## 1 Introduction  
Rice feeds more than half of the global human population [1] with more than 90% of rice production being in Asia [2]. Moreover, the International Food Policy Research Institute suggests that the demand for rice increases by 1.8% per year. Therefore, obtaining information on the rice location and distribution is important for food security and water use. Field survey-based agricultural statistical methods, which is the conventional approach to determine rice planting areas, is time- and labor-intensive. Tabular data obtained from this method lacks explicit spatial distribution information, and the data may be altered in regions where agriculture subsidies are provided. In contrast, remote sensing methods are an efficient and reliable approach to obtain spatially explicit and objective data. The main data sources are optical and synthetic aperture radar (SAR). Optical data provide spectral information of the surface and reflects the biochemical characteristicsof objects. However, the nature of passive imaging and its short wavelength means it is easily affected by atmospheric factors, such as clouds and cloud shadows. SAR data provide backscattering information in different polarizations for surface objects and captures their physical characteristics, such as spatial structure and dielectric properties. The nature of active imaging and its long wavelength has all-weather and day-and-night observation capabilities. [PERSON] and [PERSON] [3] reviewed the evolution of remote sensing methods for paddy rice mapping from the 1980s through 2015, in which the approaches were divided into four categories: (1) reflectance data and image statistics; (2) vegetation indices (VIs) and enhanced image statistics; (3) VI or radar backscatter-based temporal analysis; and (4) phenology through remote sensing recognition of key growth phases. The first and second categories use supervised or unsupervised algorithms, such as the maximum likelihood classifier, random forest, and support vector machine, which involve human intervention in sample collection or cluster labeling to build the models. The feasibility of such image- and statistics-based approaches has been limited to specific regions or periods due to the inherent spectral variability, which makes it difficult to perform large-scale monitoring of crop information over multiple years [4]. An example of the third category is rice cropping system maps as produced by Gumma [5] for South Asia. This method has generated accurate maps over wide areas, but its application to other regions would involve an additional round of time-consuming operator analysis and decision making for the classification, grouping, and labeling phases. The fourth approach defines a series of detection criteria based on the well-known temporal characteristics of rice cultivation. One of the most cited methods for rice detection in the fourth approach was proposed using the Spot Vegetation (VGT) data [6] and later MODIS data [7] to map rice flooding and rice cultivated areas in China and South and Southeast Asia [8]. This method is based on the detection of agronomic (i.e., deliberate) flooding of rice fields before the crop is established and its subsequent rapid growth within a fixed time after flooding. As these two characteristics are common to many rice cultivation practices (with the exceptions of upland rice, deepwater rice, dry direct-seeded rice, and other relatively minor rice ecosystems or rice management practices), this approach has the potential for operational monitoring of rice-cultivated areas at regional scales. However, demonstrations of this method have revealed that false positives can occur when applied in ecosystems different from those where it was originally developed [9] or when external knowledge of the local crop calendar and agro-practices are not considered [10].