## 1 Introduction  
Although food security is fundamental to human existence, the world is moving backward in its efforts to end hunger, food insecurity, and malnutrition [1]. There are still 3.1 billion people worldwide that do not have access to a healthy diet. As one of the most common food staples, rice cultivation is extremely important for maintaining food security [2]. Rice monitoring based on remote sensing can efficiently and cost-effectively provide rich information on planting areas, growth, yields, disasters, environments, and resource utilization [3,4]. With in-depth research on rice remote sensing classification methods, a number of large-scale regional rice mapping achievements have emerged [5,6]. Compared to other dryland crops, rice, as a paddy field crop, shows remarkable characteristics in both active and passive satellite remote sensing images. Based on this, a series of remote sensing classification methods for rice have been developed. Among these methods, one extracts rice using an expert knowledge-based decision tree or machine learning based on the significant water body features in rice fields during the transplanting period (i.e., Land Surface Water Index [LSWI] or Modified Normalized Difference Water Index [MNDWI]), combined with the significant vegetation characteristics of rice fields during the middle and late growth periods (i.e., Normalized Difference Vegetation Index [NDVI] and Enhanced Vegetation Index [EVI]). [PERSON] et al. [7, 8, 9] proposed the phenology- and pixel-based paddy rice mapping (PPPM) method based on MODIS time-series images and produced rice maps with a resolution of 500 m in South China, South Asia, and Southeast Asia. [PERSON] et al. [10] proposed a sophisticated rice mapping method based on the time-series EVI, LSWI, and variable EVI/LSWI threshold function to identify mixed paddy rice fields from MODIS images. [PERSON] et al. [11] obtained rice maps in Northeast Asia at a resolution of 30 m using Landsat images based on an improved PPPM method, achieving rice maps in Northeast China with a resolution of 10 m using the random forest method based on the vegetation index and texture features in time-series Sentinel-2 imagery [12]. [PERSON] et al. [13] obtained 10 m paddy rice maps for Northeast and Southeast Asia from 2017 to 2019 based on a series of rice features extracted from SAR and optical images. [PERSON] et al. [13,14] proposed the PhenoRice algorithm based on MODIS data and obtained rice maps for Italy, India, the Philippines, and Senegal. Other researchers have used this algorithm to identify rice in Nepal [15], Jiangsu [16], and California [17]. Based on the V-shaped characteristics of rice pixels in Sentinel-1 SAR images before and after transplanting, [PERSON] et al. [18] constructed recognition rules for rice in multiple experimental areas. Such methods generally have a good generalization ability and interpretability and have achieved great success in rice recognition at large regional scales [12,19]. However, whether building rice recognition rules based on expert knowledge or ordinary machine learning methods, they must all manually build and screen rice recognition features, such that the accuracy of rice mapping may be affected by the experts' subjective experience and ability, thus affecting the stability of model performance [20]. With the rapid development of deep-learning technology, crop mapping based on deep learning has gradually become a research hotspot. Compared with traditional methods, crop mapping methods based on deep learning can automatically extract rich and different types of features from massive raw satellite images and labels, which is more conducive to realizing accurate crop mapping in large-scale areas under complex scenarios [21, 22, 23, 24]. [PERSON] et al. [21] proposed the DeepCropMapping algorithm, which combines the long short-term memory and attention mechanism to achieve the high-accuracy identification of corn and soybeans in multiple regions of the United States (US) corn belt.