification technique for producing 30 m percent-water maps from Landsat data","volume":"31","author":"Rover","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Xie, H., Luo, X., Xu, X., Pan, H., and Tong, X. (2016). Automated subpixel surface water mapping from heterogeneous urban environments using Landsat 8 OLI imagery. Remote Sens., 8.","DOI":"10.3390\/rs8070584"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Sun, W., Du, B., and Xiong, S. (2017). Quantifying sub-pixel surface water coverage in urban environments using low-albedo fraction from Landsat imagery. Remote Sens., 9.","DOI":"10.3390\/rs9050428"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez-Garc\u00eda, E., Balaguer-Beser, \u00c1., Almonacid-Caballer, J., and Pardo-Pascual, J.E. (2019). A new adaptive image interpolation method to define the shoreline at sub-pixel level. Remote Sens., 11.","DOI":"10.3390\/rs11161880"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Song, Y., Liu, F., Ling, F., and Yue, L. (2019). Automatic semi-global artificial shoreline subpixel localization algorithm for Landsat imagery. Remote Sens., 11.","DOI":"10.3390\/rs11151779"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Vos, K., Splinter, K.D., Harley, M.D., Simmons, J.A., and Turner, I.L. (2019). CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery. Environ. Model. Softw., 122.","DOI":"10.1016\/j.envsoft.2019.104528"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/24\/2984\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:41:40Z","timestamp":1760190100000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/24\/2984"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,12]]},"references-count":70,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["rs11242984"],"URL":"https:\/\/doi.org\/10.3390\/rs11242984","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,12]]}}}