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Tropical floodplains, such as Pantanal in Central South America, are important features for land-atmosphere interactions. Schemes to account for floodplains should therefore be included in Earth System Models, but this requires observations of flooded area for validation. Satellite data is a possible solution to estimate the flooded area but it is important to evaluate the different flood detection algorithms available in order to use the most efficient for the region. This work explores different methods to estimate the flooded area from the MODIS MOD09A1 satellite surface reflectance product using spectral indexes (mNDWI, NDMI, NDMI-NDVI) to detect the presence of water. We include the traditional threshold-based methods but also some unsupervised classification methods such as the k-means and the Principal Component Analysis applied on the water-related spectral indexes. The calibration and validation of these methods are based on the hydrological knowledge of the region, coming from land surface models, river discharge observation and from previous satellite estimations of the flooded area. The NDMI index seems too sensible to the vegetation which leads to error in the estimation of the flooded area. The other methods were spatially and temporally consistent with previous studies over the Pantanal. [ABSTRACT FROM AUTHOR]