## 5 Discussion  
Experimental results demonstrated that both the flood map and pre-flood land cover map can be derived accurately from the proposed method, which provides reliable and valuable information for flood management and hazard assessment. The advantage of the proposed method lies in the integration of MESMA and RF classifier. Specifically, due to its capability to account for the spectral and spatial variability of complex landscapes [21], the adoption of MESMA can tackle the mixed pixel problem of flood mapping when using medium resolution remote sensing data. In previous studies [20, 21, 22, 23, 24, 25, 26, 27], MESMA was successfully utilized in several application fields, such as mapping vegetation types under complex urban environment [21], mapping burn severity in Mediterranean countries from moderate resolution satellite data [22], and mapping urban land cover types using HyMap hyperspectral data [26], _etc._ This study introduced MESMA into the field of flood mapping, which broadens its range of application and provides a new clue on deriving inundated areas accurately from medium resolution data. Besides, the adoption of the robust and efficient RF classifier also contributes to the high performance of this research. In previous studies of flood mapping [8], statistical classifier such as MLC was widely used. However, MLC is inferior to RF classifier according to several studies [29, 30, 31], therefore, we introduced RF classifier to improve the classification accuracy in the field of flood mapping. Meanwhile, it is necessary to compare with other literature on flood mapping to further verify the performance of the proposed approach. These state of art studies include water index (WI) [44, 45, 46], \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **MESMA + RF** & **RF-Only** & **MESMA+MLC** & **MLC-Only** & **NDWI-Thresholding** \\ \hline OA (\%) & 94 & 91.5 & 92 & 89 & 82.3 \\ Kappa index & 0.88 & 0.83 & 0.84 & 0.78 & 0.65 \\ \hline \hline \end{tabular} Notes: MESMA+RF, the proposed method; RF-only, using RF to classify the input image without fraction bands; MESMA+MLC, using MLC to classify the input image with fraction bands; MLC-only, using MLC to classify the input image without fraction bands. Maximum likelihood classifier (MLC). Multiple endmember spectral analysis (MESMA). Random Forest (RF). Normalized Difference Water Index (NDWI).  
\end{table} 
Table 7: Comparison of the classification results by different approaches. linear spectral mixture analysis [47], artificial neural network (ANN) [11] and object based image analysis (OBIA) [8]. To begin with, water index (e.g., NDWI) has been widely used for the detection of open surface water and inundated areas. The advantage of the water index lies in its simplicity and practicality, which can enhance open water features and suppress built up, vegetation and soil noise at the same time [44]. [PERSON] _et al._ (2012) [48] used three water indexes for delineating and mapping of surface water using MODIS (Terra) near real time images during the 2012 floods in Pakistan. The three water indexes included NDWI, Red and Short Wave Infra-Red (RSWIR) water index [49] and Green and Short Wave Infra-Red (GSWIR) water index [50]. Experimental results indicated the accuracy of NDWI, RSWIR, and GSWIR was 73.12%, 85.80%, and 81.54%, respectively, which was lower than the proposed MESMA\(+\)RF approach (94%). This was mainly due to the fact that the vegetation has relatively high reflectance in the NIR region, so the water indexes could not take water under vegetation into account [48]. Therefore, the co-existence of flooded water and vegetation within one pixel would lead to the underestimation of flooded areas when using water indexes. However, MESMA can derive the sub-pixel water information from the mixed pixels, which accounts for a higher accuracy than with the water index thresholding methods.