{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T10:59:51Z","timestamp":1768733991181,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2016,12,10]],"date-time":"2016-12-10T00:00:00Z","timestamp":1481328000000},"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>Water body detection worldwide using spaceborne remote sensing is a challenging task. A global scale multi-temporal and multi-spectral image analysis method for water body detection was developed. The PROBA-V microsatellite has been fully operational since December 2013 and delivers daily near-global synthesis with a spatial resolution of 1 km and 333 m. The Red, Near-InfRared (NIR) and Short Wave InfRared (SWIR) bands of the atmospherically corrected 10-day synthesis images are first Hue, Saturation and Value (HSV) color transformed and subsequently used in a decision tree classification for water body detection. To minimize commission errors four additional data layers are used: the Normalized Difference Vegetation Index (NDVI), Water Body Potential Mask (WBPM), Permanent Glacier Mask (PGM) and Volcanic Soil Mask (VSM). Threshold values on the hue and value bands, expressed by a parabolic function, are used to detect the water bodies. Beside the water bodies layer, a quality layer, based on the water bodies occurrences, is available in the output product. The performance of the Water Bodies Detection Algorithm (WBDA) was assessed using Landsat 8 scenes over 15 regions selected worldwide. A mean Commission Error (CE) of 1.5% was obtained while a mean Omission Error (OE) of 15.4% was obtained for minimum Water Surface Ratio (WSR) = 0.5 and drops to 9.8% for minimum WSR = 0.6. Here, WSR is defined as the fraction of the PROBA-V pixel covered by water as derived from high spatial resolution images, e.g., Landsat 8. Both the CE = 1.5% and OE = 9.8% (WSR = 0.6) fall within the user requirements of 15%. The WBDA is fully operational in the Copernicus Global Land Service and products are freely available.<\/jats:p>","DOI":"10.3390\/rs8121010","type":"journal-article","created":{"date-parts":[[2016,12,12]],"date-time":"2016-12-12T14:43:57Z","timestamp":1481553837000},"page":"1010","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Dynamic Water Surface Detection Algorithm Applied on PROBA-V Multispectral Data"],"prefix":"10.3390","volume":"8","author":[{"given":"Luc","family":"Bertels","sequence":"first","affiliation":[{"name":"Center for Remote Sensing and Earth Observation Processes, Flemish Institute for Technological Research (VITO), Boeretang 200, B-2400 Mol, Belgium"}]},{"given":"Bruno","family":"Smets","sequence":"additional","affiliation":[{"name":"Center for Remote Sensing and Earth Observation Processes, Flemish Institute for Technological Research (VITO), Boeretang 200, B-2400 Mol, Belgium"}]},{"given":"Davy","family":"Wolfs","sequence":"additional","affiliation":[{"name":"Center for Remote Sensing and Earth Observation Processes, Flemish Institute for Technological Research (VITO), Boeretang 200, B-2400 Mol, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2016,12,10]]},"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":"Green","year":"2000","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1126\/science.1089802","article-title":"Tracking fresh water from space","volume":"301","author":"Alsdorf","year":"2003","journal-title":"Science"},{"key":"ref_3","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 epidemics in Senegal","volume":"106","author":"Lacaux","year":"2007","journal-title":"Remote Sens.  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