水稻是重要的粮食作物，及时准确地获取水稻种植面积和长势信息，可以为田间耕作管理和农业政策制定提供支撑。星载合成孔径雷达（SAR，Synthetic Aperture Radar）成像不受气象干扰，能敏锐地响应水稻植株发育和土壤水分变化，是多云雾地区水稻生长监测的重要数据源。水稻雷达遥感研究的成果丰富但脉络复杂，有必要结合关键问题和主要方法，对水稻雷达遥感的发展历程、现状和前景进行梳理和分析。本文在整理统计国内外近30年相关文献的基础上，将水稻雷达遥感的关键问题概括为种植面积提取、生理参数反演、物候与熟制识别三大焦点，将监测方法提炼为数理分析、机器学习和多源协同三条主线。其中，种植面积提取概括为四种思路：时序变化分析、机器学习、面向对象分类和多源协同，生理参数反演总结为五种算法：经验模型、物理模型、半经验模型、数据同化和多源协同，物候与熟制识别归纳为两种策略：时序检测和机器学习。从数据属性和模型结构的角度，介绍各种方法的工作原理和适用场景，阐述不同方法的优势和局限性。最后，结合SAR成像性能和计算机技术的发展态势，对未来研究进行展望。本文指出水稻雷达遥感监测尚有三大难题亟待解决：（1）破碎地块和复杂地形；（2）稻田栽培条件多样；（3）季候异步和轮作间作。今后研究应重点关注：（1）依赖更少先验信息的种植面积精细识别；（2）顾及建模效率与精度的生理参数动态反演；（3）结合生长机理和时序观测的物候熟制自动识别。上述课题的发展，将有效提升水稻雷达遥感监测的时空精度。

Objective Rice is one of the most productive food crops for Asian countries. Timely and accurate access to rice cultivation information can provide professional support for farming management and agricultural policy-making. Space-borne Synthetic Aperture Radar (SAR) imaging is free from meteorological interference and can sensitively respond to rice plant development and soil moisture changes. Therefore, satellites equipped with various SAR sensors are important data sources for rice growth monitoring in cloudy and foggy areas. The research progress of microwave remote sensing in rice growth monitoring has been made on all fronts, but the technical evolution and relationship of different research topics are complex and somewhat confusing. Method It is necessary to review and analyze the development history, current focus and innovation prospect of rice radar remote sensing, considering the key scientific problems and main experimental approaches. Based on the collation and statistics of relevant literatures in recent 30 years, this paper summarizes the key problems of rice radar remote sensing into three study focuses: planting area identification, biophysical parameter retrieval, phenology and cropping intensity recognition, then summarizes the technical methods into three research strategies: mathematical and physical analysis, machine learning, and multi-source data synergism. Result Specifically, rice planting area identification methods are divided into four schemes: time domain change analysis, machine learning, object-oriented classification and multi-source data synergism. Rice biophysical parameter retrieval methods are divided into five models: empirical model, physical model, semi-empirical model, data assimilation and multi-source data synergism. Rice phenology and cropping intensity recognition methods are divided into two algorithms: time-series feature detection and multi-temporal machine learning. From the perspective of data attributes and model structure, this paper introduces the theoretical basis and applicable conditions of different methods, and explains their advantages and limitations. Finally, in view of the rapid advance of SAR imaging capability and computer science, the future research issues are discussed. Conclusion This paper comes to a conclusion that there are three difficult points to be solved in rice radar remote sensing monitoring: (1) fragmented farmlands and fluctuant terrain; (2) diverse cultivation conditions; (3) asynchronous phenology and complex interplant. The future study should focus on: (1) High temporal-spatial resolution of rice planting area identification relying on less prior information; (2) Dynamic retrieval of rice biophysical parameters balancing model efficiency and accuracy; (3) Automatic recognition of rice phenology and cropping intensity combining plants growth mechanism and time series observation. The improvement of these research topics will profoundly promote the practical application of rice radar remote sensing.