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Article

Combining Multi-Sensor Satellite Imagery to Improve Long-Term Monitoring of Temporary Surface Water Bodies in the Senegal River Floodplain

1
G-EAU, AgroParisTech, Cirad, INRAE, IRD, Montpellier SupAgro, University of Montpellier, Montpellier 34196 CEDEX 5, France
2
ISRA, BAME, BP 3120 Dakar, Senegal
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Leïdi Laboratory—Dynamics of Territories and Development, Gaston Berger University (UGB), BP 234 Saint-Louis, Senegal
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Department of Geography, Cheikh Anta Diop University (UCAD), BP 5005 Dakar, Senegal
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UFR S2ATA Agronomic Sciences, Aquaculture and Food Technologies, Gaston Berger University (UGB), BP 234 Saint-Louis, Senegal
6
Laboratory of Hydraulics and Fluid Mechanics, Faculty of Sciences and Techniques, Department of Physics, Cheikh Anta Diop University (UCAD), BP 5005 Dakar, Senegal
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(19), 3157; https://doi.org/10.3390/rs12193157
Received: 21 July 2020 / Revised: 4 September 2020 / Accepted: 14 September 2020 / Published: 26 September 2020
(This article belongs to the Special Issue Remote Sensing and Modeling of Land Surface Water)
Accurate monitoring of surface water bodies is essential in numerous hydrological and agricultural applications. Combining imagery from multiple sensors can improve long-term monitoring; however, the benefits derived from each sensor and the methods to automate long-term water mapping must be better understood across varying periods and in heterogeneous water environments. All available observations from Landsat 7, Landsat 8, Sentinel-2 and MODIS over 1999–2019 are processed in Google Earth Engines to evaluate and compare the benefits of single and multi-sensor approaches in long-term water monitoring of temporary water bodies, against extensive ground truth data from the Senegal River floodplain. Otsu automatic thresholding is compared with default thresholds and site-specific calibrated thresholds to improve Modified Normalized Difference Water Index (MNDWI) classification accuracy. Otsu thresholding leads to the lowest Root Mean Squared Error (RMSE) and high overall accuracies on selected Sentinel-2 and Landsat 8 images, but performance declines when applied to long-term monitoring compared to default or site-specific thresholds. On MODIS imagery, calibrated thresholds are crucial to improve classification in heterogeneous water environments, and results highlight excellent accuracies even in small (19 km2) water bodies despite the 500 m spatial resolution. Over 1999–2019, MODIS observations reduce average daily RMSE by 48% compared to the full Landsat 7 and 8 archive and by 51% compared to the published Global Surface Water datasets. Results reveal the need to integrate coarser MODIS observations in regional and global long-term surface water datasets, to accurately capture flood dynamics, overlooked by the full Landsat time series before 2013. From 2013, the Landsat 7 and Landsat 8 constellation becomes sufficient, and integrating MODIS observations degrades performance marginally. Combining Landsat and Sentinel-2 yields modest improvements after 2015. These results have important implications to guide the development of multi-sensor products and for applications across large wetlands and floodplains. View Full-Text
Keywords: wetlands; optical remote sensing; spatial accuracy; water bodies; Senegal River floodplain; Landsat; Sentinel-2; MODIS wetlands; optical remote sensing; spatial accuracy; water bodies; Senegal River floodplain; Landsat; Sentinel-2; MODIS
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MDPI and ACS Style

Ogilvie, A.; Poussin, J.-C.; Bader, J.-C.; Bayo, F.; Bodian, A.; Dacosta, H.; Dia, D.; Diop, L.; Martin, D.; Sambou, S. Combining Multi-Sensor Satellite Imagery to Improve Long-Term Monitoring of Temporary Surface Water Bodies in the Senegal River Floodplain. Remote Sens. 2020, 12, 3157. https://doi.org/10.3390/rs12193157

AMA Style

Ogilvie A, Poussin J-C, Bader J-C, Bayo F, Bodian A, Dacosta H, Dia D, Diop L, Martin D, Sambou S. Combining Multi-Sensor Satellite Imagery to Improve Long-Term Monitoring of Temporary Surface Water Bodies in the Senegal River Floodplain. Remote Sensing. 2020; 12(19):3157. https://doi.org/10.3390/rs12193157

Chicago/Turabian Style

Ogilvie, Andrew, Jean-Christophe Poussin, Jean-Claude Bader, Finda Bayo, Ansoumana Bodian, Honoré Dacosta, Djiby Dia, Lamine Diop, Didier Martin, and Soussou Sambou. 2020. "Combining Multi-Sensor Satellite Imagery to Improve Long-Term Monitoring of Temporary Surface Water Bodies in the Senegal River Floodplain" Remote Sensing 12, no. 19: 3157. https://doi.org/10.3390/rs12193157

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