水稻是重要的粮食作物，准确及时地获取水稻种植面积是精准分析粮食安全态势的基础。西南丘陵山区阴雨天气频发、水稻种植模式多样且实地采样成本高，难以实现高效、可靠的水稻遥感制图。本文以重庆市潼南区为研究区，利用多源遥感数据的互补优势，以耕地地块为基本单元，解析水稻在不同成像模式中的季候规律，提出了一种样本自动生成策略，并基于特征优选的随机森林模型实现水稻自动化制图。研究结果表明，该方法能够自动生成准确且具有代表性的训练样本数据，生成样本的时序特征与实地样本高度一致（光谱相关相似度0.987，动态时间规整距离4.719）。利用生成的样本进行水稻制图，总体精度高达89%，总面积提取误差为-7.5%，且制图不确定性较低。敏感性分析表明，水稻制图的准确性与稳定性受样本数量、空间分布及特征波段选择等因素影响。本研究可为丘陵山区水稻快速、精准制图提供一种可靠的样本自动化生成方案，并为样本抽样策略的制定和特征波段的选择提供科学依据。

Rice is a crucial global food crop, and the accurate, timely delineation of rice cultivation areas is vital for food security assessments and sustainable agricultural planning. Satellite remote sensing, integrated with advanced information technologies, has emerged as a pivotal tool for mapping spatial distribution patterns and monitoring growth dynamics of rice. However, in the hilly and mountainous regions of southwest China, frequent cloud cover, fragmented cultivation patterns, and high field sampling costs hinder efficient and reliable remote sensing-based rice mapping. To address these limitations, this study, focusing on Tongnan District in Chongqing Municipality, proposes a novel cropland parcel-scale rice automated sample generation and mapping framework, leveraging the complementary strengths of multi-source remote sensing data. The framework operates through several key stages: First, using cropland parcels as the basic unit, the optical-SAR time-series feature set is constructed by utilizing the multi-period Sentinel-1 and Sentinel-2 data. Secondly, adaptive identification of rice growth stages is achieved by analyzing temporal VH polarization features from SAR data, which are sensitive to vegetation structure and moisture variations. Subsequently, high-quality parcel-scale training samples are automatically generated by analyzing the rice seasonal pattern across different imaging modes, bypassing labor-intensive manual sampling. Based on the above samples, automated rice mapping is implemented by combining feature optimization and random forest. Finally, we evaluate and analyze the reliability of the generated samples and the rice mapping results. The results demonstrate four critical findings: (1) The method generates spectrally representative samples, achieving high consistency with field data (Spectral Correlation Similarity (SCS) = 0.987; Dynamic Time Warping (DTW) distance = 4.719). These samples exhibit spatial heterogeneity and broad coverage, addressing inefficiencies and biases inherent in traditional manual sampling. (2) Feature selection can effectively reduce the complexity of the model while ensuring classification accuracy. The contribution of transplantation period features is higher, and the importance of SAR features is higher than optical features. The two types of data have complementary advantages and can synergistically improve the classification robustness under cloudy conditions. (3) The automated rice mapping achieved an overall accuracy of 89%, an F1 score of 89%, and a total area extraction error of -7.5%, validating its reliability in spatially heterogeneous landscapes. In hilly mountainous areas with significant spatial heterogeneity, a moderate number of samples with uniform spatial distribution can help improve the stability and generalization ability of the model. In addition, rice exhibits significant clustering in flat, well-irrigated area. (4) The overall uncertainty of rice mapping in the study area was low (0.29), indicating that the mapping results were reliable. Uncertainty is mainly affected by the combination of environmental and agricultural management conditions, parcel morphology and sample characteristics. In the area with flat topography, favorable irrigation conditions, regular parcels and sufficient samples, the spectral characteristics of rice were significant, and the uncertainty of mapping was lower. This study provides a reliable automated sample generation approach for rapid and precise rice mapping in hilly and mountainous areas and offers scientific foundation for developing sampling strategies, selecting optimal feature bands and assessing uncertainty. In turn, it provides a reliable basis for precision agriculture planning and food security assessment.