利用交叉验证的小麦LAI反演模型研究

引用本文

任哲, 陈怀亮, 王连喜, 李颖, 李琪. 利用交叉验证的小麦LAI反演模型研究[J]. 国土资源遥感, 2015,27(4): 34-40
REN Zhe, CHEN Huailiang, WANG Lianxi, LI Ying, LI Qi. Research on inversion model of wheat LAI using cross-validation[J]. REMOTE SENSING FOR LAND & RESOURCES,2015,27(4): 34-40 复制到剪切板

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《国土资源遥感》编辑部

利用交叉验证的小麦LAI反演模型研究

任哲^1,², 陈怀亮³, 王连喜^1,², 李颖³, 李琪^1,²

1.南京信息工程大学江苏省大气环境监测与污染控制高技术研究重点实验室,南京 210044

2.南京信息工程大学环境科学与工程学院,南京 210044

3.中国气象局河南省农业气象保障与应用技术重点实验室,郑州 450003

通信作者:王连喜(1959-),男,教授,主要从事农业气象与生态气象方面的研究。Email: wlx4533@sina.com。

第一作者简介:任哲(1989-),男,硕士研究生,主要研究方向为农业遥感。Email: renzhe_za@163.com。

收稿日期: 2014-07-18

基金项目: 公益性行业(气象)科研专项“主要农作物生长动态监测与定量评价技术研究”(编号: GYHY200906022)、国家科技支撑计划项目“全球变化环境下作物产量的影响与适应监测评估技术”(编号: 2012BAH29B03)和“十二五”国家科技支撑计划重点项目“重大农业气象灾害预测预警关键技术研究”(编号: 2011BAD32B02)共同资助

摘要

叶面积指数(leave area index,LAI)是表征植被冠层结构和生长状况的关键参数,采用遥感技术进行LAI反演是遥感反演领域的热点和难点之一。利用小麦关键生育期的高光谱数据,计算其一阶和二阶导数,并构建植被指数(RVI,NDVI,EVI,DVI和MSAVI)及三边变量参数等高光谱变量;将上述参数与小麦LAI数据进行相关性分析,并利用交叉验证法进行多种回归分析,确定反演小麦LAI的敏感参数,选择反演模型;最后使用敏感参数构建所有样本的小麦LAI反演模型,并比较其拟合效果。研究结果表明:经过交叉验证的反演建模,其拟合结果的均方根误差(RMSE)整体上较未经交叉验证反演建模结果的RMSE小;在用敏感参数构建的回归模型中,RVI立方回归模型是用遥感数据反演小麦LAI的最优模型。

关键词: 叶面积指数(LAI); 遥感反演; 交叉验证; 小麦

中图分类号:TP 751.1 文献标志码:A 文章编号:1001-070X(2015)04-0034-07 doi: 10.6046/gtzyyg.2015.04.06

Research on inversion model of wheat LAI using cross-validation

REN Zhe^1,², CHEN Huailiang³, WANG Lianxi^1,², LI Ying³, LI Qi^1,²

1. Jiangsu Key Laboratory of Atmospheric Environmental Monitoring and Pollution Control, Nanjing University of Information Science and Technology, Nanjing 210044, China

2. School of Environmental Science and Engineering of Nanjing University of Information Science and Technology, Nanjing 210044, China

3. CMA/Henan Key Laboratory of Agrometeorological Support and Applied Technique, Zhengzhou 450003, China

Abstract

Leaf area index (LAI) is the key parameter to signify the growth condition and canopy structure of vegetation. Inversion of LAI using remote sensing technology is always one of the hotspots and difficulties in the field of remote sensing. In this paper, the first and second order derivatives of hyperspectral data of wheat were calculated, and several vegetation indices (RVI, NDVI, EVI,DVI and MSAVI) and trilateral variable parameters were built for the analysis. The correlation analysis between the parameters and wheat LAI data was carried out, and the method of cross-validation was used for multiple regression analysis so as to determine the sensitive parameters for wheat inversion of LAI and choosing model type of inversion. At last, the inversion models of all the samples were built by using these sensitive parameters, and their imitative effects were comparatively studied. The results show that the majority of root mean square errors(RMSE)of the inverse models using cross-validation are larger than those of the models which do not use cross-validation. In addition, among all the models built by the sensitive parameters, the cubic regression model of RVI is the optimal model for inversion of wheat LAI with remote sensing data.

Keyword: leaf area index(LAI); remote sensing inversion; cross-validation; wheat

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0 引言

叶面积指数(leaf area index, LAI)是表征植被冠层结构的关键参数, 也是植被系统的一个重要参数。LAI能够对植被的冠层结构赋予直接的量化指标^[1], 获取准确的LAI数据对提高作物长势监测和产量预测精度具有重要价值^[2]。LAI的反演研究在遥感技术发展早期就受到了重视, 也一直是遥感反演领域的热点和难点之一。Bunnik^[3]证实了利用遥感技术提取植被LAI的可能性, 为LAI的反演奠定了基础; 而高光谱遥感技术的兴起则大大加快了遥感反演技术从定性到定量发展的步伐^[4]。对LAI的反演方法主要有物理模型法和经验模型法^{[5, 6]}。物理模型法用以物理方法为基础的植被冠层反射率模型进行LAI反演^[7], 能建立植被生理生化参数与冠层反射率的联系, 但模型的解并不唯一^[8]; 经验模型法则是指建立LAI与植被特征之间的回归模型, 但模型受限于饱和效应^[9]。

高光谱遥感数据具有波段窄且数量多的特点, 相比多光谱数据, 与LAI具有更高的相关性^[10]。研究者们通常利用高光谱数据的特性确定敏感参数, 建立LAI经验模型。农业遥感中, 利用高光谱数据及其建立的植被指数能准确地反映出作物本身的光谱特征, 并更方便地获取农学信息^[11]。目前, 高光谱的导数光谱技术(differential spectroscopy)及红边参数也越来越多地被用于反演LAI^[12]。利用实测光谱模拟MODIS等效反射率, 构建植被指数及其与小麦LAI之间的经验模型^[13], 可为利用遥感技术进行大面积作物信息提取提供重要手段^[14]。王秀珍等^[15]通过水稻高光谱数据的各种变换形式表明, 以红边面积和蓝边面积为变量的模型与水稻LAI关系密切。当前的研究主要针对LAI与高光谱数据之间的关系, 反演LAI时未能充分考虑因样本数量的局限性及产生的随机误差会引起LAI经验模型中敏感参数和模型类型不同的问题。然而, 通过更精确的统计学方法可以更精细地确定敏感参数以建立LAI经验模型。本文利用小麦高光谱数据、高光谱导数数据、植被指数及三边变量参数等数据, 通过交叉验证建立小麦LAI的反演模型, 并利用交叉验证后反演模型的决定系数(R²)和均方根误差(RMSE)判断反演结果的拟合效果。研究结果表明:交叉验证法可提高对小麦LAI反演结果的评判精度, 更准确地提取反演小麦LAI的敏感参数及模型类型, 并建立小麦LAI遥感反演的最优模型。

1 研究方法

1.1 试验方法

小麦高光谱数据及LAI采集试验于2014年3— 5月间在河南省郑州市农业气象试验站(E 113° 39', N 34° 42')进行, 涵盖拔节期、孕穗期、抽穗期和乳熟期等不同生育期中不同长势的小麦。试验区年平均气温14.3℃, 年平均降雨量632 mm, 土壤类型为砂壤土, 小麦品种为郑麦366。

试验选择晴朗无云的天气, 测量时间在10:00~12:00和14:00~15:00之间。本次试验选择长势具有代表性的小麦, 取0.6 m× 0.6 m的样方, 使用冠层分析仪在垄间测定小麦的LAI值, 并采集该范围内的小麦高光谱数据。样方内小麦长势均等, 垄间距离相对均匀。将经过多次测量后的平均值作为该样方内小麦的光谱反射值和LAI值, 以消除仪器所带来的随机噪声。

测量小麦高光谱数据的光谱仪是美国SVA公司生产的GER 1500便携式野外光谱测定仪, 其光谱范围为350~1 050 nm, 拥有512个通道, 光谱采样间隔为1.5 nm, 光谱分辨率为3 nm。每次采集数据前均用参照板对该光谱仪进行校正, 以消除环境变化给采集数据带来的影响。测量小麦LAI数据的仪器是美国LI-COR公司生产的LAI-2000植物冠层分析仪, 其使用320~490 nm的感应波段, 通过“ 鱼眼” 镜头感应天空被遮蔽的情况, 进而得到太阳辐射的透过率。根据LAI-2000对测量天气(在阴天等散射光条件下进行观测)的要求, 测量时在“ 鱼眼” 镜头上使用了270%的遮光盖以减少阳光直射的影响, 且尽可能在太阳被云遮挡的时候进行测量。测量时, 光谱仪与冠层分析仪的镜头都水平朝向天空, 且不被测量者身影遮挡阳光, 以免测量者被计入LAI值内, 而且测量者都穿着深色不反光的衣物。

1.2 数据处理

本次试验采集到81组有效的小麦高光谱及对应的LAI数据, LAI的平均值为4.65, 最大值为7.85, 最小值为1.09。计算出小麦高光谱数据样本的一阶和二阶导数光谱(对高光谱数据进行求导能在一定程度上削弱土壤背景的影响), 用于进行逐步回归分析和对小麦LAI进行反演。由于高光谱数据存在着采样间隔的离散型, 故导数光谱需用差分法来近似计算^[16], 即

一阶导数光谱为

$\begin{matrix} R' (λ_{i}) = \frac{dR (λ_{i})}{dλ} = \frac{R (λ_{i + 1}) - R (λ_{i - 1})}{2 △ λ} \end{matrix}$ , (1)

二阶导数光谱为

$\begin{matrix} R″ (λ_{i}) = \frac{d^{2} R (λ_{i})}{d λ^{2}} = \frac{R' (λ_{i + 1}) - R' (λ_{i - 1})}{2 △ λ} = \frac{R (λ_{i + 2}) - 2 R (λ_{i}) + R (λ_{i - 2})}{4 {(△ λ)}^{2}} \end{matrix}$ , (2)

式中:λ _i为波段i的波长; R(λ _i)为波长λ _i处的光谱反射率; △ λ 为波长λ _i-₁到λ _i的差值。采用导数光谱可以消除部分背景和大气散射光谱对目标光谱的影响。

为了减小样本的随机性给反演效果带来的误差, 本文通过交叉验证进行模型选择。为了对模型进行验证, 本文选用了2014年4月初— 5月底山东省1 km空间分辨率的MODIS-L1B和MODIS-LAI数据, 利用ENVI软件进行几何纠正及裁剪等预处理。在利用小麦高光谱数据构建表1中的各植被指数时, 分别选用与MODIS传感器通道对应的蓝光波段(459~479 nm)、红光波段(620~670 nm)及近红外波段(841~876 nm)高光谱实测数据, 模拟MODIS传感器通道进行大面积应用。

表1 研究中采用的植被指数 Tab.1 Vegetation indices used in this study

植被指数	计算方法	出处
比值植被指数(RVI)	RVI=R_nir/R_red	Jordan^[17]
归一化差值植被指数(NDVI)	NDVI=(R_nir-R_red)/(R_nir+R_red)	Rouse et al^[18]
增强型植被指数(EVI)	EVI=2.5 (R_nir-R_red)/(R_nir+6 R_red-7.5 R_blue+1)	Liu et al^[19]
差值植被指数(DVI)	DVI=R_nir-R_red	Richardson et al^[20]
修正型土壤调节植被指数(MSAVI)	MSAVI=[2 R_nir+1- $\begin{matrix} \sqrt{(2 R_{nir} {+ 1)}^{2} - 8 (R_{nir} - R_{red})} \end{matrix}$ ]/2	Qi et al^[21]

表1 研究中采用的植被指数 Tab.1 Vegetation indices used in this study

利用小麦高光谱数据构建的各类常见植被指数(表1)和三边变量参数(表2)^{[15, 22, 23]}, 能够很好地反映出小麦在各生育期中的植被特征。

表2 研究中采用的三边变量参数 Tab.2 Trilateral variable parameters in this study

1.3 交叉验证法

交叉验证法主要用于选择模型, 在统计学上是将数据样本切割为小子集, 通过对各子集之间的分析与验证确立模型类型^[24]。该方法不需要任何前提便可直接估计误差, 具有应用普遍性和操作简便性, 是一种行之有效的模型选择方法; 特别是在数据量不足的情况下, 数据的重复利用能在模型选择方面显示出更多的优越性^[25]。交叉验证法分为保留交叉验证(hold-out)、留P交叉验证和K折交叉验证。本文使用的是9折交叉验证法。验证的基本过程为:将高光谱数据及导数光谱数据平均分为9组, 轮流选取其中8组数据作为训练集, 使用统计产品与服务解决方案(statistical product and service solutions, SPSS)软件进行逐步回归分析, 建立LAI反演模型; 利用剩余的1组数据作为验证集, 进行拟合精度检验。如上所述共重复进行9次回归分析及精度检验, 最后对9次精度检验的决定系数和均方根误差进行平均, 来检验小麦LAI反演模型的拟合效果。所有小麦LAI数据的方差为2.81; 在对数据进行分组之后, 9组LAI数据的平均方差为2.73。

2 结果与分析

2.1 相关性分析

图1示出小麦高光谱数据与LAI数据的相关关系。

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	图1 小麦高光谱数据与LAI的相关关系Fig.1 Correlation between hyperspectral data and LAI of wheat

从图1可以看出, 当波长小于728 nm时, 小麦高光谱数据与LAI呈负相关关系, 此区间内相关系数绝对值最大的为波长661 nm处的 -0.634, 且在该处形成了一个波谷; 同时, 在蓝光波段的光谱数据与小麦LAI也具有较高的相关性, 形成了另一个波谷。相关系数在680 nm处开始迅速增大, 在764 nm之后趋于平稳, 并在802 nm处达到了最大值0.627。

对5个植被指数和6个三边变量参数与小麦LAI值进行相关性分析的结果(表3)表明, 所有的参数与小麦LAI的相关性均通过了0.01的极显著性检验水平, 表现出了极好的相关性。在植被指数与小麦LAI的相关分析中, RVI与小麦LAI的相关性最优(相关系数为0.905), 其后依次为NDVI, MSAVI, EVI和DVI。而在三边变量参数与小麦LAI的相关分析中, 相关性最好的为红边面积和蓝边面积的比值(S_red/S_blue), 相关系数为0.840, 但小于RVI和NDVI与小麦LAI的相关系数; 而其归一化值[(S_red-S_blue)/(S_red+S_blue)]与小麦LAI的相关系数为0.833。其中, 由于黄边面积S_yellow为负值, 因此, 红边面积和黄边面积的比值(S_red/S_yellow)及其归一化值[(S_red-S_yellow)/(S_red+S_yellow)]与小麦LAI呈负相关关系, 且在所有参数与小麦LAI的相关性中表现不佳。

表3 植被指数、三边变量参数与小麦LAI的相关系数 Tab.3 Correlation coefficient between vegetation indices, trilateral variable parameters and LAI of wheat

2.2 小麦LAI反演模型的效果比较

本文使用小麦高光谱数据、光谱导数数据、植被指数及三边变量参数等数据, 利用交叉验证法对小麦LAI进行回归分析, 其结果如表4所示。

表4 交叉验证法反演小麦LAI模型的拟合效果 Tab.4 Imitative effect of wheat LAI model inversed by cross-validation

参数	逐步回归模型			对数回归模型		指数回归模型			立方回归模型
参数	R²		RMSE		R²	RMSE	R²	RMSE	R²	RMSE
高光谱数据	0.852	0.642 7		-	-	-	-		-	-
一阶导数数据	0.900	0.589 3		-	-	-	-		-	-
二阶导数数据	0.846	0.639 7		-	-	-	-		-	-
RVI	-	-		0.826	0.643 5	0.727	0.688 4		0.848	0.595 7
NDVI	-	-		0.686	0.722 8	0.809	0.655 7		0.840	0.641 9
EVI	-	-		0.563	0.794 8	0.526	0.825 2		0.566	0.748 4
DVI	-	-		0.512	0.835 5	0.452	0.977 2		0.514	0.855 8
MSAVI	-	-		0.607	0.721 8	0.570	0.745 4		0.613	0.730 7
R_green/R_red	-	-		0.685	0.710 8	0.658	0.730 2		0.705	0.719 7
(R_green-R_red)/(R_green+R_red)	-	-		0.592	0.735 7	0.714	0.716 9		0.707	0.714 3
S_red/S_blue	-	-		0.808	0.648 2	0.694	0.713 2		0.737	0.679 3
S_red/ $\begin{matrix} {S_{yellow}}^{①} \end{matrix}$	-	-		-	-	0.421	1.024 3		0.515	0.853 3
(S_red-S_blue)/(S_red+S_blue)	-	-		0.613	0.749 8	0.666	0.714 8		0.794	0.657 4
(S_red-S_yellow)/(S_red+S_yellow)	-	-		0.503	0.865 1	0.454	0.973 5		0.514	0.845 7

①黄边面积(S_yellow)为负数, 对S_red/S_yellow不能进行对数回归分析。

表4 交叉验证法反演小麦LAI模型的拟合效果 Tab.4 Imitative effect of wheat LAI model inversed by cross-validation

对小麦高光谱数据及其一阶和二阶导数光谱数据进行逐步回归交叉验证的结果表明, 其反演模型的R²比植被指数和三边变量参数回归模型的R²大, 在逐步回归模型中, 被优先选入的波段多为近红外波段和红光波段等对绿色植被较为敏感的波段。从表4中还可以看出, 经过交叉验证后, 逐步回归模型的RMSE比植被指数和三边变量参数与小麦LAI回归模型的RMSE小。其中, 小麦高光谱一阶导数数据对小麦LAI的逐步回归反演模型的R²达到了0.900, RMSE为0.589 3, 是所有模型中拟合效果最好的; 但因该逐步回归模型选入的波段数量较多以及需要采用导数计算方式, 在实际应用中并不方便。

在5个植被指数对小麦LAI进行的非线性(对数、指数和立方)回归分析中, RVI和NDVI的3种回归模型的反演拟合效果明显优于其余3种植被指数反演模型, 其R²较大, RMSE较小, 可作为反演小麦LAI的敏感参数; 其中, 又以RVI的立方回归模型的拟合效果最佳(R²=0.848, RMSE=0.595 7)。其余3种植被指数(EVI, DVI和MSAVI)对小麦LAI回归模型的反演拟合效果较差, 不适合用于对小麦LAI的反演。从表4可以看出, 利用植被指数对小麦LAI进行非线性回归分析时, 立方回归模型在整体上拟合效果优于对数回归模型和指数回归模型。

在利用三边变量参数对小麦LAI进行非线性回归分析中, 其拟合效果与植被指数对小麦LAI回归分析的效果接近, 其中最优的为S_red/S_blue的对数回归模型(R²=0.808, RMSE=0.648 2)。与植被指数对小麦LAI的非线性回归分析结果类似的是, 利用三边变量参数对小麦LAI反演的结果中, 立方回归模型在整体上拟合效果优于对数回归模型和指数回归模型。在对非线性回归的结果进行分析时可以发现, R²并不能决定该模型RMSE的大小; 同时, R²也不能很好地反映出模型的估测精度。由于R²是模型估算值与真实值之间的曲线相关关系的反映^[26], 因此RMSE成为本次研究中更重要的判断指标。

2.3 基于敏感参数的小麦LAI反演模型

由表4可知, RVI, NDVI, S_red/S_blue和(S_red-S_blue)/(S_red+S_blue)在反演小麦LAI中都是较为敏感的参数, 其中, RVI, NDVI, (S_red-S_blue)/(S_red+S_blue)立方回归模型以及S_red/S_blue对数回归模型对小麦LAI具有较好的拟合效果。利用小麦在所有生育期内的样本数据, 通过上述4种模型对小麦LAI进行回归建模, 得到的结果如图2所示。

	Figure Option View Download New Window
	图2 基于不同敏感参数的小麦LAI反演模型Fig.2 Wheat LAI inversion model based on different sensitive parameters

通过精度分析可知, RVI立方回归模型、S_red/S_blue对数回归模型和(S_red-S_blue)/(S_red+S_blue)立方回归模型的R²比经过交叉验证的结果更大, 但NDVI立方回归模型的R²比经交叉验证的结果减小了0.01。同时, 与交叉验证后结果的RMSE值相比, NDVI立方回归模型、S_red/S_blue对数回归模型和(S_red-S_blue)/(S_red+S_blue)立方回归模型的RMSE值更小, 但RVI立方回归模型的RMSE值增大了0.003 7。这4个模型当中, 以RVI立方回归模型的拟合效果最优, 其R²最大、RMSE最小。

利用所有样本的小麦高光谱数据及其一阶和二阶导数对小麦LAI进行逐步回归分析后可知, 3个逐步回归模型的R²都有所增加, RMSE有所减小; 但由于被选入的波段数量过多, 且多为近红外与红光波段, 故不便在实际工作中应用。虽然高光谱原始数据及其二阶导数的逐步回归模型的R²大于RVI立方回归模型的R², 但得到的RMSE却更大, 拟合效果仍然不如RVI立方回归模型。

2.4 反演模型的验证

为了评价RVI立方回归模型对小麦LAI的反演效果, 利用MODIS卫星遥感数据对其进行效果验证。验证中利用了覆盖山东省小麦种植区的MODIS-L1B及MODIS-LAI数据, 利用MODIS-L1B的近红外和红光波段构建了NDVI时间序列, 通过阈值法^[27]提取山东省小麦种植区域, 并运用这2个波段构建RVI立方回归模型以提取山东省各地区小麦LAI信息, 与同期的MODIS-LAI数据进行比对。其中, 对拔节期与孕穗期小麦反演得到的LAI值在整体上比同期MODIS-LAI的值偏大; 而到了抽穗期与乳熟期, MODIS-LAI值在整体上比反演得到的小麦LAI值偏大。经过相关分析, 反演得到的小麦LAI值与MODIS-LAI值的相关系数为0.61, 通过了0.01极显著性检验水平。验证结果表明, 利用RVI立方模型进行小麦LAI的反演具有较好的效果。

3 结论

本文利用小麦高光谱数据, 计算出其一阶和二阶导数数据, 并构建植被指数及三边变量参数等数据; 通过交叉验证法对小麦LAI进行回归分析, 并对比各反演模型的拟合效果, 得到以下结论:

1)利用高光谱原始数据及其一阶和二阶导数对小麦LAI进行逐步回归的拟合结果总体优于植被指数及三边变量参数构建的回归模型, 其中又以一阶导数逐步回归模型的拟合效果最优; 在利用敏感参数对小麦LAI进行反演时, RVI立方回归模型比其他模型的拟合效果更好。

2)在运用目前遥感技术对小麦LAI反演时, 由于卫星传感器通道的限制, 导致高光谱数据的导数无法完全获取。因此, RVI立方回归模型可认为是用于遥感数据反演小麦LAI的最优模型; 且在利用空间分辨率更高的遥感数据时, 会具有更好的相关性。随着高光谱遥感技术的发展, 利用交叉验证确定敏感参数及模型类型将会进一步提高反演作物LAI的精确度。

3)经过对同一参数是否利用了交叉验证法进行回归分析的对比之后发现, 各个参数对LAI反演结果中的RMSE值在交叉验证后总体上比未经交叉验证的RMSE值小, 且决定系数R²比未进行交叉验证反演结果的值大。同时, 在分组过程中选择不同的分组方法以及分组数量, 会导致拟合效果的不同, 但不影响交叉验证法在模型选择上的优越性, 相信会有更好的交叉验证法用于选择小麦LAI的反演模型。

The authors have declared that no competing interests exist.

参考文献

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1995

0.0

... LAI能够对植被的冠层结构赋予直接的量化指标^[1],获取准确的LAI数据对提高作物长势监测和产量预测精度具有重要价值^[2] ...

2006

0.0

黄敬峰, 王渊, 王福民, 等. 油菜红边特征及其叶面积指数的高光谱估算模型[J]. 农业工程学报, 2006, 22(8): 22-26.

Huang J

, Wang

, Wang F

, et al. Red edge characteristics and leaf area index estimation model using hyperspectral data for rape[J]. Transactions of the CSAE, 2006, 22(8): 22-26.

The hyperspectral reflectances for rape canopies consisting of five cultivars and three nitrogen levels were measured in 2003～2004 growth period by an ASD FieldSpec Pro FR TM . The corresponding leaf area indices were observed. The red edge parameters were extracted using the spectral reflectances of the rape canopies. There exist ‘two peak’ phenomena for the red edge of rape canopy spectra. The position of red edge λ red is located between 690 nm and 720 nm. There is a ‘platform of red edge’ at the bloom stage of rape. The red edge shift was detected before flowering stage and blue shift after flowering stage. The relationship between red edge parameters and the leaf area index(LAI) was investigated during 8 development periods. Results were not consistently significant at any wavelengths. The leaf area indices were significantly correlative to the red edge parameters λ red , Dλ red and S red of the canopy spectra before flowering stage, whereas the correlation coefficient showed very low r values that were in significant after flowering stage. So the red edge parameters of rape canopies before the flowering stage can be used to estimate the leaf area indices. The results for different stages, linear and non-linear regression models were compared and the optimum model for LAI before flowering stage was recommended.

在2003～2004年油菜生长季，选用6个油菜品种，设置3个氮素水平的小区试验。在不同发育期同步测定油菜冠层的光谱反射率及对应叶片的叶面积指数。利用油菜冠层的光谱反射率数据提取红边参数，分析其变化规律，油菜叶面积指数与红边参数的相关性，估算结果表明：油菜冠层红边一阶导数光谱具有“双峰” 现象，红边位置λ red 位于690～720 nm之间，在油菜生长旺盛期间出现“红边平台”，前期有“红移”，后期有“蓝移”现象；叶面积指数与冠层光谱红边参数λ red 、Dλ red 、S red 之间在开花前存在显著相关，但开花后相关性不显著；利用开花前的红边参数可以估算油菜的叶面积指数，开花后的红边参数不能用于估算油菜的叶面积指数；最后建立了不同时期和开花前油菜叶面积指数的估算模型。

... LAI能够对植被的冠层结构赋予直接的量化指标^[1],获取准确的LAI数据对提高作物长势监测和产量预测精度具有重要价值^[2] ...

1978

0.0

... Bunnik^[3]证实了利用遥感技术提取植被LAI的可能性,为LAI的反演奠定了基础 ...

2010

0.0

... 而高光谱遥感技术的兴起则大大加快了遥感反演技术从定性到定量发展的步伐^[4] ...

2012

0.0

... 对LAI的反演方法主要有物理模型法和经验模型法^[5,6] ...

2011

0.0

... 对LAI的反演方法主要有物理模型法和经验模型法^[5,6] ...

2011

0.0

杨贵军, 赵春江, 邢著荣, 等. 基于PROBA/CHRIS遥感数据和PROSAIL模型的春小麦LAI反演[J]. 农业工程学报, 2011, 27(10): 88-94.

Yang G

, Zhao C

, Xing Z

, et al. LAI inversion of spring wheat based on PROBA/CHRIS hyperspectral Multi-angular data and PROSAIL mode[J]. Transactions of the CSAE, 2011, 27(10): 88-94.

Leaf area index (LAI) is an important parameter of vegetation ecosystems, which can represent the growth situation of vegetation. The PROBA(project for onboard autonomy)/CHRIS（compact high resolution imaging spectrometer）data acquired in June 4, 2008 was used to inverse LAI of spring wheat combing with the radiative transfer model (PROSAIL) and ANN(artificial neural network), and to validate the results according to the in-situ measurements. The optimal bands were selected using segmented principal component analysis. Three bands(center wavelength 551.1 nm、696.9 nm and 871.5 nm, respectively) were finally used to inversion of LAI. The selected combination of three observation angles (0°, 36° and 55°) shows high accuracy inversion LAI with R2=0.854，RMSE=0.344, MAE=0.213. The accuracy of inversion LAI can be improved with increasing the number of observation angle. However, if the number of angles is more than three, the accuracy will conversely decrease because of the uncertainty augment of multi-angle data.

大面积区域作物叶面积指数（LAI）遥感反演，对指导作物管理具有重要的意义。该文基于2008年5－7月在黑河流域开展的大型星－机－地遥感综合试验获取的多角度高光谱PROBA/CHRIS数据及地面同步观测数据，利用PROSAIL辐射传输模型和神经元网络方法反演春小麦LAI，并利用地面实测LAI进行验证和分析，结果表明：PROBA/CHRIS数据的最佳组合波段为band 4（555.1 nm）、band 9（696.9 nm）和band15（871.5 nm），利用PROBA/CHRIS数据反演LAI时，3角度组合（0°、36°、55°）反演LAI精度最高（R2=0.854，RMSE=0.344；MAE=0.213）。随着观测角度增加LAI反演精度相应提高，但超过3个角度后，多观测角度数据会带来较大不确定性，影响神经元网络建模，导致LAI反演精度下降。

... 物理模型法用以物理方法为基础的植被冠层反射率模型进行LAI反演^[7],能建立植被生理生化参数与冠层反射率的联系,但模型的解并不唯一^[8] ...

2011

0.0

... 物理模型法用以物理方法为基础的植被冠层反射率模型进行LAI反演^[7],能建立植被生理生化参数与冠层反射率的联系,但模型的解并不唯一^[8] ...

2006

0.0

... 经验模型法则是指建立LAI与植被特征之间的回归模型,但模型受限于饱和效应^[9] ...

2012

0.0

... 高光谱遥感数据具有波段窄且数量多的特点,相比多光谱数据,与LAI具有更高的相关性^[10] ...

2006

0.0

... 农业遥感中,利用高光谱数据及其建立的植被指数能准确地反映出作物本身的光谱特征,并更方便地获取农学信息^[11] ...

1995

0.0

... 目前,高光谱的导数光谱技术(differential spectroscopy)及红边参数也越来越多地被用于反演LAI^[12] ...

2008

0.0

刘东升, 李淑敏. 北京地区冬小麦冠层光谱数据与叶面积指数统计关系研究[J]. 国土资源遥感, 2008, 20(4): 32-34, 42. doi: DOI: 106046/gtzyyg. 2008. 04. 08.

Liu D

, Li S

. Statistical relationship between LAI indices and canopy spectral data of winter wheat in Beijing area[J]. Remote Sensing for Land and Resources, 2008, 20(4): 32-34, 42. doi: DOI:10.6046/gtzyyg.2008.04.08.

Fundamental knowledge is necessary for establishing a statistical model between LAI and vegetable indices to perform the inversion of LAI with remotely sensed data. The authors processed the canopy spectral data of winter wheat in Beijing area obtained by ASD Spectrometer with spectral response function of the TM sensor，and got the canopy NDVI of the wheat. Two different kinds of non-linear statistical data of LAI and NDVI were constructed in this paper. The experimental results show that there exists a close exponent correlation between LAI and NDVI.

1. College of Information and Electrical Engineering, China Agriculture University，Beijing 100083, China；2. Institute of Agricultural Integrated Development, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China

以北京地区冬小麦为研究对象，利用TM传感器的光谱响应函数处理地面测量获得的冬小麦冠层光谱数据，得到对应于TM传感器红光波段和近红外波段的反射率，进而计算出冬小麦冠层的归一化植被指数NDVI。建立了LAI与NDVI之间的不同经验关系模型，对实验结果进行分析后得出，LAI与NDVI之间具有高度的指数相关性。

... 利用实测光谱模拟MODIS等效反射率,构建植被指数及其与小麦LAI之间的经验模型^[13],可为利用遥感技术进行大面积作物信息提取提供重要手段^[14] ...

2012

0.0

侯学会, 牛铮, 黄妮, 等. 小麦生物量和真实叶面积指数的高光谱遥感估算模型[J]. 国土资源遥感, 2012, 24(4): 30-35. doi: DOI: 106046/gtzyyg. 2012. 04. 06.

Hou X

, Niu

, Huang

, et al. The hyperspectral remote sensing estimation models of total biomass and true LAI of wheat[J]. Remote Sensing for Land and Resources, 2012, 24(4): 30-35. doi: DOI:10.6046/gtzyyg.2012.04.06.

Based on the spectral first order differential techniques and equivalent MODIS vegetation index according to spectral response function using the field sampling survey, this paper built the hyperspectral remote sensing estimation models for total biomass and LAI of wheat named Jingdong 12. The research shows that: 1the largest correlation between total dry biomass and canopy spectra lies at 552 nm and 721 nm, and the relationship between LAI and canopy spectral is significant in the band range of 400~1 100 nm; 2the relationship between red edge position (REP) and wheat biomass is most significant, with R being 0.818; 3in the 6 vegetation indices similarly to MODIS-VI, EVI is most sensitive to total dry biomass; 4the linear model using REP estimation biomass is the best,with R 2 being 0.669 4. Exponential model between LAI and EVI has the strongest fitting degree, with R 2 being 0.94. Using field spectral data and band response function to retrieve wheat parameters can provide important research methods for making use of satellite remote sensing data characterized by large area, non-destruction and acquisition of timely ground vegetation information.

1. The State Key Laboratory of Remote Sensing Science, Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China; 2. Graduate University of Chinese Academy of Science, Beijing 100049, China

利用大田小麦的参数数据和冠层光谱数据,基于光谱一阶微分技术和光谱响应函数,构建等效MODIS植被指数,建立小麦生物量(本文指总干生物量,下同)和真实叶面积指数的高光谱遥感估算模型。结果表明: 1小麦生物量与冠层光谱在552 nm,721 nm处呈现最显著相关关系,叶面积指数与冠层光谱的相关性在400~1 100 nm范围内较显著; 2红边位置与生物量的关系最为显著,相关系数 R 为0.818; 36种等效MODIS植被指数中,增强型植被指数对生物量最为敏感; 4红边位置估算小麦总生物量的指数模型最优,决定系数 R 2 为0.829; 5增强型植被指数与小麦叶面积指数的指数模型拟合度最强,决定系数 R 2 为0.94。利用实测光谱模拟MODIS等效反射率构建植被指数反演小麦参数的方法,可为利用卫星数据进行大面积、无破坏和及时获取地面植被信息研究提供重要手段。

... 利用实测光谱模拟MODIS等效反射率,构建植被指数及其与小麦LAI之间的经验模型^[13],可为利用遥感技术进行大面积作物信息提取提供重要手段^[14] ...

2004

0.0

王秀珍, 黄敬峰, 李云梅, 等. 水稻叶面积指数的高光谱遥感估算模型[J]. 遥感学报, 2004, 8(1): 81-88.

Wang X

, Huang J

, Li Y

, et al. The study on hyperspectral remote sensing estimation models about LAI of rice[J]. Journal of Remote Sensing, 2004, 8(1): 81-88.

This study is based on the rank difference of the nitrogenous nutrition level by the man made style through two years rice farm experiment about the difference of the nitrogenous nitrition level. Using linear and non linear and stepwise multiple regression methods, the estimation models about LAI of rice have been built on the basis of the experimental data in 1999 which where evaluatde and validated with experimental data of the year 2000 as training samples. The results show that there are the ralationships between the characteristic variables of hyperspectra and LAI . From the results of precision analysis, the models that the vegetation indices variables of sum of the 1st derivative value within red edge ( SDr) and the blue edge ( SDb) are the best one of estimating LAI of Rice by hyperspectral remote sensing data.

通过不同氮素营养水平的水稻田间试验 ,采用单变量线性与非线性拟合模型和逐步回归分析 ,用1 999年试验数据为训练样本 ,建立水稻LAI的高光谱遥感估算模型 ,用 2 0 0 0年试验数据作为测试样本数据 ,对其精度进行评价和验证。结果表明 ,高光谱变量与LAI之间的拟合分析中 ,蓝边内一阶微分的总和与红边内一阶微分的总和的比值和归一化差植被指数是最佳的变量

... 王秀珍等^[15]通过水稻高光谱数据的各种变换形式表明,以红边面积和蓝边面积为变量的模型与水稻LAI关系密切 ...

... 利用小麦高光谱数据构建的各类常见植被指数(表1)和三边变量参数(表2)^[15,22,23],能够很好地反映出小麦在各生育期中的植被特征 ...

1998

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... 由于高光谱数据存在着采样间隔的离散型,故导数光谱需用差分法来近似计算^[16],即 ...

1969

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1973

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1995

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1977

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1994

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2002

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王秀珍, 王人潮, 黄敬峰. 微分光谱遥感及其在水稻农学参数测定上的应用研究[J]. 农业工程学报, 2002, 18(1): 9-13.

Wang X

, Wang R

, Huang J

. Derivative spectrum remote sensing and Its application in measurement of rice agronomic parameters of rice[J]. Transactions of the CSAE, 2002, 18(1): 9-13.

The objective of this paper is to analyze the elimination of background signals(such as wet soil, water and so on) using derivative canopy spectra of rice under different nitrogen status. Application of this technique for tackling analogous problems such as interference from soil background reflectance in remote sensing is proposed. Potential areas for the application of this technique in remote sensing are considered. The optimum spectral bandwidth for smoothing is less than 10 nm. When the derivatives are applied to determine the agronomic parameters, there is a shift phenomenon of the red edge. The red edge positions move to longer wave bands till booting stage and move to shorter bands after booting stage with nitrogen increasing. The red edge parameters in the first derivative reflectance curve (wavelength, amplitude and area of the red edge peak) were studied to evaluate rice leaf chlorophyll, LAI. A high correlation was found between chlorophyll A content of top leaves and the wavelength of the red edge position and between LAI and the red edge parameter. Then, the red edge was proved to be valuable for assessment of rice upper leaves chlorophyll contents. But a correlation was not found between chlorophyll B content of leaves or carotenoid and the wavelength of the red parameters. Some red edge parameters are one of the best remote sensing descriptors. The feasibility of using derivative spectra to measure some agronomic parameters is verified.

通过不同氮素营养水平的水稻田间试验,解析了水稻冠层微分光谱对消除背景(湿土、水面)信息的影响,证实微分光谱在消除背景信息的影响方面起到了很好的作用。用微分光谱确定出最优的波段宽度应小于10nm;将微分光谱应用于农学参数测定,存在红边位移现象,在孕穗期之前,红边随施氮量增加向长波方向移动“红移”;孕穗期之后“红移”现象基本消失,而发生“蓝移”。红边参数(红边、红边振幅、红边振幅与最小振幅的比值、红边峰值面积)与上层叶片的叶绿素含量、LAI有着密切的关系,而与叶片中的叶绿素b、类胡萝卜素之间相关性不明显。一些红边参数可作为水稻叶绿素含量、LAI测定的简捷方法。从而证实了利用微分光谱测定一些农学参数的可行性

... 利用小麦高光谱数据构建的各类常见植被指数(表1)和三边变量参数(表2)^[15,22,23],能够很好地反映出小麦在各生育期中的植被特征 ...

2001

0.0

谭倩, 赵永超, 童庆禧, 等. 植被光谱维特征提取模型[J]. 遥感信息, 2001(1): 14-18.

Tan

, Zhao Y

, Tong Q

, et al. Vegetation spectral feature extraction model[J]. Remote Sensing Information, 2001(1): 14-18.

提出了一种针对植被的光谱维特征提取模型植被光谱特征提取模型（Vegetation Spectral Feature Extraction Model,缩写为VSFEM）。该模型通过分析大量野外植被高光谱曲线，选择了8个光谱维特征位置，并由它们衍生出了一系列特征参量。

... 利用小麦高光谱数据构建的各类常见植被指数(表1)和三边变量参数(表2)^[15,22,23],能够很好地反映出小麦在各生育期中的植被特征 ...

1995

0.0

... 3 交叉验证法交叉验证法主要用于选择模型,在统计学上是将数据样本切割为小子集,通过对各子集之间的分析与验证确立模型类型^[24] ...

2013

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... 特别是在数据量不足的情况下,数据的重复利用能在模型选择方面显示出更多的优越性^[25] ...

2008

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陈拉, 黄敬峰, 王秀珍. 不同传感器的模拟植被指数对水稻叶面积指数的估测精度和敏感性分析[J]. 遥感学报, 2008, 12(1): 143-151.

Chen

, Huang J

, Wang X

. Estimating accuracies and sensitivity analysis of regression models fitted by simulated vegetation indices of different sensors to rice LAI[J]. Journal of Remote Sensing, 2008, 12(1): 143-151.

In this study,vegetation indices(VI) were firstly calculated from the simulated band reflectance spectra of NOAA11-AVHRR,AQUA-MODIS and LANDSAT5-TM using rice canopy hyperspectral reflectance spectra,which were acquired in 2002 and 2004.Regression models for estimating LAI were fitted by that of 2002 and validated by the VIs data sets of 2004.Estimating accuracies between models were compared and analysis of VIs' sensitivity to changes in LAI was performed.Due to differences in spectral response function,the simulated red band reflectance of AVHRR was higher,whereas near infrared reflectance was lower than that of TM or MODIS for identical rice canopy.Compared with the models of VIs calculated from the red band reflectances,LAI showed closer linear relationship in models of ratio VIs from the red-edge or green band reflectances and closer exponential relationship in models of normalized VIs from the green band reflectances.Among others,the model using Red-edge Ratio Vegetation Index(Red-edge RVI) showed the highest estimating accuracy not only for calibration but also the highest one for external validation,furthermore the decrease in estimating accuracy between calibration and external validation was lowest.The models of Green Normalized Difference Vegetation Index(GNDVI) and Green Ratio Vegetation Index(GRVI) held the second highest position of estimating accuracies for external validation,the models of normalized difference vegetation index(NDVI) and enhanced vegetation index(EVI) were the third highest and those of RVI were the worst.Although AVHRR bands have broader ranges of spectral response than TM and MODIS,their NDVI and RVI models exhibited higher estimating accuracies for rice LAI in this study.The sensitivities of all indices decreased with the increase of LAI values,but difference of sensitivities between normalized VIs and ratio VIs was significant.The normalized VIs exhibited much higher sensitivities with rapidly decreased rate than the ratio VIs at LA1 values of less than 1.2,while the ratio VIs did higher sensitivities than the normalized VIs at LA1 values of more than 1.2.Red-edge RVI and GRVI exhibited not only higher linearity relationship with LAI but also higher sensitivities at LAI values of more than 2.8.When comparison of model fitness between linear and nonlinear regression models is made,it should be done not by directly comparing the coefficients of determination of regression models but the coefficients of determination of linearity relationship between the predicted and the observed.

本研究利用水稻冠层高光谱数据,模拟NOAA-AVHRR,Terra-MODIS和Landsat-TM的可见光波段反射率数据,计算各传感器的多种植被指数(NDVI,RVI,EVI,GNDVI,GRVI和Red-edge RVI),比较植被指数模型对水稻LAI的估测精度,分析不同植被指数对LAI变化的敏感性.相对于红波段植被指数,红边比值植被指数(Red-edge RVI)和绿波段指数GRVI与LAI有更好的线性相关关系,而GNDVI和LAI呈现更好的对数相关关系.MODIS的Red-edge RVI指数不仅模型拟合的精度最高,还有独立数据验证的估测精度也最高,而且它的验证精度较拟合精度下降幅度最小;其次是绿波段构建的GNDVI和GRVI植被指数的估测精度,再次是NDVI和EVI的估测精度,而RVI的估测精度最差.敏感性分析发现,13个植被指数对水稻LAI的估测能力都随着LAI的增加而下降,但归一化类植被指数和比值类植被指数对LAI变化反应的差异明显,归一化类植被指数在LAI较低时(LAI 1.5)比值类植被指数对LAI的变化敏感性,则明显高于归一化类植被指数.Red-edge RVI和绿波段指数GRVI和LAI不仅表现了很好的线性相关关系,而且在LAI大于2.9左右保持较高的敏感性.

... 由于R²是模型估算值与真实值之间的曲线相关关系的反映^[26],因此RMSE成为本次研究中更重要的判断指标 ...

2013

0.0

权文婷, 王钊. 冬小麦种植面积遥感提取方法研究[J]. 国土资源遥感, 2013, 25(4): 8-15. doi: DOI: 106046/gtzyyg. 2013. 04. 02.

Quan W

, Wang

. Researches on the extraction of winter wheat planting area using remote sensing method[J]. Remote Sensing for Land and Resources, 2013, 25(4): 8-15. doi: DOI:10.6046/gtzyyg.2013.04.02.

Currently,the extraction of winter wheat area has become the key point of winter wheat growth monitoring and yield estimation. Therefore,how to improve the accuracy of winter wheat planting area retrieval is one of the important topics in agricultural remote sensing at present and in the future. The retrieval model should be varying with the spatial resolution of satellite image so as to accurately retrieve winter wheat planting area from remote sensing images,and this is one of the important points of remote sensing of winter wheat planting area. In this study,the winter wheat planting area retrieval models are grouped into 5 different types, i.e., visual interpretation and computer automatic classification,classification based on pixel,classification based on parcel,remote sensing interpretative model classification and classification based on texture. The main research methods were described and evaluated,and the problems existent in current research work and the future development of remote sensing method for estimation winter wheat planting area were discussed. The focusing of the attention on the estimation of winter wheat area in the district with complex terrain,high land fragmentation and complex planting structure,the combination of remote sensing data with GIS data and meteorological data, the integration of multi-source remote sensing data with multi-temporal remote sensing data,the combination of ground spectral measurement data with high spectral data,and the changing of the validation data constitute the study direction for improving the accuracy of winter wheat planting area retrieval.

Shaanxi Provincial Agricultural Remote Sensing Information Center, Xi'an 710014, China

提取冬小麦种植面积是开展冬小麦长势监测和估产工作的重要环节,如何提高其提取精度是国内外研究的热点。针对不同空间分辨率的遥感图像,采取不同的遥感解译模型,得到更高的信息提取精度,是利用遥感方法提取冬小麦种植面积的关键。在对国内、外调研的基础上,将冬小麦种植面积遥感提取研究方法归纳为目视解译与计算机自动分类、面向像元分类、基于地块分类、遥感解译模型分类和基于纹理特征分类等5类。综述评价了主要的冬小麦种植面积遥感提取研究方法,讨论了目前冬小麦种植面积遥感提取存在的问题及未来发展方向。关注地形复杂、耕地破碎度较高及种植结构复杂地区的冬小麦种植面积提取,遥感数据、GIS和气象数据等相结合、多源多时相遥感数据相结合、地面光谱测量数据与高光谱图像数据相结合,以及验证数据的改变等,是提高冬小麦种植面积遥感提取精度的研究方向。

... 验证中利用了覆盖山东省小麦种植区的MODIS-L1B及MODIS-LAI数据,利用MODIS-L1B的近红外和红光波段构建了NDVI时间序列,通过阈值法^[27]提取山东省小麦种植区域,并运用这2个波段构建RVI立方回归模型以提取山东省各地区小麦LAI信息,与同期的MODIS-LAI数据进行比对 ...