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摘要:
雷达遥感具有全天时、全天候监测的能力，对植被具有一定的穿透能力，对植被散射体形状、结构、介电常数敏感；这些特性使得其在农业应用中极具潜力。该文首先介绍了雷达遥感在农业中的应用领域，概略总结了目前在农作物识别与分类、农田土壤水分反演、农作物长势监测等多个领域研究的综述文献；然后分别阐述了雷达散射计和各类SAR特征(包括：SAR后向散射特征、极化特征、干涉特征、层析特征)在农业各领域中应用的现状和取得的研究成果，最后结合农业应用需求和SAR技术发展总结了目前研究中存在的问题和原因，并对未来的发展进行了展望。
Abstract:Active radar remote sensing technology, with its capability of acquiring all-weather data, has great potential for agricultural monitoring. This technology can penetrate vegetation cover more deeply than optical sensors and has sensitivity to the shapes, structures, and dielectric constants of vegetation scatterers. In this paper, we discuss the applications of radar remote sensing in crop identification, cropland soil moisture inversion, crop growth parameter inversion, crop phenology retrieval, agricultural disaster monitoring, and crop yield estimation. We review several specific papers focusing these fields, and then describe the results obtained using information extracted from radar scatterometers and Synthetic Aperture Radar (SAR). Extracted SAR data include characterizations of backscattering, polarimetry, interferometry, and tomography. Lastly, we summarize the problems faced by radar applications in agriculture and consider the future trend of these applications.
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表 1 地基雷达散射计研究现状总结
Table 1. Summary of studies using ground-based scatterometers
研究团队 散射计相关参数描述 应用类型(对象) 研究结论 参考
文献名称 参数描述 堪萨斯大学Ulaby
等团队MAPS 双极化(HH+VV)；入射角可在0°～70°
之间变化，频率4～
8 GHz土壤水分 后向散射对于土壤水分的敏感性：HH>VV；后向散射对土壤水分的敏感性受到土壤表面粗糙度的影响明显，土壤表面的粗糙度可以通过频率和入射角的变化来表征，因此土壤水分反演受到频率和入射角的影响明显；当频率在4～8 GHz，入射角在5°～15°时，HH极化的后向散射几乎不受地表植被的影响，仅反映土壤水分的变化。 [16-18] 全极化(HH+VV+HV+VH)；入射角可在0°～80°之间变化，频率4～8GHz 农作物分类制图(农作物包括：
玉米、高粱、大豆和苜蓿)极化特征对农作物结构变化敏感；农田的垄向对极化散射特征影响明显，其影响具有农作物类型依赖性；对于农作物结构变化的敏感性：VV>HH；农作物密度和入射角变化均会影响不同频率微波的后向散射强度；大入射角(30°～65°)和高频波段组合可以最有效的区分不同农作物类型。 [18] MAS 双极化(HH+VV)；入射角可在7°～15°之间变化，频率2～
8 GHz裸土覆盖区土壤水分 土壤粗糙度会影响裸土覆盖区土壤水分的反演；通过优化散射计的系统参数可以降低土壤粗糙度的影响，推荐的组合是频率为4 GHz，入射角在7°～15°，极化方式为HH或VV。该参数在频率4～8 GHz之间的植被覆盖区的土壤水分反演中也适用，后向散射与土壤水分的最高相关性获得时频率为4.7 GHz，入射角为10°。 [18,20] 三极化(HH+VV+HV)；入射角可在0°～80°之间变化，频率1～8 GHz 土壤水分、地表粗糙度、土壤结构 对于裸土覆盖区的土壤水分，结论与文献[20]相似，地表粗糙度的影响在频率为5 GHz，入射角在7°～17°时影响最小；在有农作物覆盖区的土壤水分反演中，后向散射与土壤水分的相关性在频率4.25 GHz，入射角为10°，极化为HH时最高，r=0.92；后向散射系数对土壤水分的估测力依赖于土壤水分在田间含水量中所占的比例，当其比例低于50%时，估测力低，在50%～150%之间时，估测力高。 [21-23] 双极化(HH+VV)；入射角可在0°～70°之间变化，频率8～
18 GHz土壤水分和农作物识别(玉米、高粱、大豆和苜蓿) 除了与文献[18]相似的结论，还得出：采用VV的多频数据可以获得最好的农作物识别效果；入射角在30°～65°时可以将土壤水分在农作物识别中的影响降低到最小；低频小入射角数据可以获得更好的土壤水分反演结果。 [19] 荷兰ROVE
项目FM/CW X-(10 GHz)、Q(35 GHz)；角度15°～80°，极化：VV, HH, VH, HV 农作物观测、土壤水分 农作物的后向散射系数受到极化方式、观测角度等影响明显；这种成像几何的影响具有农作物类型依赖性：例如入射角变化对甜菜影响不明显，但是对马铃薯的影响可达到–5 dB；此外当地表农作物冠层的覆盖率达到80%时，后向散射系数变化呈现饱和；X-波段可用于农作物的分类识别；多频数据联合观测有助于提高农作物冠层生物量、冠层含水量、覆盖度和农作物高度估测的精度；增大观测入射角可以提高冠层含水量的估测精度。 [24-28] 加拿大CCRS相关项目 FM/CW L-, C-, Ku(1.5, 5.2, 12.8 GHz)；全极化；角度0°～85° 农作物识别与分类、土壤水分、农作物冠层水分、农作物残余 通过方差系数分析得出Ku-波段、HV极化、入射角范围在30°～60°，农作物生长29～30周时，可以得到最优的农作物识别效果；在农作物快速生长阶段，后向散射与每日冠层含水量变化相关性较高，农作物凋谢时，后向散射与每日土壤水分变化相关性较高，相关性同时受到频率的影响；HV对农田农作物残余变化敏感，并且不易受到观测方向或垄向的影响。 [29-33] 中国 地基微波散射计(FM /CW) C-；HH和VV 土壤水分土壤粗糙度 垄向使得与其平行的极化方式的后向散射系数增强；反演测得的粗糙度不同于光学方法得到的粗糙度。 [34,35] 微波散射计
(FM /CW)X-(9.375 GHz)，角度为0°～48°，步进间隔为6°；全极化 土壤水分 X-波段HH极化在6°时对裸土含水量灵敏度最高，有植被覆盖的土壤水分反演中，X-波段比C-波段差；含水量一定时，后向散射系数随入射角增大而减小，变化率随粗糙度增加而减小；随着频率的增加，与粗糙度无关的入射角增大，频率为1.1 GHz时，入射角为7°，7.5 GHz时为10°。 [34,36,37] 其他 ComRAD 双极化，1.4 GHz辐射计；全极化
1.25 GHz农作物含水量(VWC) 在L-波段采用HH、VV、极化差系数(MPDI)、雷达植被指数(RVI)进行VWC反演中，HV效果最好。 [38-40] UF-LARS L-(1.25 GHz)，全极化，入射角40° 土壤水分，农作物长势 采样时间间隔降低可以显著提高反演的土壤水分的精度，VV极化后向散射对农作物的垂直结构变化更敏感；在植被体散射为主导机制的土壤水分反演中，表面较光滑、土壤较干燥时，线性关系反演结果不确定较大。 [41,42] 
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表 2 星载散射计信息
Table 2. Major space-borne radar scatterometry and their basic information
卫星 传感器 波段 入射角 极化 服役时间 国家 Seasat SASS Ku 25°～55° HH, VV 1978-6—1978-10 美国 ERS-1 AMI C 18°～59° VV 1991-6—2000-3 欧空局 ERS-2 AMI C 18°～59° VV 1995-4— 欧空局 ADEOS-1 NSCAT Ku 18°～63° HH, VV 1996-8—1997-6 美国 QuickSCAT SeaWinds Ku 46°, 54° HH, VV 1999-7— 美国 ADEOS-2 NSCAT Ku 46°, 54° HH, VV 2002-12—2003-8 美国 SZ-4 CN/SCAT Ku 37° HH, VV 2002-12— 中国 MetOp-1 ASCAT C 25°～65° VV 2006-10— 欧空局 OceanSat-1 OSCAT-1 Ku 50°, 57° HH, VV 2009— 印度 HY-2A HY-2A Ku HH, VV 2010-8— 中国 OceanSat-2 OSCAT-2 Ku 50°, 57° HH, VV 2016— 印度 SMAP L- 2015-1—2015-7 美国
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表 3 极化特征在农业中的应用现状
Table 3. Summary of studies using polarimetric characterization
应用类型 SAR参数描述 结果 参考文献 农作物分类与识别 Pauli分解参数，Stokes参数，
基于特征值、特征向量分解参数，
Freeman-Durden, Yamaguchi分解参数，Span-Pauli分解参数, $H {\text{-}} \overline A {\text{-}} \alpha$分解参数，Cloude分解参数(1)加入极化特征，可以有效提高分类精度；
(2)对于不同农作物的可区分性差异明显；
(3)在极化特征中加入时相特征可以有效提高农作物分类精度；
(4)加入极化分解特征比仅采用简单的线性极化组合的分类精度高；
(5)简缩极化特征的分类结果几乎可以达到全极化特征分类的精度水平。[3,65,66] 农田参数反演(土壤水分/地表粗糙度) (1)引入去极化率、同极化相关系数、相干性参数、散射熵和散射角等参数分析土壤水分和后向散射系数的变化关系；(2)采用极化分解的参数，主要包括Freeman-Durden和特征值分解的参数。 (1)利用多极化特征可降低采用单极化特征反演土壤水分中的不确定性，提高反演精度；
(2)利用极化分解的参数替代后向散射系数可以提高反演精度；
(3)引入极化参数后，反演结果受到农作物物候期和农作物类型的影响。[3,67] 农作物长势参数反演 极化合成和极化分解参数；
基于极化合成及分解参数发展的参数：如各种雷达植被指数、基准高度参数等。(1)生长参数包括LAI、生物量和农作物高度；
(2)X-、C-波段对LAI变化敏感，
(3)反演结果受到农作物物候期和农作物类型的影响；
(4)多种极化合成及分解的参数可以获得更高的农作物生长参数反演精度(目前已经用于农作物长势参数反演的参数约为30个)。[80-83] 农作物物候期划分 Cloude-Pottier分解参数、极化比、极化差值比、极化合成参数(极化度)、简缩极化后向散射系数及极化分解参数、Stokes参数 (1)主要采用时间序列数据进行物候期的划分或监测；
(2)方法包括利用分类和时相动态跟踪两类方法；
(3)用于监测的数据包括X-和C-波段。[9,84-88] 农作物灾害监测 极化指数(HH/VV, HH/HV, 表面散射/Span, 二次散射/Span) (1)不同极化特征对倒伏现象响应差异明显；
(2)极化熵、极化指数均可以反映倒伏现象；
(3)倒伏发生伴随着散射机制的明显变化，因此可以通过极化特征表征。[89,90]
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表 1 Summary of studies using ground-based radar scatterometers
Research
teamDescription of scatterometer
parametersType (object) Conclusion Reference Parameter Description Ulaby Team, University of Kansas MAPS Dual polarization (HH+VV); the inci-dence angle varies from 0° to 70°; the frequency ranges from 4 to
8 GHzSoil moisture The sensitivity of backscatter to soil moisture: HH > VV; the sensitivity of backscatter to soil water is significantly affected by the soil surface roughness, which can be characterized by the change in frequency and incidence angle. Therefore, soil moisture inversion is obviously affected by frequency and incidence angle. When the frequency ranges from 4 to 8 GHz, the sensitivity of backscatter to soil water is significantly affected by the frequency and incidence angle. For instance, when the incidence angle varies between 5° and 15°, the backscattering of HH polarization is hardly affected by the vegetation and only reflects the change of soil moisture. [16-18] Full polarization (HH+VV+HV+VH); the incidence angle varies from 0° to 80°; the frequency is bet-ween 4 and 8 GHz Classification and mapping of crops (crops include corn, sorghum, soyb-ean and alfalfa) The polarization characteristics were sensitive to the change in crop structure; the ridge direction of farmland has an obvious influence on the polarization scattering characteristics, which is dependent on the type of crops; the sensitivity of the changes to crop structure: VV > HH; the changes in crop density and incidence angle affect the backscattering intensity of microwave at different frequencies; the combination of large incidence angle (30° to 65°) and high-frequency band would be the most effective way to distinguish different crop types. [18] MAS Bipolarization (HH+VV); the incidence angle varies from 7° to 15°; the frequency is between 2 and 8 GHz Soil moisture in bare soil-cove-red area Soil roughness affects the inversion of soil moisture in bare soil coverage area; the influence of soil roughness can be reduced by optimizing the system parameters of scatterometers. The recommended combination is a frequency of 4 GHz, an incidence angle that varies from 7° to 15°, and a polarization mode of HH or VV. This parameter is also applicable to soil moisture retrieval in vegetation coverage areas with frequencies ranging from
4 to 8 GHz. The highest correlation between backscatter and soil moisture is obtained at a frequency of 4.7 GHz and an incidence angle of 10°.[18,20] Tri-polarization (HH+VV+HV); the incid-ence angle varies from 0° to 80°; the frequency is between 2 and 8 GHz Soil moisture, surface roughn-ess, and soil st-ructure For the soil moisture in the bare soil-covered area, the conclusion was similar to Ref. [20], and the surface roughness effects are the lowest when the frequency is 5 GHz and the incidence angle ranges from 7° to 17°. The best soil moisture inversion with crop coverage was obtained at a frequency of 4.25 GHz, incidence angle of 10°, and polarization of HH, with r = 0.92; The estimation capability of soil moisture based on backscattering coefficient depends on the proportion of soil moisture to the field water contents. When the proportion is less than 50%, the estimation is not good, and when the proportion is between 50%–100%, the estimation performance is better. [21–23] Bipolarization (HH+VV); the incidence angle varies from 0° to 70°; the frequency is between 8 and 18 GHz Soil moisture and crop identi-fication (corn, sorghum, soyb-ean and alfalfa) In addition to the similar conclusion with Ref. [18], the best crop identification can be obtained by using the multifrequency data of VV; the influence of soil moisture on crop identification can be minimized when the incidence angle is between 30° and 65°, and better soil moisture inversion results can be obtained with the data combination of low frequency and small incidence angle. [19] ROVE, the Ne-therlands FM/CW X-(10 GHz); Q-band (35 GHz); the incidence angle varies from 15° to 80°; polarization: VV, HH, VH, HV Crop observa-tion; Soil mois-ture The backscattering coefficient of crops is obviously affected by polarization mode and observation angle. The influence of imaging geometry depends on crop types. For example, the influence of incidence angle on sugar beet is not obvious, but the impact on potato reached –5 dB. In addition, when the coverage rate of crop canopy reached 80%, the variation of backscattering coefficient was saturated. X-band can be used for agriculture crop classification and identification. Multifrequency data joint observation can improve the accuracy of crop canopy biomass, canopy water content, coverage, and crop height estimation. Increasing the observation incidence angle can improve the estimation accuracy of canopy water content. [24-28] CCRS,Canada FM/CW L-/C-/Ku (1.5/5.2/12.8 GHz); full polarization; incid-ence angle: 0°–85° Crop identific-ation and class-ification; soil moisture, crop canopy mois-ture, crop residue Through the analysis of variance coefficient, we can obtain the best crop recognition with Ku-band, HV polarization, incidence angle range of 30°–60°, and crop growth during 29–30 weeks. A high correlation exists between backscatter and daily canopy water content when the crops grow in the rapid growth stage. The correlation between backscatter and daily soil moisture change is high when the crops wither. HV is sensitive to crop residual change and is not affected by observation direction or ridge direction. [29-33] China Ground based microwave scatterometer (FM/CW) C-; HH and VV Soil moisture Soil roughness The ridge direction enhances the backscattering coefficient with the polarization channel parallel to it, and the measured roughness by SAR is different from that obtained by optical data. [34,35] Microwave scatterometer (FM/CW) X-(9.375 GHz), Incidence angle: 0°–48°; step in-terval: 6°; full polarization Soil moisture X-band with HH polarization has the highest sensitivity to bare soil water content at an incidence angle of 6°. X-band is worse than C-band for vegetation coverage soil moisture retrieval. When the water content is constant, the backscattering coefficient decreases with the increase in the incidence angle, and the change rate decreases with the increase in roughness. With the increase in frequency, the incidence angle independent of roughness increases. For example, when the frequency is 1.1 GHz, the incidence angle is 7° and 10° at 7.5 GHz. [34,36,37] Others ComRAD Bipolarization, 1.4 GHz radiometer; full polarization (1.25 GHz) Vegetation Water Content (VWC) L-band and HV showed the best performance in VWC retrieval when HH, VV, MPDI, and RVI are used to retrieve VWC. [38-40] UF-LARS L-(1.25 GHz), full polarization; inci-dence angle: 40° Soil moisture, Crop growth The accuracy of soil moisture retrieval can be improved by decreasing the sampling time interval, and VV polarization backscattering is more sensitive to the vertical structure of crops. The linear relationship inversion results are uncertain in the inversion of soil moisture where the scattering mechanism is dominated by vegetation scattering, while the surface is smooth and the soil is dry. [41-42]
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表 2 Major space-borne radar scatterometers and their basic information
Satellite Sensor Band Incidence angle Polarization Service time Nation Seasat SASS Ku 25°–55° HH, VV 1978.6–1978.10 US ERS-1 AMI C 18°–59° VV 1991.6–2000.3 ESA ERS-2 AMI C 18°–59° VV 1995.4– ESA ADEOS-1 NSCAT Ku 18°–63° HH, VV 1996.8–1997.6 US QuickSCAT SeaWinds Ku 46°, 54° HH, VV 1999.7– US ADEOS-2 NSCAT Ku 46°, 54° HH, VV 2002.12–2003.8 US SZ-4 CN/SCAT Ku 37° HH, VV 2002.12– China MetOp-1 ASCAT C 25°–65° VV 2006.10– ESA OceanSat-1 OSCAT-1 Ku 50°, 57° HH, VV 2009– India HY-2A HY-2A Ku HH, VV 2010.8– China OceanSat-2 OSCAT-2 Ku 50°, 57° HH, VV 2016– India SMAP L- 2015.1–2015.7 US
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表 3 Summary of studies using polarimetric characterization
Application Description of SAR parameters Result Reference Crop classifica-tion and identifica-tion Pauli decomposition parameter; Stokes vector feature; Decomposition param-eter based on eigenvalueand eigenve-ctor; Freeman-Durden, Yamaguchi decomposition parameter; Span-Pauli decomposition parameter, $H {\text{-}} \overline A {\text{-}} \alpha $ de-composition parameter; Cloude decom-position parameter 1. Adding polarimetric features can effectively improve the classification accuracy.
2. For different crops, the difference is obvious.
3. The accuracy of crop classification can be effectively improved by adding temporal features of polarimetric features.
4. The classification accuracy of adding polarimetric decomposition feature is higher than that when only simple linear polarimetric combination is used.
5. The classification results using compact polarimetric features can achieve the same accuracy as the use of full polarimetric features.[3,65,66] Inversion of farm-land parameters (soil moisture/gro-und roughness) 1. Parameters such as depolarization ratio, co-polarimetric correlation coeff-icient, coherence parameter, scattering entropy, and scattering angle were introduced to analyze the relationship between soil moisture and backsca-ttering coefficient; 2. The parameters of polarimetric decomposition mainly include Freeman-Durden and eigen-value decomposition 1. Using the multipolarimetric feature can better reduce the uncertainty of soil moisture inversion than using the single polarimetric feature and improve the inversion accuracy.
2. The inversion accuracy can be improved by using the parameters of polarimetric decomposition instead of backscattering coefficient.
3. With the introduction of polarimetric parameters, the inversion results are affected by crop phenology and crop types.[3,67] Inversion of crop growth parame-ters Polarimetric synthesis and polarimetric decomposition parameters; parameters were developed based on polarimetric synthesis and decomposition param-eters, such as radar vegetation index and reference height parameters. 1. Growth parameters include LAI, biomass, and crop height.
2. X- and C-bands are sensitive to the change in LAI.
3. Inversion results are affected by crop phenological period and crop type.
4. More accurate inversion of crop growth parameters can be obtained by using multipolarization synthesis and decomposition parameters (about 30 parameters have been used for crop growth parameters inversion at present)[80-83] Division of crop phenological period Cloude-Pottier decomposition param-eters, polarimetric ratio, polarimetric difference ratio, polarimetric synthesis parameter (polarization degree), CP backscattering coefficient, and polari-metric decomposition parameter, Stokes parameter 1. Time series data are mainly used to retrieve or monitor the phenological period.
2. The methods include classification and dynamic tracking.
3. The data used for monitoring include X - and C-band.[9,84-88] Crop disaster monitoring Polarimetric index (HH/VV, HH/HV surface scattering, Span, double scattering/Span) 1. Different polarizations have an obvious effect on lodging.
2. Both polarimetric entropy and polarimetric index can reflect the lodging.
3. The occurrence of lodging is accompanied by obvious changes in polarimetric scattering mechanism, so it can be characterized by polarimetric characterization.[89,90]
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