# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## 1 Introduction
However, such efforts have been hindered by the substantial shift in investment of major rice producing countries from agriculture to manufacturing, and the continuous conversion of agricultural land to residential and industry, mostly as a consequence of growing urbanization and industrialization. Additionally, urban agricultural land-uses such as vegetable gardening, fruit farming and aquaculture are increasingly encroaching into lands initially dedicated to rice farming. Globally, there is an increasing decline in rice planted areas [22] and the intensification of this trend in urban and peri-urban areas could have implications for global food security and food self-sufficiency. However, the loss of paddy rice area has occurred in tandem with intensification of rice farming through the use of agrochemicals to control pest and improve soil fertility, introduction of improved (short cycle and high yielding) varieties and mechanized agriculture. Monitoring rice cultivation is of great significance as such statistics could provide a means of early warning signals of food scarcity, and hence, supporting food security policies [23]. Perhaps the most widely and easily monitored variable of rice cultivation is acreage. Under the assumption of ceteris paribus (all other things being equal), an increase in the rice planted area should translate to an increase in rice productivity and food supply. Rice fields have also been noted to be major sources of methane, a notable green house gas [24]. Monitoring paddy rice fields is therefore of much relevance in understanding the global dynamics of greenhouse gas emissions, rising temperatures and climate change. Additionally, one of the most potent impacts of climate change is dwindlingwater resources [25]. Paddy rice is mostly flooded during its entire growth. Hence, data on the spatial distribution of this crop are of vital importance in formulating informed environmental policies aimed at the sustainable utilization and management of water resources [26]. Monitoring rice acreage with traditional ground-based surveys is time and labor consuming [27] and also requires huge finance for resource mobilization especially when it is implemented at large scales (regional to global) [28]. To ameliorate these cost limitations, satellite remote sensing has been increasingly applied to provide data on rice planted areas at a variety of spatial scales with minimal finance, time and human resources. The technology has been deemed efficient and is providing the much needed data to support a range of agricultural, environmental and development policies [4]. Both optical and microwave remote sensing data have been exploited to map and monitor rice growth [29]. However, as optical datasets are frequently contaminated by clouds in tropical areas, microwave datasets are increasingly being preferred especially in rice area estimation due to their all-weather imaging capability [30]. Microwave data are complementary to optical data, and in cloud-prone tropical regions, they could be the preeminent data for monitoring paddy rice fields. Mapping rice planted areas using optical satellite remote sensing data is primarily based on the exploitation of the crop's apparent reflectance characteristics in the visible near infrared (VNIR) portions of the electromagnetic spectrum. It has been observed that after twelve weeks of growth, the reflectance of paddy rice in the near infrared (NIR) reaches the highest value, while in the visible region it reaches the lowest value [31]. The diagnostic spectral reflectance exhibited by paddy rice during growth and their associated temporal vegetation indices have been mostly utilized to discriminate the crop from other land-cover categories using optical remote sensing datasets such as Landsat 8 imagery and the Moderate Resolution Imaging Spectroradiometer (MODIS) [29; 32; 33]. On the other hand, mapping paddy rice using microwave (radar) satellite data is based on the backscatter response [34], which is largely a function of growth stage and conditions [35; 36].