## 1 Introduction  
### The Case for Synthetic Aperture Radar to Map Rice  
Rice is the most important crop for food security in Asia [1]. Despite rapid urbanization and diversification in consumption patterns in Asian countries, rice still accounts for 31% of the calorific intake and is by far the largest single source of calories for more than 3.7 billion people [2,3]. Reliable and regular subnational information on the area under production is an essential part of many countries' national accounting process, but statistical methods cannot always meet the needs of food security research and policy [4,5]. Nevertheless, this same information is the basis of policy decisions related to imports, exports and prices, which directly impact food security, especially amongst the poor [6, 7, 8]. Remote sensing promises scalable, low-cost and unbiased estimates of rice area to support, augment, improve or even replace survey and statistical methods [5]. However, there are technical challenges for the development of national-scale, operational, remote sensing-based rice crop information systems in Asia:  
1. More than 70% of the production is during the monsoon or rainy season [9], in which cloud cover is extensive and pervasive [10]. 2. Rice can be grown under a wide range of conditions and environments and, thus, is found in all corners of Asia [1,9]. 3. Rice cultivation is dominated by smallholders with field (paddy) sizes that are usually less than two hectares and, in many cases, less than one hectare [11]. 4. These paddies can form large contiguous areas, especially in coastal areas and deltas [4,9], but the tropical and subtropical climate in much of Asia means that rice can be cultivated with diverse cropping calendars and practices over very short distances [1,12]. The issue of cloud cover (i) can be addressed by using Synthetic Aperture Radar (SAR) imagery, and there is substantial literature on the suitability of SAR for rice crop mapping in the region (see Section 1.2). Optical images can complement SAR, but they cannot be relied upon as the main information source. Even with multi-temporal compositing, many consecutive days or weeks can be lost, which is particularly problematic for algorithms that rely on the detection of agronomic flooding at the start of the growing season [4, 13]. The wide geographic distribution of rice across Asia (i) necessitates wall-to-wall coverage to adequately capture the 144 million hectares [2] of rice area and suggests that automated or low-level supervised processing is required to do this. Automated processing would also be suited to low-cost cloud computing platforms and low-level operator supervision, which could offset the common bottlenecks of infrastructure, bandwidth and human capacity in emerging economies. The wide range of practices and environments (ii) means that rice detection algorithms should be generalizable and robust [13]. Such methods should show high skill for irrigated and rainfed rice, for short (\(<\)110 day), medium (110-130 day) and long (\(>\)130 day) maturities [1], and for different establishment practices, such as direct seeding or transplanting. The spatial complexity (iii) of rice environments requires high-resolution imagery, and the temporal complexity (iv) requires high-frequency acquisitions across many months of the year. In short, the remote-sensing requirements are non-trivial, and they go some way towards explaining the dearth of operational rice crop monitoring systems. However, recent and planned launches of SAR sensors coupled with state-of-the-art automated processing can provide sustainable solutions to this challenge to map and monitor one of the world's most important crops. This aim of this paper is to develop and test a rice area mapping method across multiple sites based on the knowledge of the temporal development of the rice crop under different conditions and its relation to backscatter.