Next Article in Journal
Reversed Currents in Charged Liquid Bridges
Next Article in Special Issue
Surface Water Monitoring within Cambodia and the Vietnamese Mekong Delta over a Year, with Sentinel-1 SAR Observations
Previous Article in Journal
Using δ15N and δ18O Signatures to Evaluate Nitrate Sources and Transformations in Four Inflowing Rivers, North of Taihu Lake
Previous Article in Special Issue
Evaluation of the Water Cycle in the European COSMO-REA6 Reanalysis Using GRACE
Open AccessArticle

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
Water 2017, 9(5), 350; https://doi.org/10.3390/w9050350
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)
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
Show Figures

Figure 1

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop