智慧农业（中英文）2025，Vol.7Issue(6)：75-95,21.DOI:10.12133/j.smartag.SA202509009
多极化合成孔径雷达作物覆盖下土壤湿度反演研究进展
Progress in Soil Moisture Retrieval under Crop Canopy Cover Based on Multi-polarization SAR Data
摘要
Abstract
[Significance]Soil moisture is a critical parameter in surface water cycling and agricultural productivity,playing an essential role in crop growth monitoring,yield estimation,and field management.Synthetic aperture radar(SAR),with its all-weather capabili-ties and multi-polarization advantages,is highly sensitive to the structural,orientational,and moisture characteristics of crops and soil,making it a key remote sensing tool for soil moisture monitoring.However,under crop cover,surface scattering signals are confound-ed by vegetation scattering,and the spatial heterogeneity of crop and soil properties further complicates the scattering process.These factors make it challenging to directly apply traditional methods for agricultural soil moisture retrieval.The separation of scattering contributions from the crop canopy and underlying soil remains a significant research challenge.To address this,the present paper sys-tematically reviews the state-of-the-art advancements in soil moisture retrieval under crop cover across three dimensions:data resourc-es,scattering theory,and retrieval applications.[Progress]This review offers a comprehensive assessment of multi-polarization SAR-based agricultural soil moisture retrieval technology from the viewpoints of data,theory,and application,emphasizing future optimi-zation.In terms of data resources,the paper presents a comprehensive summary of spaceborne multi-polarization SAR data.It com-pares key imaging parameters(e.g.,frequency band,polarization mode,spatial resolution,and incidence angle)and analyzes their im-pacts on agricultural soil moisture retrieval.Research shows that,under single-source data conditions,long-wavelength bands,small incidence angles,and co-polarization modes are less prone to canopy scattering interference.Under multi-modal data condi-tions,integrating multi-band,multi-angle,and multi-polarization SAR data can more effectively distinguish between vegetation and surface scattering contributions.Regarding theoretical and technical progress,the paper tracks the development of scattering mod-els,reviews existing soil and vegetation scattering models,and contrasts the applicability of physical,empirical,and semi-empirical models.It also emphasizes the advantages of coupled modeling approaches.Moreover,the paper examines various solution methods for scattering models,focusing on local and global optimization algorithms.In the application context,this paper evaluates the perfor-mance of multi-polarization SAR in soil moisture retrieval across different crop and soil conditions,using wheat,corn,rapeseed,and soybean as typical crops.It discusses the influence of different crop types(e.g.,differences in leaf and stem structure)and phenologi-cal stages on retrieval accuracy.The paper compares the applicability of soil scattering models and retrieval methods under various soil surface roughness and soil texture conditions(e.g.,sandy and loamy soils)and examines their retrieval accuracy under different soil scenarios.Additionally,it reviews the improvements in retrieval performance through multi-source data fusion,including optical-SAR combinations and active-passive remote sensing fusion.It also synthesizes the main challenges and future directions for multi-source data fusion strategies,especially with regard to scale effects.[Conclusions and Prospects]Based on the reviewed advance-ments,the paper identifies key technical challenges,including discrepancies in monitoring range and scale among spaceborne,air-borne,and ground-based data,difficulties in adapting scattering models to crop morphology,and the lack of standardized validation protocols for retrieval results.Looking ahead,the paper envisions the potential for future technological progress driven by multi-mod-al big data and artificial intelligence.This review highlights critical insights,addresses key bottlenecks,and drives the development of intelligent,adaptive,high-resolution,and high-precision soil moisture retrieval systems in multi-polarization SAR soil moisture re-trieval.关键词
极化合成孔径雷达/农业遥感/土壤湿度反演/作物覆盖场景/定量遥感监测Key words
polarimetric synthetic aperture radar/agricultural remote sensing/soil moisture retrieval/crop coverage scene/quantitative remote sensing monitoring分类
农业科技引用本文复制引用
SUN Rong,ZHANG Jie,BAO Yuhai,GAO Han,JIANG Yujie,LI Qiaochu,WU Haoyu,WU Shangrong,YU Shan,XU Lei,YU Liangliang..多极化合成孔径雷达作物覆盖下土壤湿度反演研究进展[J].智慧农业（中英文）,2025,7(6):75-95,21.基金项目
国家自然科学基金(42301399) （42301399）
山东省自然科学基金(ZR2023QD097,ZR2024MD108) （ZR2023QD097,ZR2024MD108）
中国地质大学(武汉)国家地理信息系统工程技术研究中心开放基金(NERCGIS-202408) （武汉）
内蒙古师范大学自主科研项目(2025JYJFZX001) National Natural Science Foundation of China(42301399) （2025JYJFZX001）
Shandong Provincial Natural Science Foundation(ZR2023QD097,ZR2024MD108) （ZR2023QD097,ZR2024MD108）
Open Fund of National Engineering Research Center of Geographic Information System in China University of Geosciences(NERCGIS-202408) （NERCGIS-202408）
Independent Scientific Research Projects of Inner Mongolia Normal University(2025JYJFZX001) （2025JYJFZX001）
