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Automatic Detection of Inland Water Bodies along Altimetry Tracks for Estimating Surface Water Storage Variations in the Congo Basin
Surface water storage in floodplains and wetlands is poorly known from regional to global scales, in spite of its importance in the hydrological and the carbon balances, as the wet areas are an important water compartment which delays water transfer, modifies the sediment transport through sedimenta...
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Published in: | Remote sensing (Basel, Switzerland) Switzerland), 2021-10, Vol.13 (19), p.3804 |
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description | Surface water storage in floodplains and wetlands is poorly known from regional to global scales, in spite of its importance in the hydrological and the carbon balances, as the wet areas are an important water compartment which delays water transfer, modifies the sediment transport through sedimentation and erosion processes, and are a source for greenhouse gases. Remote sensing is a powerful tool for monitoring temporal variations in both the extent, level, and volume, of water using the synergy between satellite images and radar altimetry. Estimating water levels over flooded area using radar altimetry observation is difficult. In this study, an unsupervised classification approach is applied on the radar altimetry backscattering coefficients to discriminate between flooded and non-flooded areas in the Cuvette Centrale of Congo. Good detection of water (open water, permanent and seasonal inundation) is above 0.9 using radar altimetry backscattering from ENVISAT and Jason-2. Based on these results, the time series of water levels were automatically produced. They exhibit temporal variations in good agreement with the hydrological regime of the Cuvette Centrale. Comparisons against a manually generated time series of water levels from the same missions at the same locations show a very good agreement between the two processes (i.e., RMSE ≤ 0.25 m in more than 80%/90% of the cases and R ≥ 0.95 in more than 95%/75% of the cases for ENVISAT and Jason-2, respectively). The use of the time series of water levels over rivers and wetlands improves the spatial pattern of the annual amplitude of water storage in the Cuvette Centrale. It also leads to a decrease by a factor of four for the surface water estimates in this area, compared with a case where only time series over rivers are considered. |
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Remote sensing is a powerful tool for monitoring temporal variations in both the extent, level, and volume, of water using the synergy between satellite images and radar altimetry. Estimating water levels over flooded area using radar altimetry observation is difficult. In this study, an unsupervised classification approach is applied on the radar altimetry backscattering coefficients to discriminate between flooded and non-flooded areas in the Cuvette Centrale of Congo. Good detection of water (open water, permanent and seasonal inundation) is above 0.9 using radar altimetry backscattering from ENVISAT and Jason-2. Based on these results, the time series of water levels were automatically produced. They exhibit temporal variations in good agreement with the hydrological regime of the Cuvette Centrale. Comparisons against a manually generated time series of water levels from the same missions at the same locations show a very good agreement between the two processes (i.e., RMSE ≤ 0.25 m in more than 80%/90% of the cases and R ≥ 0.95 in more than 95%/75% of the cases for ENVISAT and Jason-2, respectively). The use of the time series of water levels over rivers and wetlands improves the spatial pattern of the annual amplitude of water storage in the Cuvette Centrale. It also leads to a decrease by a factor of four for the surface water estimates in this area, compared with a case where only time series over rivers are considered.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><identifier>DOI: 10.3390/rs13193804</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Altimeters ; Altimetry ; Backscattering ; Climate ; Congo ; Environmental Sciences ; Estimation ; Flooded areas ; Floodplains ; Floods ; Greenhouse gases ; Hydrologic regime ; Hydrology ; Inland waters ; Lakes ; Radar ; radar altimetry ; Radar imaging ; Remote sensing ; River networks ; Rivers ; Satellite imagery ; Sediment transport ; Software ; Surface water ; surface water storage ; Temporal variations ; Time series ; Topography ; Vegetation ; Water levels ; Water storage ; Water transfer ; Wetlands</subject><ispartof>Remote sensing (Basel, Switzerland), 2021-10, Vol.13 (19), p.3804</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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Remote sensing is a powerful tool for monitoring temporal variations in both the extent, level, and volume, of water using the synergy between satellite images and radar altimetry. Estimating water levels over flooded area using radar altimetry observation is difficult. In this study, an unsupervised classification approach is applied on the radar altimetry backscattering coefficients to discriminate between flooded and non-flooded areas in the Cuvette Centrale of Congo. Good detection of water (open water, permanent and seasonal inundation) is above 0.9 using radar altimetry backscattering from ENVISAT and Jason-2. Based on these results, the time series of water levels were automatically produced. They exhibit temporal variations in good agreement with the hydrological regime of the Cuvette Centrale. Comparisons against a manually generated time series of water levels from the same missions at the same locations show a very good agreement between the two processes (i.e., RMSE ≤ 0.25 m in more than 80%/90% of the cases and R ≥ 0.95 in more than 95%/75% of the cases for ENVISAT and Jason-2, respectively). The use of the time series of water levels over rivers and wetlands improves the spatial pattern of the annual amplitude of water storage in the Cuvette Centrale. It also leads to a decrease by a factor of four for the surface water estimates in this area, compared with a case where only time series over rivers are considered.</description><subject>Accuracy</subject><subject>Altimeters</subject><subject>Altimetry</subject><subject>Backscattering</subject><subject>Climate</subject><subject>Congo</subject><subject>Environmental Sciences</subject><subject>Estimation</subject><subject>Flooded areas</subject><subject>Floodplains</subject><subject>Floods</subject><subject>Greenhouse gases</subject><subject>Hydrologic regime</subject><subject>Hydrology</subject><subject>Inland waters</subject><subject>Lakes</subject><subject>Radar</subject><subject>radar altimetry</subject><subject>Radar imaging</subject><subject>Remote sensing</subject><subject>River networks</subject><subject>Rivers</subject><subject>Satellite imagery</subject><subject>Sediment transport</subject><subject>Software</subject><subject>Surface water</subject><subject>surface water 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Detection of Inland Water Bodies along Altimetry Tracks for Estimating Surface Water Storage Variations in the Congo Basin</title><author>Frappart, Frédéric ; Zeiger, Pierre ; Betbeder, Julie ; Gond, Valéry ; Bellot, Régis ; Baghdadi, Nicolas ; Blarel, Fabien ; Darrozes, José ; Bourrel, Luc ; Seyler, Frédérique</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c395t-f2508933ef774223e191fd3797e4ad0c2fff9c689441613fc5af88d38a3b61863</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Altimeters</topic><topic>Altimetry</topic><topic>Backscattering</topic><topic>Climate</topic><topic>Congo</topic><topic>Environmental Sciences</topic><topic>Estimation</topic><topic>Flooded areas</topic><topic>Floodplains</topic><topic>Floods</topic><topic>Greenhouse gases</topic><topic>Hydrologic regime</topic><topic>Hydrology</topic><topic>Inland 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Remote sensing is a powerful tool for monitoring temporal variations in both the extent, level, and volume, of water using the synergy between satellite images and radar altimetry. Estimating water levels over flooded area using radar altimetry observation is difficult. In this study, an unsupervised classification approach is applied on the radar altimetry backscattering coefficients to discriminate between flooded and non-flooded areas in the Cuvette Centrale of Congo. Good detection of water (open water, permanent and seasonal inundation) is above 0.9 using radar altimetry backscattering from ENVISAT and Jason-2. Based on these results, the time series of water levels were automatically produced. They exhibit temporal variations in good agreement with the hydrological regime of the Cuvette Centrale. Comparisons against a manually generated time series of water levels from the same missions at the same locations show a very good agreement between the two processes (i.e., RMSE ≤ 0.25 m in more than 80%/90% of the cases and R ≥ 0.95 in more than 95%/75% of the cases for ENVISAT and Jason-2, respectively). The use of the time series of water levels over rivers and wetlands improves the spatial pattern of the annual amplitude of water storage in the Cuvette Centrale. 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subjects | Accuracy Altimeters Altimetry Backscattering Climate Congo Environmental Sciences Estimation Flooded areas Floodplains Floods Greenhouse gases Hydrologic regime Hydrology Inland waters Lakes Radar radar altimetry Radar imaging Remote sensing River networks Rivers Satellite imagery Sediment transport Software Surface water surface water storage Temporal variations Time series Topography Vegetation Water levels Water storage Water transfer Wetlands |
title | Automatic Detection of Inland Water Bodies along Altimetry Tracks for Estimating Surface Water Storage Variations in the Congo Basin |
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