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

doi:10.3724/SP.J.1006.2021.03057

摘要/Abstract

摘要：

作物病害是影响粮食产量和质量的生物灾害, 病害的侵染消耗了作物营养和水分, 扰乱了其正常的生命过程, 引起了作物内部生理生化和外部表观形态的改变。冠层反射光谱能够较好地探测作物群体结构信息, 叶绿素荧光能敏感反映作物光合生理上的变化, 二者均能够实现作物病害的遥感探测。本文从作物病害遥感探测的方法和尺度两个方面综述了基于反射率光谱的作物病害遥感监测现状, 概括了主动荧光、被动荧光以及协同日光诱导叶绿素荧光和反射率光谱在作物病害遥感监测中的研究进展, 分析了反射率光谱和叶绿素荧光数据在作物病害遥感探测方面的优缺点, 探讨了不同数据源、不同监测方法在作物病害遥感探测中可能存在的问题, 并在此基础上展望了作物病害遥感监测的未来发展, 旨在为后续利用反射率光谱和叶绿素荧光数据探测作物病害提供重要的参考依据。

关键词: 反射率, 叶绿素荧光, 作物病害, 遥感监测

Abstract:

Crop diseases are biological disasters that affect grain production and quality. The infestation of diseases consumes the nutrients and water, disrupts its normal life process, and causes changes in the internal physiological and biochemical state and external appearance of the crop. Canopy reflectance spectrum can detect crop population structure information well, and chlorophyll fluorescence data can sensitively reflect changes in crop photosynthetic physiology, both methods are capable of detecting crop diseases via remote sensing technology. This article outlined the current research status of crop diseases detection based on reflectance spectrum through remote sensing technology from the aspects of monitoring methods and monitoring scales, summarized the research progress of using active fluorescence, passive fluorescence and coordinated solar-induced chlorophyll fluorescence and reflectance spectroscopy to monitor crop diseases, analyzed the advantages and disadvantages of reflectance spectrum and chlorophyll fluorescence data in crop disease early warning detection, and discussed the possible problems in the remote sensing detection of crop diseases. On the basis, we made a prospect for the development of remote sensing monitoring crop diseases. This paper provides an important reference for the subsequent applications of crop diseases detection based on reflectance spectrum and chlorophyll fluorescence data.

Key words: reflectance, chlorophyll fluorescence, crop diseases, remote sensing monitoring

竞霞, 邹琴, 白宗璠, 黄文江. 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[J]. 作物学报, 2021, 47(11): 2067-2079.

JING Xia, ZOU Qin, BAI Zong-Fan, HUANG Wen-Jiang. Research progress of crop diseases monitoring based on reflectance and chlorophyll fluorescence data[J]. Acta Agronomica Sinica, 2021, 47(11): 2067-2079.

图/表 6

表1

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不同尺度的作物病害遥感监测应用案例"

监测尺度 Monitoring scale	设备 Devices	特点 Characteristics	病害类型 Type of diseases	参考文献 Reference
叶片及冠层尺度 Leaf and canopy scale	非成像高光谱扫描仪、成像高光谱仪。 Non-imaging hyperspectral scanner and imaging hyperspectral spectrometer.	方便、灵活以及受外界因素影响较小、监测精度高, 通常用于作物病害的遥感探测机理研究, 受探测范围限制, 难以实现大区域作物病害的遥感探测。 Convenient, flexible and less affected by external factors, with high detection accuracy. It is usually used to study the mechanism of early warning and detection of crop diseases. Due to the limitation of the detection range, it is difficult to realize the correction and detection of large-area crop diseases.	甜菜叶斑病、叶锈病和白粉病 Sugar beet leaf spot, leaf rust and powdery mildew	[66]
叶片及冠层尺度 Leaf and canopy scale			小麦白粉病 Wheat powdery mildew	[67]
地块尺度 Plot scale	成像多光谱仪、成像高光谱相机、热红外成像仪。 Imaging multi-spectrometer, imaging hyperspectral camera and thermal infrared imager.	通常利用搭载于航空平台的传感器监测, 探测范围较大, 数据源获取相对航天数据更为灵活且受天气状况影响较小, 对爆发性流行病害具有一定的应急监测能力。 Usually, the sensors carried on the aviation platform are used for monitoring, which has a large detection range, more flexible data source acquisition compared with space data, and less affected by weather conditions, and have a certain emergency monitoring ability for explosive epidemic diseases.	水稻穗瘟病 Rice panicle blast	[26]
地块尺度 Plot scale			番茄晚疫病 Tomato late blight	[68]
区域尺度 Regional scale	多光谱卫星、高光谱卫星、热红外卫星。 Multispectral satellites, hyperspectral satellites and thermal infrared satellites.	探测范围广, 以卫星数据为数据源, 能周期性地对同一地区进行重复监测, 为大尺度病害预报和流行趋势提供依据。 Wide detection range. Using satellite data as a data source, it can periodically re-monitor the same area and provide a basis for large-scale disease forecasts and epidemic trends.	小麦锈病 Wheat rust	[69]
			小麦白粉病 Wheat powdery midew	[70]
			小麦黄锈病 Wheat yellow rust	[71]
			芦笋紫斑病 Asparagus purple spot disease	[72]
			黄锈病和蚜虫 Wheat yellow rust and aphid	[73]

表3

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图2

表4

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作物病害类型 Type of crop diseases	光谱响应波段 Spectral response band (nm)	特征选择算法 Feature selection algorithm	参考文献 References
小麦条锈病 Wheat stripe rust	560-670	Filter	[24]
小麦白粉病 Wheat powdery mildew	490, 510, 516, 540, 780, 1300	Filter	[25]
水稻穗颈瘟 Rice panicles blast	430-530, 580-680, 1480-2000	Filter	[26]
番茄晚疫病 Tomato late blight	700-750, 750-930, 950-1030, 1040-1130	Filter	[27]
棉花黄萎病 Cotton verticillium wilt	680-760, 731-1371	Filter	[28]
玉米大斑病 Corn leaf blight	725-740	Filter	[29]
小麦条锈病、小麦白粉病 Wheat yellow rust, wheat powdery mildew	480, 633, 934	Wrapper	[30]
水稻颖枯病 Rice panicles	450-850	Wrapper	[31]
番茄叶斑病 Tomato bacterial spot	395, 633-635, 750-760	Wrapper	[32]
花生叶斑病 Peanut leaf spots	761, 938	Wrapper	[33]
苹果黑星病 Venturia inaequalis infection	1350-1750, 2200-2500	Embedded	[34]
马铃薯晚疫病 Potato late blight	600-900	Embedded	[35]

模型 Model	作物病害种类 Type of crop diseases	算法 Algorithm	文献 Reference
统计模型 Statistical model	小麦白粉病 Wheat powdery mildew	相关分析、方差分析 Correlation analysis and variance analysis	[41]
	小麦条锈病 Wheat stripe rust	线性回归、非线性回归 Linear regression and nonlinear regression	[42]
	小麦白粉病 Wheat powdery mildew	Logistic回归 Logistic regression	[43]
	番茄叶斑病 Tomato bacterial spot	偏最小二乘回归、多元逐步回归 Partial least squares regression and multiple stepwise regression	[44]
	小麦条锈病 Wheat stripe rust	偏最小二乘法 Partial least squares	[9]
人工智能模型 Artificial intelligence model	水稻颖枯病、曲霉病 Rice glume blight disease and false smut disease	主成分分析 Principal component analysis	[31]
	黄瓜花叶病毒 Cucumber mosaic virus	人工神经网络 Artificial neural network	[45]
	小麦白粉病 Wheat powdery mildew	Fisher线性判别分析、AdaBoost和支持向量机 Fisher linear discriminant analysis, support vector machine, and AdaBoost model	[46]
	大豆枯萎病 Soybean sudden death syndrome	偏最小二乘判别分析 Partial least squares discriminant analysis	[47]
	油棕茎腐病 Oil palm basal stem rot	决策树、随机森林和支持向量机 Decision tree, random forest, and support vector machine	[48]
	小麦白粉病 Wheat powdery mildew	随机森林 Random forest	[49]
	蚕豆病虫害 Broad bean disease and pests	聚类算法 K-Means and the FCM clustering	[50]

反演算法 Retrieval algorithms		Fraunhofer线内外反射率和荧光关系 The relationship of reflectance and SIF between the internal and external Fraunhofer dark line respectively	参考文献 Reference
单波段 Single spectrum	FLD	r(λ_out) = r(λ_in), F(λ_out) = F(λ_in)	[102]
	3FLD	r(λ_in) = r(λ_left)×ω_left+r(λ_right)×ω_right, F(λ_out) = F(λ_in)	[103]
	iFLD	r(λ_out) = α_Rr(λ_in), F(λ_out) = α_FF(λ_in)	[104]
	SFM	r(λ) = f(r_λ), F(λ) = f(F_λ)	[106]
	pFLD	$\ddot{R}(\lambda )=\sum\limits_{i=1}^{n}{{{k}_{i}}{{\phi }_{i}}(\lambda )} $	[105]
全波段 Full spectrum	SFM	r(λ) = S(r_λ), F(λ) = V(F_λ)	[107]
	FSR	F(λ) ≈ b₀+b₁∙(λ-λ₀)+b₂∙(λ-λ₀)², r(λ) ≈ b₃+b₄∙(λ-λ₀)+b₅∙(λ-λ₀)²	[108]
	F-SFM	$r(\lambda )=\sum\limits_{i}^{m}{{{k}_{i}}{{\varphi }_{i}}}(\lambda )$., $F(\lambda)=\sum_{i}^{n} j_{i} {\phi }_{i}(\lambda)$	[109]