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Water 2017, 9(5), 350; doi:10.3390/w9050350

Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from SMOS Brightness Temperatures

1
Centre d’Etudes Spatiales de la BIOsphère (CESBIO—Université de Toulouse, CNES, CNRS, IRD), UMR5126, BPI 2801, 31401 Toulouse CEDEX 9, France
2
Laboratoire d’Etudes en Géophysique et Océanographie Spatiales (LEGOS), UMR5566, Université de Toulouse, CNES, CNRS, IRD, Observatoire Midi-Pyrénées (OMP), 14 Avenue Edouard Belin, 31400 Toulouse, France
3
Géosciences Environnement Toulouse (GET), UMR5563, Université de Toulouse, CNES, CNRS, IRD, Observatoire Midi-Pyrénées (OMP),14 Avenue Edouard Belin, 31400 Toulouse, France
4
Indo-French Cell for Water Sciences (IFCWS), IRD-IISc-NIO-IITM Joint International Laboratory, Bangalore 560012, India
5
INRA, UMR 1391 ISPA, F-33140 Villenave d’Ornon, Bordeaux, France
*
Author to whom correspondence should be addressed.
Academic Editor: Y. Jun Xu
Received: 23 February 2017 / Revised: 9 May 2017 / Accepted: 11 May 2017 / Published: 17 May 2017
(This article belongs to the Special Issue The Use of Remote Sensing in Hydrology)
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Abstract

Inland surface waters in tropical environments play a major role in the water and carbon cycle. Remote sensing techniques based on passive, active microwave or optical wavelengths are commonly used to provide quantitative estimates of surface water extent from regional to global scales. However, some of these estimates are unable to detect water under dense vegetation and/or in the presence of cloud coverage. To overcome these limitations, the brightness temperature data at L-band frequency from the Soil Moisture and Ocean Salinity (SMOS) mission are used here to estimate flood extent in a contextual radiative transfer model over the Amazon Basin. At this frequency, the signal is highly sensitive to the standing water above the ground, and the signal provides information from deeper vegetation density than higher-frequencies. Three-day and (25 km × 25 km) resolution maps of water fraction extent are produced from 2010 to 2015. The dynamic water surface extent estimates are compared to altimeter data (Jason-2), land cover classification maps (IGBP, GlobeCover and ESA CCI) and the dynamic water surface product (GIEMS). The relationships between the water surfaces, precipitation and in situ discharge data are examined. The results show a high correlation between water fraction estimated by SMOS and water levels from Jason-2 (R > 0.98). Good spatial agreements for the land cover classifications and the water cycle are obtained. View Full-Text
Keywords: water fraction extent; L-band; Amazon Basin water fraction extent; L-band; Amazon Basin
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Parrens, M.; Al Bitar, A.; Frappart, F.; Papa, F.; Calmant, S.; Crétaux, J.-F.; Wigneron, J.-P.; Kerr, Y. Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from SMOS Brightness Temperatures. Water 2017, 9, 350.

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