## 3 Results
### Model Predictions on the Image  
#### 3.4.1 Spectral Resampling  
Figure 7 shows the model predictions for all of the simulations as a series of maps with white (clean) and red (oil) pixels. Figure 7 is divided into three panels, A, B, and C, which correspond to maps of the entire scene, maps for a specific contaminated zone, and maps for a specific uncontaminated zone, respectively. As implied by the results in Table 3 and Figure 5, the hyperspectral sensors (excluding CASI) and WV3 display similar classification schemes, which is in good agreement with the EROS image, whereas some multispectral sensors present unsatisfying classification maps (SN2 and LS8) and some (ASTER and Self) moderate results. It is important to mention that the surface of the Evrona Nature Reserve (covered in Figure 7A) is quite homogenous in terms of stream channels, vegetation, and soil compositions. According to Figure 7, one might consider that the classification follows the stream and not the oil; however, there are many similar uncontaminated streams that the models did not indicate them as contaminated. Additionally, most of the different light intensities in the true-color  
Figure 6: Validation results for all of the sensors with all levels of additive noise. image (Figure 7A) are due to clouds. It also means that the stream channels that the oil flowed in are similar to the ones that the oil did not penetrate. In other words, the good agreement between the maps and the pattern of the spilled oil (also shown in Figure 12 in [7]) resulted from sensing the contamination itself and not from differences between the stream channels the oil \"chose\" to flow. Figure 7: _Cont._  
Figure 7: Model prediction (spectral resampling) for all of the sensors together with an EROS image (several days after the spill) and Fenix true color (two and a half years after the spill). Red pixels represent oil and white pixels represent clean. (**A**)—An overview of the entire study area. The areas denoted as 1 and 2 correspond to the scenes shown in (**B**) (contaminated scene) and (**C**) (uncontaminated scene), respectively. (**B**)—A scene with a clear outline of the oil contamination in narrow stream channels, which helps to appreciate the model performances. (**C**)—A scene with a relatively large amount of false positive (prediction of oil where it should be clean). Note that these maps are in 1 m GSD. The main area of misclassification (i.e., oil predictions in clean areas) seems to be in the northeast part of the map, which is specifically dominant in ASTER, LS8, SN2, Self, and even Hyperion, Fenix, and WV3 to some extent. It is less observable in EnMap, AVIRIS, and HyMap. This dominant misclassified area seems to occur on the boundaries of the image (it is nicely shown in ASTER, SN2, and LS8). A reason for this misclassification might be the off-nadir location of these pixels. Figure 7B,C allow for a deeper evaluation of the model performances, where Figure 7B focuses on a specific zone (marked as 1 in panel A) with a clearly visible oil flow pattern, and Figure 7C centers over an uncontaminated zone. The hyperspectral sensors illustrate a prediction map, which is similarly close to the flow outlines depicted in the EROS image (acquired two and a half years before the hyperspectral Fenix image). EnMap seems to attain more false-positives (prediction oil when it should be clean) than its hyperspectral counterparts. In the multispectral domain, WV3, Self, and ASTER also demonstrate good agreement between the maps, where WV3 and ASTER seem to have a larger number of false-positives in a similar location to EnMap classification map. A surprising result is that the map produced by LS8 shows a nice clean outline of the spill. However, when observing the performance of LS8 in panel C, one can see that the LS8 model performed poorly, as all of the pixels in the image (in Panel C) ought to be white (i.e., clean). A similar phenomenon can be observed in ASTER and Self.