## 1 Introduction
The aim of LSMA is to decompose a mixed pixel into the spectra of endmembers and estimate the fractional abundance of each endmember within a pixel. However, LSMA uses an invariant set of endmembers to model the entire landscape while the spectrum of each endmember is assumed to be constant across the image scene [20, 21, 22, 23]. It neglects the fact that the same material may have different spectral curves, which cannot account for within-class spectral variability. As an extension of LSMA, multiple endmember spectral mixture analysis (MESMA) allows the type and number of endmembers to vary for each pixel, which takes into account the spectral and spatial variability of the real complex landscapes [20, 21, 22, 23, 24, 25, 26, 27]. MESMA has been used in remote sensing fields especially in urban vegetation mapping [20, 21, 22, 23, 24, 25, 26, 27], but to our knowledge, it has been rarely used in flood monitoring. Thus, this study aims to justify the performance of MESMA in extracting flooded areas from medium resolution remote sensing data. Given the fact that most LSMA and MESMA applications have focused on the extraction of fractional information rather than thematic land cover types [21, 22, 23, 24, 25, 26, 27], this study attempted to introduce an image classifier, Random Forest (RF) [28], to classify the fraction maps generated from MESMA into different land cover categories. In general, Random Forest classifier has several advantages over other classification methods [29, 30, 31, 32, 33, 34, 35, 36, 37, 38]. It is easy to parameterize, it is non-sensitive to over-fitting and is good at dealing with outliers in the training data [29]. When compared to statistical methods such as the maximum likelihood classifier (MLC), Random Forest requires no assumptions of data distribution [29], which can improve the classification performance. When compared to other machine learning methods such as support vector machine (SVM), RF has the advantage of easier parameterization and better generalization capability [30]. The successful use of SVM depends on several experiments to search for the optimal combination of kernel function type, punishment coefficient, and the kernel parameter Gamma [30]. However, the parameterization of SVM can be time consuming and cannot match that of RF. Meanwhile, RF has shown similar or even higher classification accuracy than SVM according to several studies [29, 30]. RF has been widely used in remote sensing fields due to the above advantages. [PERSON] _et al._[32] utilized Random Forest for the Mediterranean land cover classification using multi-seasonal imagery and multi-seasonal texture, and the results indicated a high Kappa index of 0.92. [PERSON] _et al._[29] adopted Random Forest and texture analysis for urban vegetation mapping using high resolution UAV images and the results also showed a high classification accuracy of 90.6%. However, the usage of Random Forest in flood mapping has not been well documented and we are motivated to justify its performance in this study. Overall, the main objective of this study is to propose a hybrid method based on MESMA and RF classifier for flood mapping using medium resolution optical satellite data. Specifically, this paper aims to (i) extend the application of MESMA to the field of flood mapping and verify its performance;(ii) justify whether Random Forest classifier can show good performance in flood mapping; (iii) discuss the merits and demerits of the proposed method based on the comparison with other state of art studies.