{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T22:18:39Z","timestamp":1785881919085,"version":"3.56.0"},"reference-count":41,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,7]],"date-time":"2018-08-07T00:00:00Z","timestamp":1533600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate and frequent updates of surface water have been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from the background based on a threshold value. Generally, the threshold is a fixed value, but can be challenging in the case of environmental noise, such as shadow, forest, built-up areas, snow, and clouds. One such challenging scene can be found in Nepal where no such evaluation has been done. Taking that in consideration, this study evaluates the performance of the most widely used water indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Automated Water Extraction Index (AWEI) in a Landsat 8 scene of Nepal. The scene, ranging from 60 m to 8848 m, contains various types of water bodies found in Nepal with different forms of environmental noise. The evaluation was conducted based on measures from a confusion matrix derived using validation points. Comparing visually and quantitatively, not a single method was able to extract surface water in the entire scene with better accuracy. Upon selecting optimum thresholds, the overall accuracy (OA) and kappa coefficient (kappa) was improved, but not satisfactory. NDVI and NDWI showed better results for only pure water pixels, whereas MNDWI and AWEI were unable to reject snow cover and shadows. Combining NDVI with NDWI and AWEI with shadow improved the accuracy but inherited the NDWI and AWEI characteristics. Segmenting the test scene with elevations above and below 665 m, and using NDVI and NDWI for detecting water, resulted in an OA of 0.9638 and kappa of 0.8979. The accuracy can be further improved with a smaller interval of categorical characteristics in one or multiple scenes.<\/jats:p>","DOI":"10.3390\/s18082580","type":"journal-article","created":{"date-parts":[[2018,8,7]],"date-time":"2018-08-07T11:20:23Z","timestamp":1533640823000},"page":"2580","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":276,"title":["Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0886-4201","authenticated-orcid":false,"given":"Tri Dev","family":"Acharya","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, Kangwon National University, Chuncheon 24341, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3585-9005","authenticated-orcid":false,"given":"Anoj","family":"Subedi","sequence":"additional","affiliation":[{"name":"Institute of Forestry, Pokhara Campus, Tribhuvan University, Pokhara 33700, Nepal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6934-1247","authenticated-orcid":false,"given":"Dong Ha","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Kangwon National University, Chuncheon 24341, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1126\/science.289.5477.284","article-title":"Global water resources: Vulnerability from climate change and population growth","volume":"289","author":"Vorosmarty","year":"2000","journal-title":"Science"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"3209","DOI":"10.3390\/s7123209","article-title":"Remote sensing sensors and applications in environmental resources mapping and modelling","volume":"7","author":"Melesse","year":"2007","journal-title":"Sensors"},{"key":"ref_4","first-page":"1","article-title":"Exploring land cover classification accuracy of Landsat 8 image using spectral index layer stacking in hilly region of South Korea","volume":"30","author":"Lee","year":"2018","journal-title":"Sens. Mater."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Acharya, T.D., Lee, D.H., Yang, I.T., and Lee, J.K. (2016). Identification of water bodies in a Landsat 8 OLI image using a J48 decision tree. Sensors, 16.","DOI":"10.3390\/s16071075"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"L\u00e4ssig, J., Kersting, K., and Morik, K. (2016). Global monitoring of inland water dynamics: State-of-the-art, challenges, and opportunities. Computational Sustainability, Springer International Publishing.","DOI":"10.1007\/978-3-319-31858-5"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.isprsjprs.2013.01.010","article-title":"Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011","volume":"79","author":"Tulbure","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","first-page":"353","article-title":"Water-body area extraction from high resolution satellite images\u2014An introduction, review, and comparison","volume":"3","author":"Nath","year":"2010","journal-title":"Int. J. Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.ecoinf.2009.09.013","article-title":"Improvements in mapping water bodies using ASTER data","volume":"5","author":"Sivanpillai","year":"2010","journal-title":"Ecol. Inf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"277","DOI":"10.2747\/1548-1603.42.4.277","article-title":"Remote detection of Prairie Pothole Ponds in the Devils Lake Basin, North Dakota","volume":"42","author":"Sethre","year":"2005","journal-title":"GISci. Remote Sens."},{"key":"ref_11","first-page":"335","article-title":"Evaluation of seasonal water body extents in Central Asia over the past 27 years derived from medium-resolution remote sensing data","volume":"26","author":"Klein","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf"},{"key":"ref_12","first-page":"1461","article-title":"Water body detection and delineation with Landsat TM data","volume":"66","author":"Frazier","year":"2000","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_13","first-page":"685","article-title":"Utilization of satellite data for inventorying prairie ponds and lakes","volume":"42","author":"Work","year":"1976","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4574","DOI":"10.1080\/01431161.2016.1217441","article-title":"Inland waterbody mapping: Towards improving discrimination and extraction of inland surface water features","volume":"37","author":"Malahlela","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","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_16","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_17","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_18","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_19","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_20","doi-asserted-by":"crossref","unstructured":"Wang, X., Liu, Y., Ling, F., Liu, Y., and Fang, F. (2017). Spatio-temporal change detection of Ningbo Coastline using Landsat Time-Series images during 1976\u20132015. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6030068"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Acharya, T.D., Yang, I.T., Subedi, A., and Lee, D.H. (2017). Change detection of Lakes in Pokhara, Nepal using Landsat data. Proceedings, 1.","DOI":"10.3390\/ecsa-3-E005"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","unstructured":"Fisher, A., and Danaher, T. (2013). A water index for SPOT5 HRG satellite imagery, New South Wales, Australia, determined by linear discriminant analysis. Remote Sens., 5.","DOI":"10.3390\/rs5115907"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shen, L., and Li, C. (2010, January 18\u201320). Water body extraction from Landsat ETM+ imagery using adaboost algorithm. Proceedings of the 18th International Conference on Geoinformatics, Beijing, China.","DOI":"10.1109\/GEOINFORMATICS.2010.5567762"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.1080\/01431160110107734","article-title":"Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data","volume":"23","author":"Xiao","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Acharya, T.D., Subedi, A., Yang, I.T., and Lee, D.H. (2018). Combining water indices for water and background threshold in Landsat image. Proceedings, 2.","DOI":"10.3390\/ecsa-4-04902"},{"key":"ref_28","unstructured":"Acharya, T.D., Yang, I.T., and Lee, D.H. (2016, January 6\u20137). Surface Water Area Delineation in Landsat OLI Image using Reflectance and SRTM DEM derivatives. Proceedings of the 2016 Conference on Geo-Spatial Information, Gunsan, Korea."},{"key":"ref_29","first-page":"428","article-title":"Water body mapping method with HJ-1A\/B satellite imagery","volume":"13","author":"Lu","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","unstructured":"Menarguez, M. (2015). Global Water Body Mapping from 1984 to 2015 Using Global High Resolution Multispectral Satellite Imagery, University of Oklahoma."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5067","DOI":"10.3390\/rs6065067","article-title":"An automated method for extracting rivers and lakes from Landsat imagery","volume":"6","author":"Jiang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_32","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_33","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_34","doi-asserted-by":"crossref","unstructured":"Taravat, A., Rajaei, M., Emadodin, I., Hasheminejad, H., Mousavian, R., and Biniyaz, E. (2016). A spaceborne multisensory, multitemporal approach to monitor water level and storage variations of lakes. Water, 8.","DOI":"10.3390\/w8110478"},{"key":"ref_35","unstructured":"(2017, July 31). Landsat 8, Available online: http:\/\/landsat.usgs.gov\/landsat8.php."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2005RG000183","article-title":"The shuttle radar topography mission","volume":"45","author":"Farr","year":"2007","journal-title":"Rev. Geophys."},{"key":"ref_37","first-page":"17","article-title":"Comparative analysis of digital elevation models between aw3d30, srtm30 and airborne lidar: A case of Chuncheon, South Korea","volume":"36","author":"Acharya","year":"2018","journal-title":"J. Korean Soc. Surv. Geodesy Photogramm. Cartogr."},{"key":"ref_38","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1973, January 10\u201314). Monitoring vegetation systems in the Great Plains with ERTS (Earth Resources Technology Satellite). Proceedings of the Third Earth Resources Technology Satellite Symposium, Greenbelt, ON, Canada."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"276","DOI":"10.11613\/BM.2012.031","article-title":"Interrater reliability: The kappa statistic","volume":"22","author":"McHugh","year":"2012","journal-title":"Biochem. Med."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"026016","DOI":"10.1117\/1.JRS.11.026016","article-title":"Evaluation of automated urban surface water extraction from Sentinel-2A imagery using different water indices","volume":"11","author":"Yang","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1826","DOI":"10.1080\/01431161.2016.1168948","article-title":"Evaluation of Landsat 8 OLI imagery for unsupervised inland water extraction","volume":"37","author":"Xie","year":"2016","journal-title":"Int. J. Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/8\/2580\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:17:06Z","timestamp":1760195826000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/8\/2580"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,7]]},"references-count":41,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2018,8]]}},"alternative-id":["s18082580"],"URL":"https:\/\/doi.org\/10.3390\/s18082580","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,7]]}}}