# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## Methodology
### Mapping Algorithm  
In our proposed algorithm, we have made an initial categorization of the Rice class into two subclasses (A and B) so as to take into account the different planting dates which cause differences in growth stages and hence temporal backscatter between rice fields. Such variations could trigger omissions in the Rice class. In the final map however, the Rice subclasses have been merged into a single Rice class. Our proposed mapping algorithm is described along the following paragraphs. A hierarchical knowledge-based decision rule classier has been employed in this study. As stated in [23; 47; 58], this knowledge can be heuristic, i.e., based on experience and reasoning. We have developed the classification rules based on our expert knowledge of class signatures and data characteristics using the per pixel information in each image, with its temporal behavior, and the corresponding temporal backscatter coefficients (dynamic range, minimum and maximum) and the single-date optical indices in each of the land-cover types. Dynamic and variable thresholds have been employed to discriminate paddy rice fields from the other land-cover. All 7 images in the co-registered layer stack described in Section 2.6 are used as input datasets in the decision tree. For the identification of Rice, the observed dynamic range of backscatter values in this class is from \(-23.5\) to \(-13.5\). As the Rice class showed the most dynamic backscatter range among the classes investigated and being that there is wide inter-field variability in planting dates and hence growth conditions, we have proposed two temporal thresholds for Rice discrimination. Firstly, all pixels less than or equal to \(-19.0\) dB and greater than or equal to \(-17.0\) dB from b1 (9 June) to b2 (8 July) or from b1 to b3 (1 August), or from b2 to b3, and with b6 (NDVI) greater than or equal to 0.3 but less than 0.5 are classified as Rice. Secondly, all pixels less than or equal to \(-18.0\) dB and greater than or equal to \(-16.0\) dB from b1 to b2 or from b1 to b3, or from b2 to b3, with b6 greater than or equal to 0.3 but less than 0.5 are classified as Rice. NDVI is derived from a 3 August image. From field information, late planting of paddy rice in this area in 2015 was done within the first week of July. In view of the observed paddy rice phenology in this area, at 3 August, even late planted rice is already \(3-4\) weeks old, corresponding to a part of the vegetative stage, in which NDVI values are no less than 0.3 [59]. These rules are expected to reduce, if not eliminate confusions involving Rice. For the identification of Water, the maximum backscatter value of water or flooded surfaces is below \(-18.0\) dB. Therefore, all pixels for which after the extraction of Rice were less than \(-18.0\) dB at any three consecutive dates of imaging (b1\(-\)b3, b2-b4, or b3-b5), and with a positive MNDWI and negative NDVI are classified as Water. Between subsequent dates of SAR imaging, a backscatter increase could be recorded over a water surface due to an increase in surface roughness caused by moving elements such as winds or boats as is mostly common along rivers and coastlines. The introduction of a consecutive three-date temporal backscatter below \(-18.0\) dB could offset the effect of isolated increases in backscatter between dates of SAR imaging. Additionally, the use of optical indices could eliminate the influence of any spontaneous increase in temporal backscatter. For the mapping of Built, all pixels greater than \(-13.0\) dB at any three consecutive dates of imaging (b1-b3, b2-b4, or b3-b5), and with a negative NDVI were classified into this category. This class exhibited the highest temporal backscatter values due primarily to corner reflections from buildings and other vertical objects [45]. As we have placed other low backscatter surfaces such as asphalt pavements into the class \"Others\" and as the temporal backscatter profile of Built is far removed from that of Water, the MNDWI image was not applied in the mapping of Built.