## 1 Introduction  
Rice is consumed by more than half of the world's population and rice fields account for over 10% of the global cropland area. Globally, rice is the largest plant-based emitter of greenhouse gases [1]. Accurate and up-to-date information on how much rice can be produced is crucial to achieving food, climate, and water security. This is especially crucial in Malaysia, where rice is the main staple food. To ensure national food security, the government of Malaysia has set a goal of a 70% rice self-sufficiency level (SSL) [2]. Measuring the achievement of the SSL rice target requires accurate and up-to-date information on how many rice fields can be cultivated and how much of them can be harvested. Currently, information on rice fields relies on field survey data that are incomplete, time-consuming, and lack spatial details. Remote sensing technology has been widely used to map rice fields worldwide. Moderate Resolution Imaging Spectroradiometer (MODIS) data with a high temporal resolution have been widely used for 20 years [3; 4]. Although it can identify paddy fields, the coarse spatial resolution (500 m) makes it unhelpful in resolving smallholder paddy fields in Southeast Asia, which are usually less than 1 hectare [5; 6; 7; 8]. Recently, Sentinel-1 and 2 have begun to offer high spatial (10 m) and temporal resolution (12 day) data. In addition, Sentinel-2 has two-satellite constellations, and thus its combination provides a temporal resolution of 5 days. Many studies have shown that Sentinel-1 can be used successfully for mapping rice fields in various countries, including Vietnam [9; 10], China [11; 12; 13; 14], the USA and Spain [15], India [16; 17], the Mediterranean region [18], Iran [19], Bangladesh [20], South Korea [21], Indonesia and Malaysia [22], Philippines [23], Thailand [1; 24], and Myanmar [25]. Concurrently, Sentinel-2 was also successfully used to map rice fields in Egypt [26] and China [27; 28; 29]. Several studies have used the combination of Sentinel-1 and 2 data to map rice fields, especially in temperate regions, including Japan [30], India [31], and China [32; 33; 34; 35; 36; 37; 38]. However, most remote sensing studies have only mapped the rice field extent. In Southeast Asia, rice is grown throughout the year, and thus, mapping the growth stages of rice fields would be more valuable. Another challenge in the tropics is that the period and frequency of cropping are variable. Each region has its own cropping calendar--for example, East Java, Indonesia, has four cropping patterns, while West Java has three patterns [39]. Thus, this study aims to fill this research gap by applying a phenological-based approach to map rice growth stages using a combination of Sentinel-1 and Sentinel-2 at a national scale. The hypothesis is that the unsupervised classification of Sentinel-1 and 2 time-series data can identify rice fields and growth stages, because (1) the presence of flooding during the transplanting can be identified by Sentinel-1 VH backscatter; and (2) changes in the canopy of rice fields during growth stages (vegetative, generative, and ripening phases) up to the point of harvesting can be identified by Normalized Difference Vegetation Index (NDVI) time series. There are advantages and limitations for each satellite; Sentinel-1, a typical SAR, is not affected by clouds or illumination conditions. However, it has noise (speckles) that makes the map uncertain. On the other hand, Sentinel-2, an optical satellite image, is limited by cloud cover, but it provides a vegetation index directly related to rice growth stages. Mapping rice fields using remote sensing can be broadly grouped into machine learning techniques and phenology-based approaches. The machine learning approach is based on the calibration of field observations; while accurate, it requires lots of data. The phenology-based approach relies on the knowledge of the rice growth stages, which are related to the time series of reflectance data.