# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## 1 Introduction
Three main components account for the observed temporal radar response produced by the interaction of electromagnetic waves and the vegetation canopy; surface scattering, volume scattering, and surface scattering attenuated by the vegetation volume [35; 37]. Prior to sowing/planting, paddy rice fields are flooded and a characteristic low backscatter is observed at this stage due to a dominance of smooth or specular reflections from the water surface [36]. As the rice grows, a significant increase in backscatter is recorded as a result of multiple scattering (double-bounce) effects involving the water surface and the vertical elements of the crop [35]. It is this temporal backscatter change that has been exploited to discriminate the rice crop from other surfaces using microwave data such as Sentinel-1 [38; 39]. A number of studies have explored the comparative advantages of integrating optical and microwave datasets for more efficient and accurate crop classifications. These research works have employed classification algorithms ranging from the simple and traditional supervised classifiers of maximum likelihood (MLC) to the more complex machine learning algorithms such as support vector machine (SVM), random forest (RF), and classification and regression trees (CART). In this study, we have explored both optical and microwave datasets to map paddy rice based on a simple decision tree algorithm. We have combined five images from the new Sentinel-1A synthetic aperture radar (SAR) with one Landsat 8 Operational Land Imager (OLI) image to discriminate paddy rice in a subtropical and urban landscape in Shanghai, southeast China, at the 2015 rice growing season. The test site selected in this study is one of the most populated, industrialized and urbanized areas in Southeast Asia. Even though rice cultivation is not a major economic activity in this area, being a major staple food warrants its monitoring to provide data of relevance to urban food supply. To better understand the relative contributions of economic sectors to greenhouse gas emissions in urbanized and industrialized landscapes around the globe, it is extremely important to monitor urban rice agriculture as a major source of methane. With the continuous increase in the population of Shanghai mostly as a consequence of rural-urban migration, rice farming also provides an alternative source of livelihood for migrants unabsorbed by the industrial labor market. Moreover, urban and industrial areas such as Shanghai are faced with the challenges of meeting water demands for household and industrial purposes. Rice in Shanghai is grown in paddies and as a result of insufficient rainfall, irrigation is often practiced to meet growth requirements of this water-loving crop. In view of the above, it is therefore of essence, to develop methods that enhance effective mapping of rice agriculture in highly industrialized urban areas such as Shanghai, as data derived from such studies could have implications for social and environmental policies. In addition to the aforementioned, it is worth noting that most paddy rice mapping projects using satellite remote sensing data are conducted in rural and more agriculturally intensive landscapes, in or proximal to river basins, inland valley swamps, flood plains and estuarine deltas. Limited efforts have been directed towards the mapping of urban agriculture where rice fields in particular, are characterized by fuzzy boundaries, small sizes, and are also highly fragmented by other land-uses. Wide inter-field variability due primarily to differences in sowing or transplanting dates, crop variety, soils and management practices have intensified the challenges of paddy rice mapping in urban agricultural landscapes, and to ameliorate this, higher spatial resolution and multi-source information are required to adequately discriminate urban rice fields.