| 摘要: |
| 【目的】 土壤水分对于农作物的生长、生态系统的平衡、水资源的稳定具有深远影响，精确监测土壤水分空间分布与时空变化，对于农业生产和环境保护至关重要。雷达遥感具备全天时、全天候工作的优势，还可以穿透云层和作物获取地表和土壤的关键信息，在土壤水分反演中具有重要作用。【方法】 文章系统探讨了雷达遥感在土壤水分反演领域的研究进展，介绍了反演的理论模型、经验模型和半经验模型发展历程与特点，阐述了人工智能和极化分解方法在土壤水分反演中的应用，同时论述了光学与雷达遥感协同反演土壤水分的原理与应用，着重介绍了光学模型结合水云模型的协同反演方法。【结果】 土壤水分雷达反演存在着雷达遥感影像和验证数据获取成本较高、作物覆盖影响下的反演建模困难、光学与雷达数据融合难度较大等问题。【结论】 未来应建立高质量雷达数据集、研发定点观测仪器、推广半经验反演模型，以及将人工智能技术引入到多源数据融合领域等，以促进雷达遥感反演土壤水分精度与效率的提升。 |
| 关键词: 雷达遥感 合成孔径雷达 土壤水分反演 作物覆盖 |
| DOI：10.12105/j.issn.1672-0423.20240304 |
| 分类号: |
| 基金项目:国家自然科学基金“耦合叶和角果微波散射机理的区域油菜光合面积指数反演研究”（42271374）；中国农业科学院青年创新专项“油菜同化估产机理与方法”（Y2023QC18） |
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| Soil moisture retrieval by radar remote sensing |
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Zhu Yiqing, Wu Shangrong, Wang Di
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State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China/Institute of Agricultural Resources and Regional Planning，Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural Remote Sensing，Ministry of Agriculture and Rural Affairs，Beijing 100081，China
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| Abstract: |
| Purpose Soil moisture has profound impacts on crop growth，ecosystem balance，and water resource stability. Accurate monitoring of soil moisture spatial distribution and temporal variations is crucial for agricultural production and environmental protection. The development of remote sensing technology provides technical support for soil moisture inversion. Radar remote sensing，with its ability to work day and night and penetrate cloud cover and vegetation，plays an irreplaceable role in obtaining key information about land surface and soil in soil moisture inversion.Method This study systematically explored the research progress of radar remote sensing in soil moisture inversion. It introduced the development history and characteristics of theoretical models，empirical models，and semi-empirical models for inversion. It discussed the application of artificial intelligence and polarimetric decomposition methods in soil moisture inversion，and the principles and applications of optical and radar remote sensing synergies in soil moisture inversion，focusing on the synergistic inversion method combining optical models and water-cloud models.Result The article identified issues with radar soil moisture inversion，including the high cost of obtaining radar remote sensing images and validation data，difficulties in modeling inversion under crop cover，and challenges in integrating optical and radar data.Conclusion In the future，high-quality radar datasets should be established，fixed-point observation instruments should be developed，semi-empirical inversion models should be promoted，and artificial intelligence technology should be introduced into multisource data fusion，in order to promote the improvement of accuracy and efficiency in radar remote sensing soil moisture inversion. |
| Key words: radar remote sensing synthetic aperture radar soil moisture inversion crop cover |
