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TitleTemporal soil moisture estimates from Radarsat-1 and Envisat ASAR for flood forecasting
DownloadDownloads (Preprint)
LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorDeschamps, A; Pultz, T J; Pietroniro, A; Best, K
SourceIEEE International Geoscience and Remote Sensing Symposium - IGARSS 2004, Anchorage, Alaska, September 20-24; 2004., Open Access logo Open Access
Alt SeriesEarth Sciences Sector, Contribution Series 20043290
Mediapaper; on-line; digital
File formatpdf
Released2004 01 01

Estimating the amount of water stored in a soil profile is essential in most water management projects and for assessing the hydrologic state of a basin. It determines infiltration during a rainfall event and controls evapotranspiration between storms. Rarely, however, are soil moisture data available for model input. In many cases, particularly watershed scale monitoring or modelling, soil moisture is inferred from more easily obtainable hydrologic variables such as rainfall, runoff and temperature.

As such, there is a strong need for procedures to estimate soil moisture in a watershed independently from the models. These procedures must provide not only basin average estimates but also the spatial distribution within a basin in order to meet the requirements of emerging distributed models. Active and passive microwave imagery are both candidate sources for these data. Active SAR imagery, with its high resolution, is particularly attractive for use in areas of mixed land cover.

This paper addresses the potential of Radarsat and Envisat ASAR Synthetic Aperture Radar (SAR) data to extract information on soil moisture at the watershed scale. Multiple Radarsat and Envisat data acquisitions collected over the Roseau River watershed, located in Manitoba, for the period of September 2002 through June 2003 were analyzed in relation to ground observations and meteorological conditions. A method was then developed to produce soil moisture maps for input to a hydrological model for flood forecasting.


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