植被叶绿素含量的高光谱遥感反演方法研究进展

doi:10.13466/j.cnki.lczyyj.2024.05.018

摘要/Abstract

摘要：

叶绿素含量对于植物的光合作用能力至关重要,是评估植被生长状况的重要指标。叶绿素含量的测定对植物的健康状况、施肥管理以及产量评估具有显著意义。然而,传统的测量方法费时费力。近年来,高光谱遥感技术作为一种前沿技术,得到了迅速发展,利用高光谱数据进行叶绿素含量估算已成为一种重要手段。全面回顾国内外典型机载星载高光谱成像仪发展历程及其数据应用,通过查阅国内外相关文献,分析光谱植被指数构建、高光谱红边位置参数和机器学习算法3种方法在高光谱数据反演叶绿素中的优势与局限性,并指出当前高光谱遥感发展及植被叶绿素定量反演研究中存在的不足,提出未来的研究方向。

关键词: 叶绿素反演, 高光谱发展历程, 光谱指数, 红边位置, 机器学习

Abstract:

Chlorophyll content is crucial for the photosynthetic capacity of plants and serves as an important indicator of vegetation growth status.Accurate measurement of chlorophyll content is essential for assessing plant health,optimizing fertilizer management,and evaluating crop yields.However,traditional measurement methods are time-consuming and labor-intensive.In recent years,hyperspectral remote sensing technology has been rapidly developing as a cutting-edge technology,and using hyperspectral data for estimating chlorophyll content has become an important approach.This paper provides a comprehensively review of the development of typical airborne star-borne hyperspectral imagers both domestically and internationally.By analyzing relevant literature,the paper analyzes the advantages and limitations of three methods,namely,spectral vegetation index construction,hyperspectral red-edge positional parameters and machine learning algorithms,in inverting chlorophyll from hyperspectral data,and points out the shortcomings of the current development of hyperspectral remote sensing and the research on quantitative inversion of vegetation chlorophyll,and proposes the future research direction.

Key words: chlorophyll inversion, hyperspectral development history, spectral indices, red-edge position, machine learning

中图分类号:

TP79

马荣菲, 陈妍, 侯鹏, 任晓琦. 植被叶绿素含量的高光谱遥感反演方法研究进展[J]. 林草资源研究, 2024,(5): 166-178.

MA Rongfei, CHEN Yan, HOU Peng, REN Xiaoqi. Progress on Hyperspectral Remote Sensing Inversion Method for Vegetation Chlorophyll Content[J]. Forest and Grassland Resources Research, 2024,(5): 166-178.

图/表 5

表1

表2

国内外星载高光谱成像技术及其应用

国家	高光谱成像传感器	应用
美国	MightSat-II.1/FTHSI	用于地形分类
美国	EO-1/Hyperion	可以实现精确的农作物估产、地质填图、精确制图,应用于采矿、地质、森林、农业以及环境保护领域
欧洲	PROBA/CHRIS	研究植被BRDF现象
欧洲	Envisat-1/ MERIS	主要对水色进行观测,促进了海洋生物学和海洋水色的遥感观测
美国	MRO/CRISM	研究地表矿物成分、绘制关键区域的矿物以及测量大气的空间和季节性变化等
美国	HICO	研究沿海海洋和河口、河流或其他浅水区域
印度	Cartosat 2E/HRMX	用于自然资源普查、灾害管理、地面形态以及农作物、植被等探测
德国	EnMAP	促进空间成像光谱产品的普及,并提供有关不同生态系统状况的信息
美国	HyspIRI	研究世界生态系统,并提供有关火山、野火和干旱等自然灾害的重要信息
俄罗斯	Resurs-P1/P2/P3/P4/P5	应用于农业、渔业、气象、交通、紧急情况、自然资源和国防等方面
中国	神舟三号/CMODIS	研究海洋、陆地和大气,它对地球进行了连续的遥感观测
	嫦娥一号	分析月球表面矿物的化学成分
	FY-3A/B/C卫星/MERSI-I	实现植被,生态,土地覆盖分类和积雪覆盖的全球地表特征
	FY-3D/MERSI-II	结合了MERSI-I和VIRR的功能
	FY-3E/MERSI-LL	首次具备了夜间可见光波段观测的能力
	FY-3F/MERSI-III	提高了定标精度、观测灵敏度和使用寿命
	FY-3G/MERSI-RM	精确的云和降水观测
	HJ-1A	动态监测生态环境变化,有利于开展大气成分监测、水环境监测和植被生长监测等定量研究
	天宫一号	用于森林防火、石油和天然气勘探、水文生态监测和地质调查
	SPARK	应用于农业预测、病虫害监测、环境保护和灾害监测
	高分五号	能够实现对陆地和大气的全面观测
	珠海一号	应用于农业、林业、生态环境、自然资源等领域
	资源一号02 D卫星	应用于自然资源调查和监测
	天问一号	对火星的表面形貌、土壤特性、物质成分、水冰、大气、电离层、磁场等的科学探测
	高分五号01A	主要应用于环境污染监测、环境质量监管、大气成分监测、自然资源调查、气候变化研究等

表2

表3

表4

表5

红边位置提取方法

红边位置提取方法	算法	参考文献
拉格朗日插值	A= $D λ (i - 1) (λ i - 1 - λ i) (λ i - 1 - λ i + 1)$ ;B= $D λ (i) (λ i - λ i - 1) (λ i - λ i + 1)$ C= $D λ (i + 1) (λ i + 1 - λ i - 1) (λ i + 1 - λ i)$ R_EP= $A (λ i + λ i + 1) + B (λ i - 1 + λ i + 1) + C (λ i - 1 + λ i) 2 (A + B + C)$	[70]
线性插值	(1)R_re=(R₆₇₀+R₇₈₀)/2 (2)λ_re=700+40[(R_re-R₇₀₀)/(R₇₄₀)-R₇₀₀)]	[71]
最大一阶导数	D_λ₍_i₎=(R_λ₍_j₊₁₎-R_λ₍_j₎)/△λ	[72]
倒高斯模型	R_λ=(R_s-R₀)exp- $(λ - λ 0) 2 2 k 2$	[73]
线性外推法	R_EP=-(c₁-c₂)/(m₁-m₂)	[74]
多项式拟合法	ρ=a₀+ $∑ i = 1 5$ a_iλⁱ	[75]

表5

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国家		高光谱成像传感器	应用
国外	美国	AIS	获取美国一些地质目标上和各地的植被上的数据
	美国	AVIRIS	在美国各地进行广泛的数据收集,以支持NASA的数据评估和技术评估计划
	加拿大	CASI	具有辐射精度较高的初始校准程序
	澳大利亚	HyMap	用于商业探测
	德国	HySpex	适合用于基准参考测量和对地观测应用的可行性研究
国内		MAIS	地质和环境调查
		OMIS	安装了高质量的差分GPS,定位精度优于10 m
		PHI	植被生长状态、湿地生态环境调查,地物精细光谱分析,环境监测和城市规划

反演方法	模型名称	优点	缺点	参考文献
光谱植被指数构建法		计算比较简单,速度较快	对不同植被及其不同生育期内植被指数的适用性不同,进而影响反演精度	[40]
高光谱红边位置参数法		对叶绿素含量非常敏感,能够捕捉到植物叶片中叶绿素变化的微小差异	红边位置的提取相对复杂,对光谱数据的质量要求较高,通常需要更高光谱分辨率,不适用于低分辨率的遥感数据	[41]
	偏最小二乘回归	计算量小,处理数据速度快	容易受到观测噪声的影响,且在时间和空间维度上受到限制,适用于对模型精度要求不高	[42-43]
	支持向量机回归	适合解决小样本的问题;能将多目标问题转化为单目标问题	不适合处理大规模数据集,计算时间较长	[44]
机器学习算法	随机森林	对数据噪声和异常值有较好的稳定性;能够评估各特征对模型的重要性	由于模型的复杂性,解释性差	[45]
	BP神经网络	有很强的非线性转化能力	当训练集和验证集和特征空间的差异较小时,容易出现拟合问题	[46]
	梯度提升回归树	有较强的特征选择的能力,能够实现降维;泛化能力较强	对参数设置敏感,因此要选择合适的参数,否则影响模型精度	[47]

指数类型	光谱植被指数	计算公式	参考文献
差值光谱指数	差值植被指数(Difference Vegetation Index,DVI)	R₈₁₀-R₆₈₀	^[48]
	反射率差(Reflectance Difference1,RD1)	R₈₀₀-R₆₈₀	^[49]
	反射率差(Reflectance Difference2,RD2)	R₇₀₅/R₅₀₅	^[49]
比值光谱指数	比值植被指数(Ratio Vegetation Index,RVI)	R₈₁₀/R₆₈₀	^[50]
	色素比值指数(Pigment Specific Simple Ratio Index,PSSR)	P_SSRa=R₈₀₀/R₆₇₅	^[51]
	色素比值指数(Pigment Specific Simple Ratio Index,PSSR)	P_SSRb=R₈₀₀/R₆₅₀	^[51]
归一化光谱指数	归一化植被指数(Normalized Difference Vegetation Index,NDVI)	(R₈₁₀-R₆₈₀)/(R₈₁₀+R₆₈₀)	^[52]
	光化学植被指数(Photochemical Reflectance Index,PRI)	(R₅₃₁-R₅₇₀)/(R₅₃₁+R₅₇₀)	^[53]
	绿色归一化光谱指数(Green Normalized Difference Vegetation Index,GNDVI)	(R₇₅₀-R₅₅₀)/(R₇₅₀+R₅₅₀)	^[54]
	优化土壤调节植被指数(Optimized Soil Adjusted Vegetation Index,OSAVI)	(R_NIR-R)/(R_NIR+R+0.16)	^[55]
多波段光谱指数	草地叶绿素指数(Grassland Chlorophyll Index,GCI)	(R₇₈₀-R₇₃₅)/(R₇₁₅-R₆₇₀)	^[56]
	MERIS陆地叶绿素指数(MERIS Terrestrial Chlorophyll Index,MTCI)	(R₇₅₃_.₇₅-R₇₀₈_.₇₅)/(R₇₀₈_.₇₅-R₆₈₁_.₂₅)	^[57]
	修正叶绿素吸收反射率指数(Modified Chlorophyll Absorption Ratio Index,MCARI)	[(R₇₀₀-R₆₇₀)-0.2(R₇₀₀-R₅₅₀)]× (R₇₀₀/R₆₇₀)	^[58]