作物学报 | 综述：基于反射光谱和叶绿素荧光数据的作物病害遥感监测研究进展
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竞霞 邹琴 白宗璠 黄文江
西安科技大学测绘科学与技术学院，陕西西安 710054
中国科学院空天信息创新研究院遥感科学国家重点实验室，北京 100101
摘要 Abstract
受近年来极端天气的影响，作物病害出现来势早、灾情重和大面积爆发等特点[1]，严重影响了作物产量和质量，快速、无损、高精度、大范围的监测和预警是有效防控作物病害的关键[2]。传统的作物病害监测主要由植保专家等通过田间调查的方法判断病害严重度，该方法费时费力，时效性差，且受观测者的主观因素影响较大[3]，难以适应大范围病害实时监测和预报的需求[4]。遥感技术具有快速、大范围和无破坏等显著优点，已被广泛应用于作物长势及病害胁迫监测中[5-6]。
作物受到病菌侵染后，叶片色素及水分含量、光合生理状态等均会发生变化，病害的不同侵染阶段其生理变化强度及其症状显现程度均不相同[7]。在作物受到病害胁迫的早期阶段，主要是通过生理机制的调整使其快速适应外在胁迫的变化，而叶绿素荧光能够灵敏反映作物光合生理上的变化，实现作物病害的早期探测[8-9]。当作物受到持续的病害胁迫后，不但其细胞活性、生化组分等发生变化，叶片形态、叶倾角分布及冠层结构、密度等均会随之改变，进而引起植物叶片、冠层反射光谱发生变化。因此，利用反射率和叶绿素荧光光谱均能实现作物病害的遥感监测。
01
基于反射率光谱的作物病害遥感监测
1.1 基于反射率光谱的作物病害监测方法
受病害胁迫作物生理生化特性及表观形态的改变会引起光谱特征的改变[13]，其光谱响应特性是由病害胁迫导致的植物损伤所引起的色素、水分、形态、结构等变化的函数[14]。作物不同，病害种类及其发展阶段不同，导致了光谱特征的多样性[15]，因此不同病害类型具有不同波段的光谱响应特性，利用光谱响应的敏感波段及异常光谱的变化程度可实现作物病害的识别及发病程度的预测(表1)。目前主要采用过滤法(Filter)、包裹法(Wrapper)和嵌入法(Embedded)三类特征选择算法挑选作物病害遥感探测的敏感因子[16]。Filter算法从数据特征的结构出发，利用光谱特征参量与病情指数之间的相关性作为敏感因子的优选标准，特征参量的选择独立于模型算法[17]，能够快速实现作物病害的诊断，但该方法忽略了各特征参量间的相关性，难以挖掘出特征参量之间的组合效应，影响了模型构建的精度[9]。为提高模型的泛化能力与预测精度[18]，结合特征选择和模型构建方法的Wrapper算法诞生，Wrapper算法需要定义启发策略，复杂性高，在作物病害监测的实现上具有一定的难度[19]。Embedded方法是基于Filter算法和Wrapper算法的折中方案，能通过学习器自身主动选择特征，包括基于惩罚项的特征选择法[20]和基于树模型的特征选择法[21-22]等，具有良好的统计性质，但参数设置需要深厚的背景知识[23]。
作物病害遥感探测精度除与所选特征因子有关外，建模算法也是影响其精度的重要因素。作物病害遥感监测模型主要包括统计模型和人工智能模型(表2)。统计模型能够综合描述两组变量之间典型的相关关系，方法简单且在样本充足的情况下能达到较好的监测精度，但由于数据获取时外界条件的差异，该方法在空间维和时间维上的普适性较差[36]。因此一些学者提出了能够兼顾训练误差和泛化能力的模式识别和机器学习的作物病害监测模型[37]，该方法具有较好的非线性拟合能力，能不断训练样本数据使目标达到最优化[38]，解决了反射系数轻微变化而导致作物病害探测困难的问题[39]，但基于机器学习的作物病害遥感监测需要海量数据样本，且存在着过学习、局部极值点和维数灾难等缺点[40]。
1.2 基于不同尺度的作物病害遥感监测
在叶片及冠层尺度上，作物病害的遥感监测主要基于手持仪器[51]、塔基平台[52-54]等近地平台搭载的光谱仪获取不同发病状态下的作物光谱信息。叶片水平的光谱特征不受土壤和形态等因素影响，适用于作物病害的机理研究，受限于观测范围难以实现大区域的病害监测。1927年Taubenhaus等[55]利用飞机搭载的黑白相机实现了棉花根腐病判定，首次实现了地块尺度上的作物病害识别。随着光谱成像技术的发展，目前地块尺度的作物病害遥感监测主要利用飞行器上搭载的光谱仪及数码相机获取的高光谱影像和多光谱影像等得到不同病情严重度下作物病害信息[56-61]。尽管基于航空的作物病害监测取得了较好的诊断效果，但仪器昂贵限制了其广泛使用[62]，区域尺度的病害监测以卫星影像作为数据源，在观测范围和成本上具有一定优势。利用卫星数据获取作物病害胁迫信息要求使用的卫星传感器具有较高的光谱分辨率以及可区分健康作物和受病害胁迫作物的光谱通道[63]，高分辨率和高精度的卫星遥感影像与其他多源异构信息结合形成时空序列数据集，为区域内作物病害监测、大尺度病害预报和流行趋势提供依据[64-65]。表3对叶片及冠层、地块和区域尺度上的部分研究成果进行了总结和归纳。
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基于叶绿素荧光的作物病害遥感探测
2.1 基于主动叶绿素荧光的作物病害探测
主动叶绿素荧光的探测主要包括叶绿素荧光动力学技术和激光诱导荧光技术2种方法。叶绿素荧光动力学技术多借助(非)调制式叶绿素荧光仪的叶片“点”式接触方式测量叶绿素荧光参数[85]。而激光诱导荧光技术以紫外光作为激发光源，测量单色光激发照明条件下荧光波长的发射荧光[86]。
通过主动方法探测的叶绿素荧光已被广泛应用于作物病害监测以及病害识别和分类等研究中。如Atta等在实验条件下记录了叶绿素荧光光谱随病情严重度的变化，基于同步荧光光谱特征实现了小麦条锈病的早期监测[87]。周丽娜等[88]基于激光诱导的叶绿素荧光实现了稻瘟病发病等级预测；隋媛媛等[89]利用叶绿素荧光光谱指数在显症前完成了黄瓜霜霉病预测。在胁迫的分类上，Belasque等[90]利用激光诱导荧光技术实现了人工损伤、养分胁迫和病害胁迫的准确分类；Wang等[91]利用ΦPSII、Fv/Fm和F550/F510三个指标实现了氮、干旱和灰霉病胁迫的分类。上述研究主要是利用非成像荧光技术进行作物病害的遥感监测，该方法具有成本低、数据量小、处理速度快的优势，然而叶片不同部位的组织结构和叶绿素含量存在差异，导致叶片不同部位的光合作用具有横向异质性，而荧光成像技术能够获取植物的颜色纹理等特征信息和荧光强度信息，揭示受生物或非生物因素胁迫的植物叶片或表面的时空异质性[92]，因此一些研究者利用荧光影像的这种特性实现了染病与健康植物的区分[93-94]、染病作物的早期诊断[95]和作物病害的实时检测[96]等。
基于主动荧光的作物病害遥感监测对于揭示叶片光合状态、解释病害胁迫机理具有重要意义，但该方式测定的叶绿素激发荧光与自然条件光合作用荧光的物理意义差别较大，而且由于使用条件的限制(激光激发或叶片接触式测量等)，难以推广到大范围的遥感应用[97-98]。
2.2 基于日光诱导叶绿素荧光探测作物病害
日光诱导叶绿素荧光(Sun-Induced Chlorophyll Fluorescence, SIF)是植物在太阳光照条件下，由光合中心发射出的光谱信号(650~800 nm)，具有红光(685 nm左右)和近红外(740 nm左右)两个波峰，能直接反映植物实际光合作用的动态变化[99]，实现作物病害的遥感监测。
自然条件下，遥感传感器探测的冠层光谱信号中SIF信号与植被反射光谱信号混叠，且冠层SIF信号很微弱、通常不足入射辐射的2% [100]，因此对于SIF的信息提取具有很大挑战性[101]。随着遥感技术的进步，研究者发现SIF在Fraunhofer暗线处具有填充效应(图2)，这使得SIF的直接遥感探测成为可能。学者们基于此原理提出了标准FLD (Fraunhofer Line Discriminator) [102]、3FLD (3-bands Fraunhofer Line Discriminator) [103]、iFLD (improved Fraunhofer Line Discriminator) [104]、pFLD (PCA-basedFLD)[105]和光谱拟合法(Spectral Fitting Method，SFM)[106]等单波段SIF反演算法和全波段SFM [107]、FSR (Fluorescence Spectral Reconstruction) [108]及F-SFM (Full-spectrum Spectral Fitting Method)[109]等全波段SIF反演算法。
标准FLD算法是在假设Fraunhofer吸收线内外的反射率和透过率相等的基础上，通过建立吸收谷内外的辐亮度光谱方程解求SIF [102]。为了克服标准FLD方法在吸收线内外波段的反射率和荧光值实际上存在差异的局限性[86]，Maier等[103]提出了3FLD的SIF提取算法，该方法假设反射率在很窄的Fraunhofer吸收线内外呈线性变化，通过吸收线内外波段的加权平均值减弱SIF和反射率随波长变化带来的影响。Luis等[104]提出引入2个校正系数表示吸收线内外反射率和荧光关系的iFLD算法，利用三次样条函数插值获得的表观反射率代替真实反射率进行计算，从而消除荧光和反射率对SIF反演算法的影响。Liu等[105]以主成分分析代替插值拟合吸收线处的反射率曲线，以更精确的估算发射率及荧光校正系数。SFM则是假定Fraunhofer吸收线内外的荧光和发射率是变化的，利用二次函数拟合SIF光谱和反射率光谱[106]。
全波段SIF光谱反演算法的精度取决于反射率和SIF光谱的估算精度，Mazzoni等[107]分别利用2个Voigt函数之和以及三次样条函数代替二次函数拟合SIF光谱和反射率光谱，基于SFM实现了675~770 nm波段范围内的SIF光谱反演，并用模拟数据进行了验证。FSR算法利用SFM反演出5条吸收线处的SIF辐照度，通过奇异值分解提取3个具有SIF光谱一般分布特征的基谱，利用加权线性最小二乘和5个反演的SIF值拟合确定基谱系数，重建全波段SIF光谱[108]。F-SFM算法则利用主成分分析提取反射率和SIF的特征波段，根据不同权重的反射率和SIF主成分重建反射率和SIF光谱，并引入迭代过程提高反射率的估算精度[109]。表4归纳了目前常用的单波段和全波度SIF反演方法，为今后研究者选择合适的SIF估测算法提供参考。
SIF数据能够快速、无损的探知植物光合生理及其胁迫状况，被广泛应用于作物病害遥感监测。张永江等[110]基于FLD提取了O2-A (760 nm)和O2-B (688 nm)波段的SIF强度，构建了用于反映作物受胁迫状况的荧光比值指数F688/F760，证实了利用FLD提取的SIF信息可以反映田间小麦条锈病的发病状况。Hernández-Clemente等利用不同分辨率下的SIF监测了受疫霉菌侵染的橡树林，基于提出的FluorFLIGHT模型实现了健康和发病橡树林的分类[111]。Raji等[112]利用O2-A和O2-B波段SIF数据构建了荧光比值F687/F760，实现了木薯花叶病的早期探测。赵叶等[8]对比分析了反射率光谱和SIF数据对小麦条锈病不同发病状态下的敏感性，发现当病情指数低于20%，SIF数据对小麦条锈病害胁迫响应更为敏感，冠层SIF数据比反射率光谱数据更适于作物病害的早期探测[9]。但叶绿素荧光光谱范围有限，无法探测到光谱响应位置不在此范围内的病害类型，且监测精度受SIF提取精度的影响。
03
SIF与反射率光谱协同的作物病害遥感探测
陈思媛[5]、竞霞等[21]研究结果表明，在反射率光谱指数及一阶微分光谱指数中加入冠层SIF数据能够改善小麦条锈病的遥感监测精度，然而利用少量波段信息计算反射率光谱指数或一阶微分光谱指数在一定程度上丢失了对作物病害遥感探测的有用信息。基于此，白宗璠等利用改进离散粒子群算法从全波段光谱数据中优选遥感探测小麦条锈病严重度的特征变量，协同冠层SIF数据分别利用随机森林和后向传播神经网络构建小麦条锈病遥感探测模型，改善了模型的收敛速度和寻优精度并提高了小麦条锈病遥感探测精度[120]。上述研究是将SIF和反射率光谱特征直接拼接形成高维特征向量，没有考虑不同特征向量与作物病情严重度之间的最优映射关系，利用单一函数映射所有特征构建作物病害遥感监测模型，不仅难以充分挖掘特征中包含的信息，还会增加分类器训练和预测时的计算代价。高媛等基于核函数的特征融合法将SIF和反射率光谱特征用不同的核函数进行映射，通过多核学习算法构建了反射率与SIF协同的小麦条锈病遥感监测模型。结果表明，对SIF和反射光谱特征分别利用其最优核进行映射构建的小麦条锈病严重度估测精度优于直接拼接法[20]。叶绿素荧光的发射和NPQ能量耗散都是植物碳固定机制中的重要组成部分[78,121]，都能够敏感反映植物受胁状况及其光合性能，因此一些专家综合利用反射率光谱、SIF和热红外信息进行作物病害的遥感监测。Poblete等[122]基于高光谱影像和热影像提取的SIF和作物水分胁迫指数(CWSI)，实现了健康橄榄树和受木糖杆菌胁迫橄榄树的分类。Calderón等[123]利用连续3年的机载热、多光谱和高光谱影像提取出SIF、温度信息和窄带光谱指数实现了橄榄黄萎病的早期监测。
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讨论与展望
(1)全波段SIF光谱的作物病害遥感监测精度及稳定性有待提高。全波段SIF光谱(650~850 nm)在红光区(685~690 nm附近)和远红光区(730~740 nm附近)存在2个荧光峰值，不仅包含病害胁迫下的SIF强度信息，还能提供形状信息，与植被生理状态存在显著相关关系[78]，更适用于作物病害的识别与监测。然而遥感传感器探测到的SIF信号微弱且与反射率信号混叠，如何提高全波段SIF信息的提取精度，是利用全波段SIF光谱进行作物病害遥感监测面临的重要挑战之一。
(2)群体生物量影响了作物病害的SIF探测精度。作物在受病菌侵染初期即能通过调整光合速率的方式启动光保护机制，以发射叶绿素荧光消耗过剩的光能等生理机制对病害作出快速响应[11,62]，实现作物病害的早期探测。然而冠层SIF一方面随能量耗散途径的生理调节而改变，另一方面也受到植物色素组成、叶面积、叶倾角等生化物理参数的影响，如何消除群体生物量对冠层SIF的影响，是基于SIF数据进行作物病害早期探测需要解决的关键问题。
(3)病害微观特性和宏观遥感监测的结合不足。病菌生长、繁殖和侵染过程会消耗寄主养分、破坏其正常的生理过程，如小麦条锈病夏孢子突破表皮破坏了大量的叶绿素，从而使各个生育期的叶绿素含量降低，导致了小麦叶片褪绿、发黄等症状[13]，这些变化在反射率和荧光光谱曲线上均有体现。研究不同病害胁迫下叶片的色素含量、细胞水含量、细胞间隙比等微观特性以及叶面积指数群体参数与SIF和反射率光谱的作用机制，在病害光谱响应特性分析的基础上，建立SIF和反射率光谱随病情发展的动态响应规律和关键参数的估算模型，对作物病害的遥感探测和科学防治具有重要意义。
(4)作物病害逆向遥感识别问题没有很好地解决。目前作物病害遥感探测主要侧重于研究病害胁迫下反射率光谱和叶绿素荧光数据的响应特性，并利用实验数据中探测到的光谱差异分析作物是否受到病害胁迫及其病情严重度，而极少有研究涉及作物病害类型的遥感识别问题，即作物病害遥感逆向识别与诊断问题尚未得到很好地解决。农作物病害的逆向遥感识别是实现大范围航空航天遥感监测的关键，是利用遥感影像监测农作物病害不可回避的问题。建立基于大尺度范围的作物病害逆向遥感识别技术方法和体系，构建具有较强机理解释和一定普适性的作物病害诊断模型，还有待进一步研究。
(5)不同尺度作物病害遥感探测之间没能很好地结合。近地高光谱作物病害遥感监测具有航空航天遥感监测难以比拟的方便性、灵活性、经济性等优势，而且受外界因素的影响较小，能获得相对比较理想的监测结果，但在空间上具有一定随机性，只有结合航空航天遥感影像数据才能反映病害发生发展的空间特征，从真正意义上实现作物病害的遥感监测[124]。利用航空航天遥感影像监测农作物病害时，由于传感器接收到的信号是地面分辨率范围内像元目标物的总和，受下垫面状况、植株的形态结构、天气状况、栽培措施等因子的影响，因此研究不同尺度作物病害遥感探测之间的关系，对提高作物病害的探测精度，实现大范围作物病害的遥感监测具有重要意义。
(6)作物病害遥感探测模型对植被病理机制和定量遥感机理结合不够充分。将遥感探测机理与植被病理机制相结合，构建具有一定生理机制的作物病害遥感探测模型对提高模型的实用性具有重要意义。已有研究主要侧重于分析病害胁迫下反射率或荧光数据的响应特性，忽略了病害发生的生理机制及其遥感探测机理。结合病害生理机制的遥感探测模型较单纯基于光谱响应特性构建的模型更能提升对复杂农田环境的适应能力，提高作物病害遥感监测精度和模型的普适性。
05
总结
随着农业信息化的不断深入，利用遥感技术监测作物病害逐步从理论走向应用，并且在弥补传统病害监测时效性差和人力损耗大等缺陷上显示出极大潜力。论文总结了利用反射率光谱数据进行作物病害遥感探测中常用的特征优选和模型构建方法，概括了主动荧光、被动荧光以及协同SIF和反射率光谱在作物病害遥感监测中的研究进展，分析了反射率数据和叶绿素荧光数据在作物病害遥感探测中的优势和局限性，探讨了目前研究中可能存在的问题及未来的发展方向。尽管目前遥感监测技术与实际生产管理应用存在较大差距，但在充分考虑病害生理机制和定量遥感机理的基础上，结合生境条件、农学背景深入挖掘多时相遥感数据所包含的病害信息，可为现代农业大面积精准管理和植保提供实时动态监测信息，使得作物病害遥感监测方法和技术在应用中不断走向成熟。
本研究由国家自然科学基金项目(41601467, 52079103)资助。
通信作者:黄文江, E-mail: huangwj@aircas.ac.cn
第一作者联系方式: E-mail: jingxiaxust@163.com
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