{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T19:51:01Z","timestamp":1776109861112,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,13]],"date-time":"2019-02-13T00:00:00Z","timestamp":1550016000000},"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>In order to produce useful hydrologic and aquatic habitat data from the Landsat system, the U.S. Geological Survey has developed the \u201cDynamic Surface Water Extent\u201d (DSWE) Landsat Science Product. DSWE will provide long-term, high-temporal resolution data on variations in inundation extent. The model used to generate DSWE is composed of five decision-rule based tests that do not require scene-based training. To allow its general application, required inputs are limited to the Landsat at-surface reflectance product and a digital elevation model. Unlike other Landsat-based water products, DSWE includes pixels that are only partially covered by water to increase inundation dynamics information content. Previously published DSWE model development included one wetland-focused test developed through visual inspection of field-collected Everglades spectra. A comparison of that test\u2019s output against Everglades Depth Estimation Network (EDEN) in situ data confirmed the expectation that omission errors were a prime source of inaccuracy in vegetated environments. Further evaluation exposed a tendency toward commission error in coniferous forests. Improvements to the subpixel level \u201cpartial surface water\u201d (PSW) component of DSWE was the focus of this research. Spectral mixture models were created from a variety of laboratory and image-derived endmembers. Based on the mixture modeling, a more \u201caggressive\u201d PSW rule improved accuracy in herbaceous wetlands and reduced errors of commission elsewhere, while a second \u201cconservative\u201d test provides an alternative when commission errors must be minimized. Replication of the EDEN-based experiments using the revised PSW tests yielded a statistically significant increase in mean overall agreement (4%, p = 0.01, n = 50) and a statistically significant decrease (11%, p = 0.009, n = 50) in mean errors of omission. Because the developed spectral mixture models included image-derived vegetation endmembers and laboratory spectra for soil groups found across the US, simulations suggest where the revised DSWE PSW tests perform as they do in the Everglades and where they may prove problematic. Visual comparison of DSWE outputs with an unusual variety of coincidently collected images for locations spread throughout the US support conclusions drawn from Everglades quantitative analyses and highlight DSWE PSW component strengths and weaknesses.<\/jats:p>","DOI":"10.3390\/rs11040374","type":"journal-article","created":{"date-parts":[[2019,2,14]],"date-time":"2019-02-14T03:21:46Z","timestamp":1550114506000},"page":"374","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":193,"title":["Improved Automated Detection of Subpixel-Scale Inundation\u2014Revised Dynamic Surface Water Extent (DSWE) Partial Surface Water Tests"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6117-3691","authenticated-orcid":false,"given":"John W.","family":"Jones","sequence":"first","affiliation":[{"name":"U.S. Geological Survey, 415 National Center, Reston, VA 20192, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.rse.2011.09.022","article-title":"Landsat: Building a strong future","volume":"122","author":"Loveland","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_2","unstructured":"Australia, G. (2018, September 05). Water Observations from Space, Available online: http:\/\/www.ga.gov.au\/scientific-topics\/hazards\/flood\/wofs."},{"key":"ref_3","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_4","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_5","doi-asserted-by":"crossref","unstructured":"Devries, B., Huang, C., Lang, M.W., Jones, J.W., Creed, I.F., and Carroll, M.L. (2017). Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9080807"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Carroll, M.L., and Loboda, T.V. (2017). Multi-Decadal Surface Water Dynamics in North American Tundra. Remote Sens., 9.","DOI":"10.3390\/rs9050497"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1016\/j.jhydrol.2018.04.005","article-title":"Satellite remote sensing estimation of river discharge: Application to the Yukon River Alaska","volume":"561","author":"Bjerklie","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Huang, W., Devries, B., Huang, C., Lang, M.W., Jones, J.W., Creed, I.F., and Carroll, M.L. (2018). Automated Extraction of Surface Water Extent from Sentinel-1 Data. Remote Sens., 10.","DOI":"10.3390\/rs10050797"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1029\/2018RG000598","article-title":"Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review","volume":"56","author":"Huang","year":"2018","journal-title":"Rev. Geophys."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1023\/A:1020908432489","article-title":"Satellite remote sensing of wetlands","volume":"10","author":"Ozesmi","year":"2002","journal-title":"Wetlands Ecol. Manage."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s13157-011-0254-8","article-title":"Multi-temporal sub-pixel landsat ETM+ classification of isolated wetlands in cuyahoga county, OHIO, USA","volume":"32","author":"Frohn","year":"2012","journal-title":"Wetlands"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.rse.2013.10.020","article-title":"Wetland inundation mapping and change monitoring using Landsat and airborne LiDAR data","volume":"141","author":"Huang","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8098","DOI":"10.1029\/JB091iB08p08098","article-title":"Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 Site","volume":"91","author":"Adams","year":"1986","journal-title":"J. Geophys. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.ecolmodel.2005.10.033","article-title":"A comparison of the classification of wetland characteristics by linear spectral mixture modelling and traditional hard classifiers on multispectral remotely sensed imagery in southern India","volume":"194","author":"Shanmugam","year":"2006","journal-title":"Ecol. Model."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.apgeog.2009.05.006","article-title":"Detecting drought induced environmental changes in a Mediterranean wetland by remote sensing","volume":"30","author":"Koch","year":"2010","journal-title":"Appl. Geogr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2516","DOI":"10.1109\/TGRS.2005.852082","article-title":"Multisensor approach to determine changes of wetland characteristics in semiarid environments (central Spain)","volume":"43","author":"Schmid","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.5846\/stxb201204270604","article-title":"Research on estimating wetland vegetation abundance based on spectral mixture analysis with different endmember model: a case study in Wild Duck Lake wetland, Beijing","volume":"33","author":"Cui","year":"2013","journal-title":"Acta Ecol. Sin."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4319","DOI":"10.1080\/01431160903252343","article-title":"Linear spectral mixture analysis of Landsat TM data for monitoring invasive exotic plants in estuarine wetlands","volume":"31","author":"He","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1080\/01431160310001618103","article-title":"Reducing signature variability in unmixing coastal marsh Thematic Mapper scenes using spectral indices","volume":"25","author":"Rogers","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.rse.2016.02.040","article-title":"Reconstructing semi-arid wetland surface water dynamics through spectral mixture analysis of a time series of Landsat satellite images (1984\u20132011)","volume":"177","author":"Halabisky","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xia, H., Zhao, W., Li, A., Bian, J., and Zhang, Z. (2017). Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China. Remote Sens., 9.","DOI":"10.3390\/rs9010031"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1007\/s13157-015-0696-5","article-title":"Assessing Land Cover Change and Anthropogenic Disturbance in Wetlands Using Vegetation Fractions Derived from Landsat 5 TM Imagery (1984\u20132010)","volume":"35","author":"Robertson","year":"2015","journal-title":"Wetlands"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/BF02743034","article-title":"Assessing wetland changes in the venice lagoon by means of satellite remote sensing data","volume":"2","author":"Brivio","year":"1996","journal-title":"J. Coast. Conserv."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Xiong, L., Deng, R., Li, J., Liu, X., Qin, Y., Liang, Y., and Liu, Y. (2018). Subpixel Surface Water Extraction (SSWE) Using Landsat 8 OLI Data. Water, 10.","DOI":"10.3390\/w10050653"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Adams, J.B., and Gillespie, A.R. (2006). Remote Sensing of Landscapes with Spectral Images: A Physical Modeling Approach, Cambridge University Press.","DOI":"10.1017\/CBO9780511617195"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.rse.2014.03.034","article-title":"Incorporating spatial information in spectral unmixing: A review","volume":"149","author":"Shi","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/S0034-4257(00)00100-0","article-title":"Quantifying Vegetation Change in Semiarid Environments: Precision and Accuracy of Spectral Mixture Analysis and the Normalized Difference Vegetation Index","volume":"73","author":"Elmore","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2004.06.007","article-title":"The Landsat ETM+ spectral mixing space","volume":"93","author":"Small","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1080\/10643389.2010.530924","article-title":"Remote Sensing of Vegetation Pattern and Condition to Monitor Changes in Everglades Biogeochemistry","volume":"41","author":"Jones","year":"2011","journal-title":"Crit. Rev. Environ. Sci. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.rse.2017.01.033","article-title":"Global cross-calibration of Landsat spectral mixture models","volume":"192","author":"Sousa","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1016\/j.rse.2008.11.007","article-title":"The ASTER spectral library version 2.0","volume":"113","author":"Baldridge","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.rse.2013.05.024","article-title":"Multi-scale standardized spectral mixture models","volume":"136","author":"Small","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_36","unstructured":"USGS (2018). LANDSAT 4-7 Surface Reflectance (LEDAPS) Product."},{"key":"ref_37","unstructured":"USGS (2018). LANDSAT 8 Surface Reflectance Code (LASRC) Product."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1006\/jema.2002.0551","article-title":"Assessing state-wide biodiversity in the Florida Gap analysis project","volume":"66","author":"Pearlstine","year":"2002","journal-title":"J. Environ. Manage."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gergely, K.J., and McKerrow, A. (2013). Terrestrial ecosystems: national inventory of vegetation and land use.","DOI":"10.3133\/fs20133085"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_41","unstructured":"USGS (2018, October 02). What are Landsat7 SLC-off Masks?, Available online: https:\/\/landsat.usgs.gov\/what-are-landsat-7-slc-gap-mask-files."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kokaly, R.F., Clark, R.N., Swayze, G.A., Livo, K.E., Hoefen, T.M., Pearson, N.C., Wise, R.A., Benzel, W.M., Lowers, H.A., and Driscoll, R.L. (2017). USGS Spectral Library Version 7, in Data Series 2017.","DOI":"10.3133\/ds1035"},{"key":"ref_43","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_44","unstructured":"Soil Survey Staff (1999). Soil Taxonomy: A Basic System of Soil Classification for Making and Interpreting Soil Surveys."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Jones, J.W., and Price, S.D. (2007). Conceptual Design of the Everglades Depth Estimation Network (EDEN) Grid.","DOI":"10.3133\/ofr20071200"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Telis, P. (2006). The Everglades Depth Estimation Network (EDEN) for Support of Ecological and Biological Assessments.","DOI":"10.3133\/fs20063087"},{"key":"ref_47","unstructured":"USGS (2018, September 05). South Florida Information Access, Available online: http:\/\/sofia.usgs.gov."},{"key":"ref_48","unstructured":"USGS (2015, May 05). EDEN Gage Ancillary Data Collection Protocol, Available online: http:\/\/sofia.usgs.gov\/eden\/geprotocol.php."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"7952","DOI":"10.3390\/rs6097952","article-title":"Continuity of reflectance data between landsat-7 ETM+ and landsat-8 OLI, for both top-of-atmosphere and surface reflectance: A study in the australian landscape","volume":"6","author":"Flood","year":"2014","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"12619","DOI":"10.3390\/rs61212619","article-title":"Radiometric Cross Calibration of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+)","volume":"6","author":"Mishra","year":"2014","journal-title":"Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"13485","DOI":"10.3390\/rs71013485","article-title":"Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types","volume":"7","author":"She","year":"2015","journal-title":"Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"310","DOI":"10.3390\/rs6010310","article-title":"Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.rse.2015.08.030","article-title":"Evaluation of the Landsat-5 TM and Landsat-7 ETM+ surface reflectance products","volume":"169","author":"Claverie","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_54","unstructured":"Nachtergaele, F. (2015). Status of the World\u2019s Soil Resources (SWSR)\u2014Main Report, FAO."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2014.03.031","article-title":"Monitoring Everglades freshwater marsh water level using L-band synthetic aperture radar backscatter","volume":"150","author":"Kim","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1080\/01431161.2017.1420938","article-title":"SAR-based detection of flooded vegetation\u2014A review of characteristics and approaches","volume":"39","author":"Tsyganskaya","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1641\/0006-3568(2005)055[0863:VONPWT]2.0.CO;2","article-title":"Vulnerability of Northern Prairie Wetlands to Climate Change","volume":"55","author":"Johnson","year":"2005","journal-title":"BioScience"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1023\/A:1005448416571","article-title":"Hydrology of Prairie Pothole Wetlands during Drought and Deluge: A 17-Year Study of the Cottonwood Lake Wetland Complex in North Dakota in the Perspective of Longer Term Measured and Proxy Hydrological Records","volume":"40","author":"Winter","year":"1998","journal-title":"Clim. Change"},{"key":"ref_59","unstructured":"Duarte, A. (2018). US Fish and Wildlife Habitat Conservation Planning Polygon, USFWS."},{"key":"ref_60","unstructured":"USGS (2018, October 11). USGS 14056500 Deschutes R BL Wickiup Res NR LA Pine, Oreg, Available online: https:\/\/waterdata.usgs.gov\/or\/nwis\/nwismap\/?site_no=14056500&agency_cd=USGS."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/4\/374\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:31:37Z","timestamp":1760185897000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/4\/374"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,13]]},"references-count":60,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["rs11040374"],"URL":"https:\/\/doi.org\/10.3390\/rs11040374","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,13]]}}}