## 1 Introduction  
Flooding is among one of the most destructive disasters which results in tremendous economic and human losses worldwide [1; 2; 3; 4]. It is of great significance to map accurately the extent of inundated areas and the land cover types under water [4, 5], which can assist in flood monitoring, relief works planning and damage assessment. Due to its synoptic view and continuous coverage of flooding events, remote sensing has been recognized as a powerful and effective tool to provide inundation maps in near real time according to many researches [1; 2; 3; 4; 5; 6; 7; 8]. Generally, remotely sensed data used for flood monitoring are mainly collected from radar and optical satellites. The advantage of radar remote sensing is that it enables data acquisition regardless of weather conditions and time of day [7]. Space-borne sensors such as Synthetic Aperture Radar (SAR) [8] are capable of penetrating the cloud, and can provide views of the extent of inundation even when thick clouds exist above the disaster-stricken areas [1; 2; 6; 7; 8]. [PERSON] _et al._[9] utilized visual interpretation and thresholding algorithms for multi-temporal ERS-1 (European Remote Sensing satellite) SAR data in the determination of inundated areas at the peak of the flood. Results showed that only 20% of the flooded areas were determined due to the time delay between the flood peak and the satellite overpass [9]. To tackle this limitation, [PERSON] _et al._[9] proposed a new procedure with the synthetic use of SAR data and digital topographic data from a Geographical Information System (GIS) technique and a high proportion, 96.7%, of the flooded areas was detected [9]. Although optical sensors are unable to penetrate thick clouds which is a major drawback in flood monitoring, the images acquired under cloud free conditions can still be utilized to extract flooded areas with high accuracy [10; 11; 12]. Besides, optical remote sensing can provide true color images which are much easier for visual interpretation than radar data. Widely used optical remote sensing data are mainly from Landsat series, _i.e._, Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), SPOT (Systeme Probatoire d'Observation de la Terre) series (_i.e._, SPOT-4, SPOT-5), and Ikonos due to their ideal combination of spatial and temporal resolutions, easy access, and ease in data processing and analysis [4]. [PERSON] _et al._[10] used medium resolution (30 m) TM images to delineate the maximum flood event caused by Hurricane Floyd in North Carolina. [PERSON] [11] proposed a model based on Artificial Neural Network to generate a floodplain map using high-resolution (1 m) Ikonos imagery and digital elevation model (DEM). [PERSON] _et al._[12] utilized multisensor data (TM and SPOT-4) to map Hurricane Katrina's widespread destruction in New Orleans and adopted a change detection method to extract the land cover types under water. Above all, optical remote sensing can also play an important role in flood mapping given cloud free conditions. Nevertheless, mixed pixels are common in medium spatial resolution data such as TM and ETM+, and such pixels have been recognized as a problem for remote sensing applications [13; 14; 15; 16; 17; 18; 19; 20]. In terms of flood monitoring using medium resolution data, flooded and non-flooded features may co-existwithin one pixel, resulting in the challenge to extract these small and fragmented flooded patches especially under complex urban landscapes. To deal with the mixed pixel problem, several approaches such as linear spectral mixture analysis (LSMA) [13], fuzzy-set possibilities [14], and Bayesian possibilities [15] have been developed to partition the proportions of each pixel between classes. Among these methods, LSMA appears to be the most promising and has been widely used to extract sub-pixel information with physical meaning [13, 16, 17, 18, 19]. LSMA assumes that the reflectance of each pixel can be modeled as a linear combination of a few spectrally pure land cover components, known as endmembers [13].