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

doi:10.12133/j.smartag.SA202509009

Abstract

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

CLC Number:

S127

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.

Figures/Tables 12

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Table 3

Comparison of the applicable roughness range and soil moisture retrieval accuracy of different soil scattering models

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

Table 3

Fig. 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