{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T15:35:25Z","timestamp":1780932925751,"version":"3.54.1"},"reference-count":36,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,12]],"date-time":"2020-01-12T00:00:00Z","timestamp":1578787200000},"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>Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location and dispersed nature of these water bodies limits typical methods of monitoring, such as with gauges; fortunately, remote sensing offers a quick and cost-effective means of regularly measuring surface water extent in these isolated regions. Dozens of operational methods exist to use remote sensing to identify waterbodies, however, their performance when identifying surface water in the semi-arid Sahel has not been well-documented and the limitations of these methods for the region are not well understood. Here, we evaluate two global dynamic surface water datasets, fifteen spectral indices developed to classify surface water extent, and three simple decision tree methods created specifically to identify surface water in semi-arid environments. We find that the existing global surface water datasets effectively minimize false positives, but greatly underestimate the presence and extent of smaller, more turbid water bodies that are essential to local livelihoods, an important limitation in their use for monitoring water availability. Three of fifteen spectral indices exhibited both high accuracy and threshold stability when evaluated over different areas and seasons. The three simple decision tree methods had mixed performance, with only one having an overall accuracy that compared to the best performing spectral indices. We find that while global surface water datasets may be appropriate for analysis at the global scale, other methods calibrated to the local environment may provide improved performance for more localized water monitoring needs.<\/jats:p>","DOI":"10.3390\/s20020431","type":"journal-article","created":{"date-parts":[[2020,1,13]],"date-time":"2020-01-13T04:05:51Z","timestamp":1578888351000},"page":"431","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["An Assessment of Surface Water Detection Methods for Water Resource Management in the Nigerien Sahel"],"prefix":"10.3390","volume":"20","author":[{"given":"Kelsey","family":"Herndon","sequence":"first","affiliation":[{"name":"NASA SERVIR Science Coordination Office, NASA Marshall Space Flight Center, Huntsville, AL 35899, USA"},{"name":"Earth System Science Center, The University of Alabama in Huntsville, Huntsville, AL 35899, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6687-0995","authenticated-orcid":false,"given":"Rebekke","family":"Muench","sequence":"additional","affiliation":[{"name":"NASA SERVIR Science Coordination Office, NASA Marshall Space Flight Center, Huntsville, AL 35899, USA"},{"name":"Earth System Science Center, The University of Alabama in Huntsville, Huntsville, AL 35899, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0524-4744","authenticated-orcid":false,"given":"Emil","family":"Cherrington","sequence":"additional","affiliation":[{"name":"NASA SERVIR Science Coordination Office, NASA Marshall Space Flight Center, Huntsville, AL 35899, USA"},{"name":"Earth System Science Center, The University of Alabama in Huntsville, Huntsville, AL 35899, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Griffin","sequence":"additional","affiliation":[{"name":"NASA SERVIR Science Coordination Office, NASA Marshall Space Flight Center, Huntsville, AL 35899, USA"},{"name":"Department of Atmospheric and Earth Science, The University of Alabama in Huntsville, Huntsville, AL 35899, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.gloenvcha.2014.06.014","article-title":"Divergent adaptation to climate variability: A case study of pastoral and agricultural societies in Niger","volume":"29","author":"Snorek","year":"2014","journal-title":"Glob. Environ. Chang."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.jrurstud.2017.01.015","article-title":"The production of contested landscapes: Enclosing the pastoral commons in Niger","volume":"51","author":"Snorek","year":"2017","journal-title":"J. Rural Stud."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1080\/01431169608949041","article-title":"Remote sensing of ephemeral water bodies in western Niger","volume":"17","author":"Verdin","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","unstructured":"Snorek, J., Terasawa, K., and Stark, J. (2014). Climate Change and Conflict in the Sahel: A Policy Brief on Findings from Niger and Burkina Faso, USAID African and Latin American Resilience to Climate Change Project."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of 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_6","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalized 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_8","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_9","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1080\/2150704X.2014.915434","article-title":"Derivation of a tasseled cap transformation based on Landsat 8 at-satellite reflectance","volume":"5","author":"Baig","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_10","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_11","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":"2","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2006.07.012","article-title":"Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever in Senegal","volume":"106","author":"Lacaux","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1002\/jgrg.20121","article-title":"Characterization of the spatial and temporal variability of surface water in the Soudan-Sahel region of Africa","volume":"118","author":"Kaptue","year":"2013","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1080\/0143116031000139908","article-title":"Surveillance et cartographie des plans d\u2019eau et des zones humides et inondables en regions arides avel l\u2019instrument VEGETATION embarque sur SPOT-4","volume":"25","author":"Gond","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Huang, C., Chen, Y., Zhang, S., Li, L., Shi, K., and Liu, R. (2017). Spatial downscaling of Suomi NPP-VIIRS image for lake mapping. Water, 9.","DOI":"10.3390\/w9110834"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Acharya, T.D., Subedi, A., and Lee, D.H. (2019). Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal. Sensors, 19.","DOI":"10.3390\/s19122769"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4909","DOI":"10.1109\/JSTARS.2017.2735443","article-title":"Surface water mapping by deep learning","volume":"10","author":"Isikdogan","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","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":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.jhydrol.2012.07.042","article-title":"Normalized difference water indexes have dissimilar performances in detecting seasonal and permanent water in the Sahara-Sahel transition zone","volume":"464","author":"Campos","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_24","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_25","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_26","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_27","unstructured":"USGS (2018). Product Guide: Landsat Surface Reflectance Code (LaSRC) Product v. 4.3."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"453521","DOI":"10.1155\/2013\/453521","article-title":"The West African Sahel: A review of recent studies on the rainfall regime and its interannual variability","volume":"2013","author":"Nicholson","year":"2013","journal-title":"ISRN Meteorol."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1109\/36.581987","article-title":"Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: An overview","volume":"35","author":"Vermote","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/S0034-4257(02)00089-5","article-title":"Atmospheric correction of MODIS data in the visible to middle infrared: First results","volume":"83","author":"Vermote","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_32","unstructured":"Herndon, K.E. (2018). Applications of Remote Sensing for the Monitoring of Surface Water Dynamics in the Sahel. [Master\u2019s Thesis, The University of Alabama in Huntsville]."},{"key":"ref_33","first-page":"344","article-title":"Assessing optical Earth observation systems for mapping and monitoring temporary ponds in arid areas","volume":"11","author":"Soti","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/S0001-2998(78)80014-2","article-title":"Basic principles of ROC analysis","volume":"8","author":"Metz","year":"1978","journal-title":"Seminars Nucl. Med."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/0169-2607(91)90072-2","article-title":"Strategies for graphical threshold determination","volume":"35","author":"Vermont","year":"1991","journal-title":"Comput. Methods Programs Biomed."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/2\/431\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:42:51Z","timestamp":1760362971000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/2\/431"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,12]]},"references-count":36,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20020431"],"URL":"https:\/\/doi.org\/10.3390\/s20020431","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,12]]}}}