## 5 Discussion
[PERSON] _et al._ (2012) [47] utilized linear spectral mixture analysis as part of the proposed sub-pixel analysis methodology to identify flooded areas from MODIS remote sensing data. The proposed methodology was demonstrated to be effective for mapping the flood extent with an accuracy of 80% [47]. However, the proposed approach of our paper outperformed that of [PERSON] _et al._ One possible reason lies in that [PERSON] _et al._ only adopted the LSMA model, which used an invariant set of endmembers (water, vegetation, _etc._) to model the complex flooded landscape and could not account for the within-class variance [21]. On the other hand, MESMA allowed the type and number of endmembers to vary on each pixel [22], which could yield more accurate fractional information than LSMA. Artificial neural network was employed in [PERSON]'s study (2010) [11] to classify the high-resolution Ikonos image to determine the inundated classes after flooding. A multi-layer perceptron neural network was chosen due to its ability to implement nonlinear decision functions and the fact that no prior conjectures needed to be made for the input data [11]. Results showed that the utilization of ANN achieved an accuracy of 70% and outperformed MLC with an accuracy increase of 15% [11]. Meanwhile, the proposed MESMA\(+\)RF method outperformed that of [PERSON]. One possible interpretation is that ANN has the drawback of a low generalization capability due to over-fitting of the training data, which leads to the decline of performance in predicting new datasets. However, RF uses a bootstrap strategy to generate independent training samples to tackle the problem of over-fitting, which accounts for the higher accuracy when compared with ANN. In addition, as one important method in remote sensing image classification, object based image analysis has also been applied in detecting inundated areas. [PERSON] _et al._ (2013) [8] utilized Geographic Object-Based Image Analysis (GEOBIA) and Landsat TM data for flood area delineation. Results indicated that the proposed GEOBIA based method showed high performance and attained an overall accuracy of 92.67% in inundated-areas detection [8]. The advantage of OBIA in flood mapping is the ability to incorporate semantic knowledge in the classification process, thus restricting limitations resulting from imagery characteristics and temporal availability [8]. The RF classifier used in this paper is a pixel-based method, although it has a similar accuracy to that of OBIA, it may still cause a \"salt and pepper\" effect in the classification results. Therefore, future study should be focused on incorporating OBIA into the RF classifier to further increase the accuracy of flood mapping. Although the above discussion verifies the high performance of the proposed approach of the study, there are still some limitations which can be stated as follows. First, the optical sensor's inability to penetrate the canopies of forests can cause the underestimation of flooded areas in the woodland region. The fact that only 0.075 km\({}^{2}\) of woodland was detected as flooded in this study supported this point of view. This is in accordance with [PERSON]'s study [10], which found that scattered \"holes\" or \"islands\" exited in the submerged forest areas. To tackle this issue, radar data should be incorporated and future study should focus on the fusion of multi-sensor (optical and radar) data to increase flood mapping accuracy. Second, the underestimation of flooded areas in the built up regions was also a problem. This is due to the fact that some submerged roads are too narrow to be detected in the medium resolution HJ data (30 m). The buildings adjacent to the submerged roads usually have a very high reflectance and will cover the signal of the flooded roads within one pixel. Although MESMA can tackle the mixed pixel problem to some degree, however, high resolution remote sensing data should be used in order to generate more accurate flood maps in built up areas such as city blocks and factories.