# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## Methodology
### Field Data  
Prior to image classification and accuracy assessment, a field survey of rice fields cultivated in 2015 was conducted from April to May 2016 to obtain training and validation datasets. An ancillary field campaign was also conducted during rice maturity and harvest in November 2016. Rice fields in 2015 were identified through rice stubble (residue), as some of the paddies remained uncultivated after the previous rice harvest. Other 2015 rice fields, which were at the time of our survey cultivated with winter wheat or other crops, were verified through sign boards and interviews with local farmers. Figure 2 shows photos taken during the 2016 field campaigns. Figure 2: Spatial dynamics of rice cultivation in the study area: (**a**) field showing winter wheat at maturity in April 2016, but previously cultivated with rice in 2015; (**b**) field showing inter-field variability in rice planting time in June 2016, with the right part already planted and the left part prior to planting; (**c**) field showing a small rice plot (smallholder rice field) at maturity surrounded by residential and another agricultural land-use in November 2016; and (**d**) field showing adjacent rice plots in November 2016, one harvested and the other at maturity. Based on knowledge acquired during the 2016 field campaigns, we have proposed a simplified land-cover classification scheme for the area under study with five broad classes: Rice refers to paddy rice fields; Water includes rivers, lakes, streams and ponds; Built includes residential and industrial estates; Tress include forests and plants with considerable height and density; and Others include transitional areas, paved roads, bare land surfaces, grassland, other low biomass crops, and in the final map could include any or a combination of the other classes due to misclassification. We have adopted this broad classification scheme as the major focus of this study is rice field mapping. The broad land-cover classes of Water, Built, Trees and Others have also been taken into consideration as their coexistence with rice fields could induce confusions and influence Rice classification accuracy. A total of 150 points, each away from field boundaries, were randomly collected from paddy rice fields using a portable Garmin Global Positioning System (GPS) device, and an average horizontal accuracy of 4.0 m was recorded. From the 150 paddy points, 42 were randomly selected to guide the onscreen digitization of training polygons for the extraction of backscatter coefficients from the temporal Sentinel-1A images. Each of the digitized rice polygons had over 30 pixels. The remaining 108 points were reserved for accuracy assessment. As the other land-cover categories (Water, Built, Trees and Others) are broad and more easily recognizable, 30 training polygons (each over 50 pixels) in each class were directly digitized from high resolution and geo-referenced Google Earth images and exported to Sentinel-1A data for the extraction of temporal backscatter profiles. Additionally, 42 points from Water, and 50 points each from Built, Trees and Others, were randomly selected for the purpose of map accuracy assessment. [PERSON] [42] stated that without stratification, it is often difficult to find sufficient samples for classes that occupy a small portion of the study area. Therefore, we have intentionally increased the number of training and validation samples for the Rice class as it forms the major focus of our study. In a highly heterogeneous landscape such as urban Shanghai, accuracy assessment of the rice maps warrants a larger number of samples from the Rice class relative to other land-cover categories. According to [42], the number of samples can also be adjusted based on the relative importance of categories within the objectives of the project or the inherent variability within each category.