多极化合成孔径雷达作物覆盖下土壤湿度反演研究进展

doi:10.12133/j.smartag.SA202509009

Smart Agriculture ›› 2025, Vol. 7 ›› Issue (6): 75-95.doi: 10.12133/j.smartag.SA202509009

• 专刊--遥感+AI 赋能农业农村现代化 • 上一篇下一篇

多极化合成孔径雷达作物覆盖下土壤湿度反演研究进展

孙荣¹, 高晗¹(), 姜钰杰¹, 李翘楚¹, 吴昊宇¹, 吴尚蓉², 玉山³, 许磊⁴, 于亮亮⁵, 张杰¹, 包玉海³

^1. 中国石油大学（华东）海洋与空间信息学院，山东青岛 266580，中国
^2. 中国农业科学院农业资源与农业区划研究所，北京 100081，中国
^3. 内蒙古师范大学地理科学学院，内蒙古呼和浩特 010022，中国
^4. 中国地质大学（武汉）国家地理信息系统工程技术研究中心，湖北武汉 430074，中国
^5. 内蒙古巴彦淖尔市气象局，内蒙古巴彦淖尔 015000，中国

收稿日期:2025-09-06 出版日期:2025-11-30
基金项目:
国家自然科学基金(42301399,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); 内蒙古师范大学自主科研项目(2025JYJFZX001)
作者简介:
孙荣，硕士研究生，研究方向为极化SAR农田土壤湿度反演。E-mail： Z24160035@s.upc.edu.cn
通信作者:
高晗，博士，副教授，研究方向为农业遥感与极化SAR作物遥感监测研究。E-mail： gaohangeo@upc.edu.cn

Progress in Soil Moisture Retrieval under Crop Canopy Cover Based on Multi-polarization SAR Data

SUN Rong¹, GAO Han¹(), JIANG Yujie¹, LI Qiaochu¹, WU Haoyu¹, WU Shangrong², YU Shan³, XU Lei⁴, YU Liangliang⁵, ZHANG Jie¹, BAO Yuhai³

^1. College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
^2. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
^3. College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China
^4. National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China
^5. Bayannur Meteorological Bureau, Bayannur 015000, China

Received:2025-09-06 Online:2025-11-30
Foundation items:National Natural Science Foundation of China(42301399,ZR2023QD097,ZR2024MD108); Open Fund of National Engineering Research Center of Geographic Information System in China University of Geosciences(NERCGIS-202408); Independent Scientific Research Projects of Inner Mongolia Normal University(2025JYJFZX001)
About author:
SUN Rong, E-mail: Z24160035@s.upc.edu.cn
Corresponding author:
GAO Han, E-mail: gaohangeo@upc.edu.cn

摘要/Abstract

摘要：

［目的/意义］ 土壤湿度是地表水循环和农业生产的关键参数，直接影响作物光合作用、呼吸作用和碳循环，是反映作物健康状态的重要指标，对作物生长监测、产量预估和田间管理具有重要意义。合成孔径雷达（Synthetic Aperture Radar, SAR）技术凭借其全天时、全天候观测能力，以及多极化通道对地表水分的敏感性，已成为农田土壤湿度监测的重要手段。其中，在多极化SAR农田土壤湿度反演中，如何厘清作物冠层散射和土壤散射作用是研究的核心。 ［进展］ 系统梳理了多极化SAR技术在作物覆盖场景下土壤湿度反演的研究进展，分别从数据资源、技术理论、结果应用3个方面进行总结，重点统计并分析了主要星载极化SAR平台发展与成像参数对土壤湿度反演的影响，总结了散射模型从裸土散射模型到作物-裸土耦合散射模型的发展，讨论了模型解算策略，阐述了作物类型和物候、土壤表面粗糙度和土壤质地参数及多源数据融合对土壤湿度反演的影响。 ［结论/展望］ 总结了天空地监测数据的范围和尺度难匹配、散射模型难以精细适配作物形态、反演结果缺乏统一检验标准和交叉验证等技术瓶颈，展望了在多模态大数据、人工智能技术等新技术支持下的未来技术发展。本研究系统总结了多极化SAR农田土壤湿度反演在数据、理论和应用中的主要进展，凝练技术要点、聚焦技术瓶颈，有助于推动农田土壤湿度反演实现自适应、高分辨、高精度的智能化发展。

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

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 capabilities 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 confounded 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 systematically reviews the state-of-the-art advancements in soil moisture retrieval under crop cover across three dimensions: data resources, 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 optimization. In terms of data resources, the paper presents a comprehensive summary of spaceborne multi - polarization SAR data. It compares key imaging parameters (e.g., frequency band, polarization mode, spatial resolution, and incidence angle) and analyzes their impacts 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 conditions, 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 models, 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 performance 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 phenological 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 advancements, the paper identifies key technical challenges, including discrepancies in monitoring range and scale among spaceborne, airborne, 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-modal 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 retrieval.

Key words: polarimetric synthetic aperture radar, agricultural remote sensing, soil moisture retrieval, crop coverage scene, quantitative remote sensing monitoring

中图分类号:

S127

孙荣, 高晗, 姜钰杰, 李翘楚, 吴昊宇, 吴尚蓉, 玉山, 许磊, 于亮亮, 张杰, 包玉海. 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.

SUN Rong, GAO Han, JIANG Yujie, LI Qiaochu, WU Haoyu, WU Shangrong, YU Shan, XU Lei, YU Liangliang, ZHANG Jie, BAO Yuhai. Progress in Soil Moisture Retrieval under Crop Canopy Cover Based on Multi-polarization SAR Data[J]. Smart Agriculture, 2025, 7(6): 75-95.

图/表 12

图1

图2

表1

图3

图4

表2

图5

图6

图7

图8

表3

不同土壤散射模型的土壤表面粗糙度适用范围对比及土壤湿度反演精度对比

模型名称	土壤表面粗糙度适用范围	应用场景与RMSE对比	总体RMSE范围
物理光学模型	$0.05 λ < s < 0.15 λ, l > λ, m < 25 %$ ^［111］	/	/
几何光学模型	$s > λ / 3, l > λ, 0.4 < m < 0.7$ ^［112］	/	/
小扰动模型	$k s < 0.3, k l < 3, m < 0.3$ ^［113］	/	/
积分方程模型	$k s < 3.0,0.05 < m < 0.5$ ^［66］	青海省，乌图美仁草原：0.050 2~0.094 0^［3］青海省青海湖流域，草原：0.040 5~0.046 5^［3］美国亚利桑那州，稀疏草丛：0.050~0.086^［83］河北邯郸，玉米、小麦：0.047~0.079^［114］	0.040 5~0.086 0
Oh模型	$0.1 < k s < 6.0,2.6 < k l < 19.7$ $9 % < M v < 31 %$ ^［64］	青藏高原北麓河流域，高寒草甸：0.039^［115］美国亚利桑那州，稀疏草丛：0.099~0.160^［83］	0.039~0.160
Dubois模型	$k s < 2.5, M v < 35 %$ ^［65］	青海省，乌图美仁草原：0.057 3~0.116 9^［3］四川省，若尔盖草原：0.035 0~0.065 5^［3］加拿大中西部，小麦、大豆、玉米、油菜：0.032 0~0.082 1^［4］	0.032 0~0.116 9
Shi模型	$0.2 < s < 3.6,2.5 < l < 35$ $2 % < M v < 50 %$ ^［67］	斯洛文尼亚，裸土区：<0.15^［116］美国小沃希托河流域，裸土区：0.034^［67］	0.034~0.150

表3

图9

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卫星系统	国家/机构	极化方式	波段	入射角/（°）
高分三号-1/2/3	中国CAST	单/双/全极化	C	10~60
Sentinel-1-A/C	欧空局ESA	单/双极化	C	19~47
Radarsat-2	加拿大CSA	单/双/全极化	C	20~60
ALOS-PALSAR	日本JAXA	单/双/全极化	L	8~60
ALOS-PALSAR2	日本JAXA	单/双/全极化	L	8~70
陆探一号-A/B	中国CAST	单/双/全极化	L	10~60
TerraSAR-X	德国DLR	单/双/全极化	X	20~55
TanDEM-X	德国DLR	单/双/全极化	X	20~55
PAZ	西班牙	单/双极化	X	15~60
COSMO-SkyMed	意大利	单/双/全极化	X	20~60
NISAR	美国/印度	双/全极化	S+L	20~45
SAOCOM-1A/B	阿根廷	单/双/全极化	L	20~50
BIOMASS	欧空局ESA	单/双/全极化	P	23~35

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

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