# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## 3 Results
### Mapping Results from Sentinel-1A and Landsat 8
Excluding the Rice subclasses, the class \"Others\", which comprises bare soils, paved roads, herbaceous cover, grass and other low biomass crops, showed a temporal backscatter profile higher only than that of Water. This is due to the significant influence of roads in the class \"Others\". Even in optical images, asphalt materials could show low reflectance. From a visual inspection of the individual pixels of highways and airport runways, we observed a low temporal backscatter closer to the maximum backscatter value recorded at the flooding stage of rice. The temporal backscatter profile of the class \"Others\" is approximately mid-way between the peaks and troughs of rice temporal backscatter. As stated earlier, the first mapping results presented in this study are only based on five VH images from Sentinel-1A. We have employed as a classification feature, the temporal increase in rice backscatter during the first two months of growth, and its steady decline thereafter relative to the nearly uniform temporal backscatter profiles of the other land-cover classes. The resultant land-cover map from this approach shows serious misclassifications of Rice, Water, Trees and Others. Even though almost all validation Rice pixels were correctly classified, there were some commission errors in this class. Several Water pixels have been misclassified as Rice. This is one major source of uncertainty in paddy rice mapping algorithms solely based on the temporal increase in backscatter. It is important to note that an increase in surface roughness, which could be induced by moving objects such as wind or boats, could increase the backscatter value of a given pixel between SAR acquisitions [28]. If this backscatter increase is within the observed dynamic range of paddy rice backscatter and also fall within the lower and upper limit thresholds, such a pixel will be categorized as Rice. Several confusions also exist between Trees and Others, and between Water and Others. Figure 6 shows a relatively large area covered by Trees, a condition that is very unlikely in this area. Much of the pixels of the class \"Others\", especially those of herbaceous plants, have been misclassified as Trees. Several pixels of the class \"Others\" have equally been misclassified as Water due to significantly low backscatter profiles of areas covered by grass and asphalts pavements. In order to eliminate some of these confusions, we have combined the temporal VH images with the NDVI and MNDWI images. The introduction of the aforementioned optical indices into our mapping algorithm brought a significant reduction in inter-class confusions as evident in the respective producer's and user's accuracies. In the Rice class, these optical indices eliminated all the commission errors derived from omissions in the other classes. Our study area is not only located along the coast but is also home to large rivers including the famous Huangpu River. As boats and other marine vessels frequently sail along these waterways, the backscatter of a given pixel could increase between dates of imaging. Furthermore, this area is prone to subtropical monsoon winds, which occur during the rice growing season. Thus, sporadic or spontaneous increases in surface roughness and, hence, SAR backscatter are most likely, which could lead to misclassification of Water pixels as Rice. Water and Built typically have negative NDVI values, and therefore, the introduction of this optical index removed all Water and Built pixels misclassified as Rice on the basis of a temporal increase in backscatter. In Table 5, half of the commission errors in the Rice class are accounted for by the class \"Others\". With the introduction of the condition, 0.5 > NDVI \(\geq\) 0.3, all commission errors in the Rice class derived from omissions in \"Trees and Others\" were eliminated. It is assumed that at 3 August (date of optical indices), rice plants in the study area would have acquired enough surface area for NDVI to be greater than or equal to 0.3, but less than 0.5.