mapping dynamic water fraction under the tropical rain forests of the amazonian basin from smos brightness temperatures

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ID: 176901
2017
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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.
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parrens2017watermapping Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Marie Parrens;Ahmad Al Bitar;Frédéric Frappart;Fabrice Papa;Stephane Calmant;Jean-François Crétaux;Jean-Pierre Wigneron;Yann Kerr
Journal Journal of food biochemistry
Year 2017
DOI
10.3390/w9050350
URL
http://www.mdpi.com/2073-4441/9/5/350
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