利用PROSPECT+SAIL模型反演植物生化参数的植被指数优化模拟

分析3个植被生化参数(叶绿素含量、叶片含水量和叶面积指数)对冠层光谱反射率变化的敏感程度以及影响波段区间,选择3个植被指数作为代价函数的优化比较对象,然后运用微粒群算法和PROSPECT+SAIL模型分别反演叶绿素含量、叶片含水量和叶面积指数,结果表明:基于植被指数作为优化比较对象的模型反演效率较全波段方法有所提高;叶绿素含量、叶片含水量和叶面积指数反演值与实测值的复相关系数分别为90.8％、95.7％和99.7％,均方根误差分别为4.73μg·cm-2、0.001 g·cm-2和0.08.采用植被指数作为优化比较对象可有效地提高基于PROSPECT+SAIL模型反演植被生化参数的精度和效率.     

PROSPECT5耦合4SAIL模型的亚热带典型森林冠层反射率时间序列模拟

冠层反射率在森林植被类型精确解译、森林碳同化关键参数如叶面积指数(LAI)、叶绿素等遥感反演等方面具有重要意义.本研究以亚热带毛竹林、雷竹林和常绿落叶阔叶混交林3种典型森林类型为研究对象,通过耦合PROSPECT5和4SAIL模型模拟其冠层反射率时间序列.首先,对PROSPECT5和4SAIL模型参数进行敏感性分析,探讨模型参数对冠层反射率的影响;其次,利用实测反射率对不敏感参数进行优化,并确定其参数值;最后,耦合PROSPECT5和4SAIL模型模拟3种亚热带森林冠层反射率,并与MODIS反射率进行对比.结果表明:LAI对第1、2、3、5、7波段最敏感,各波段的总敏感指数分别为0.80、0.83、0.94、0.66、0.47;叶绿素含量对第4波段最敏感,总敏感指数为0.59;叶片含水量对第6波段的敏感性最大,总敏感性指数为0.54;叶子结构参数、类胡萝卜素、热点参数、干物质含量和土壤干湿比等参数对各个波段都不敏感或敏感性较小.优化后的PROSPECT5和4SAIL模型模拟得到的冠层反射率能够真实反映3种典型森林的季节性变化规律,通过与MODIS反射率对比分析发现,模拟冠层反射率和MODIS反射率之间具有较高的决定系数,分别为0.86、0.90、0.93,均方根误差(RMSE)也较小,分别为0.09、0.07、0.05,且模拟反射率能在一定程度上解决MODIS反射率数据冬季易受雨雪、混合像元影响等问题.     

基于PROSAIL辐射传输模型的毛竹林分冠层反射率模拟研究

冠层光谱反射率直接关系到毛竹（Phyllostachys pubescens Mazel）林冠层参数的反演,对毛竹林地土壤肥力间接估测具有重要意义。以PROSPECT5、PROSAIL模型为基础,从叶片尺度和冠层尺度分析模型参数对叶片和冠层反射率的影响,构建毛竹冠层叶面积指数（LAI）-冠层反射率查找表并通过代价函数选取最优冠层反射率,从而实现毛竹林分冠层反射率的准确模拟。结果表明,在叶片尺度,PROSPECT模型参数敏感性从高到低依次为叶肉结构参数（N） > 叶绿素含量（C_ab） > 等效水厚度（EWT） > 干物质含量（C_m） > 类胡萝卜素含量（C_ar）;在冠层尺度,PROSAIL模型参数敏感性从高到低依次为LAI > C_ab > EWT > C_m > N > C_ar > ALA（平均叶倾角）;叶片尺度反射率整体大于冠层尺度反射率;在400~900 nm波长范围内,PROSAIL模型模拟的冠层光谱反射率与实测光谱反射率拟合效果较好,相对误差为6.71%。     

基于PROSAIL辐射传输模型的毛竹林叶面积旨数遥感反演

采用PROSAIL辐射传输模型建立毛竹林叶面积指数(LAI)-冠层反射率查找表,并结合Landsat TM卫星遥感数据,实现了毛竹林LAI的定量反演.结果表明:PROSAIL模型各输入参数的敏感性由高到低依次为LAI＞叶绿素含量(Cab)＞叶片结构参数(N)＞平均叶倾角(ALA)＞等效水厚度(Cw)＞干物质含量(Cm),并以LAI、Cab两个主要敏感因子用于构建毛竹林LAI-冠层反射率查找表;基于PROSAIL模型的毛竹林LAI遥感反演结果与实测LAI具有很好的一致性,二者相关系数为0.90,均方根误差和相关的均方根误差也较小,分别为0.58和13.0％,但也存在反演LAI平均值高于实际值的问题.     

基于PROSAIL辐射传输模型的毛竹林叶面积指数遥感反演

采用PROSAIL辐射传输模型建立毛竹林叶面积指数(LAI) 冠层反射率查找表,并结合Landsat TM卫星遥感数据,实现了毛竹林LAI的定量反演.结果表明： PROSAIL模型各输入参数的敏感性由高到低依次为LAI>叶绿素含量(C_ab)>叶片结构参数(N)>平均叶倾角(ALA)>等效水厚度(C_w)>干物质含量(C_m),并以LAI、C_ab两个主要敏感因子用于构建毛竹林LAI 冠层反射率查找表;基于PROSAIL模型的毛竹林LAI遥感反演结果与实测LAI具有很好的一致性,二者相关系数为0.90,均方根误差和相关的均方根误差也较小,分别为0.58和13.0%,但也存在反演LAI平均值高于实际值的问题.     

遥感反演植被理化参数的光谱和空间尺度效应

植被理化参数是生态系统中碳和养分等物质循环与能量交换的重要指标,利用遥感技术反演是获取区域及全球植被理化参数的重要手段,但光谱和空间尺度效应的存在,限制了源自不同遥感传感器植被理化参数产品的统一应用。阐述了遥感反演植被理化参数光谱尺度效应的概念及其产生原因,主要从光谱波段位置和波段宽度两方面对国内外相关研究进行了介绍和评述。同时,从遥感反演植被理化参数的空间尺度效应产生原因、空间异质性描述方法和空间尺度转换方法等方面对其国内外研究现状进行了归纳和评述。最后,总结了遥感反演植被理化参数光谱和空间尺度效应研究的不足之处和发展趋势,并指出光谱和空间耦合效应的研究将是一大趋势,而在生态学等领域形成的尺度效应研究的理论和方法也值得借鉴参考。     

4-Scale几何光学模型冠层反射率模拟的空间尺度适用性

明确4-Scale模型模拟森林冠层反射率适用的空间尺度,有助于提高其应用于不同植被类型冠层反射率模拟时的精度,进而提升其开展叶面积指数、郁闭度和其他参数的反演精度。以黑龙江省尚志市帽儿山实验林场2块100 m×100 m森林样地(阔叶林与混交林各一块)为研究对象,分别分割为10、20、30、40和50 m空间尺度,使用4-Scale模型模拟森林冠层反射率,采用局部平均法、最邻近法、双线性内插法和立方卷积法对空间分辨率为10 m的Sentinel-2影像升尺度转换至其他尺度并评价,对比分析模拟冠层反射率和遥感像元反射率,明确混交林和阔叶林适合4-Scale模型高精度反演参数的空间尺度。结果表明：4-Scale模型整体低估了像元森林冠层反射率,混交林和阔叶林冠层反射率在20 m尺度的模拟效果均最差,红光波段和近红外波段的均方根误差(RMSE)和平均绝对偏差(MAE)均较大;>20 m尺度的模拟效果开始变好,混交林40 m、阔叶林30 m时模型的适用性最佳,红光波段和近红外波段下,模拟值与遥感像元反射率之差的均值和标准差最小,RMSE和MAE同样最小;10 m尺度混交林和阔叶林模拟结果...     

落叶阔叶林冠层光合有效辐射分量的遥感模拟与分析

冠层绿色叶片(光合组分)的光合有效辐射分量(绿色FPAR)真实地反映了植被与外界进行物质和能量交换的能力,获取冠层光合组分吸收的太阳光合有效辐射,对生态系统生产力的遥感估算精度的提高具有重要的意义。研究以落叶阔叶林为例,基于SAIL模型模拟森林冠层光合组分和非光合组分吸收的光合有效辐射,研究冠层FPAR变化规律以及与植被指数的相关关系。结果表明,冠层结构的改变会影响冠层对PAR的吸收能力,冠层绿色FPAR的大小与植被面积指数及光合组分面积比相关;在高覆盖度植被区,冠层绿色FPAR占冠层总FPAR的80%以上,非光合组分的贡献较小,但在低植被覆盖区,当光合组分和非光合组分面积相同时,绿色FPAR不及冠层总FPAR的50%;相比于NDVI,北方落叶阔叶林冠层EVI与绿色FPAR存在更为显著的线性相关关系(R~20.99)。     

基于地-空遥感耦合的冬小麦叶片氮积累量估算

利用不同冬小麦生态区同步的SPOT-5多光谱遥感影像、地面光谱数据和植株取样数据,提出一种基于波谱响应函数拟合和混合像元分解的纯净像元光谱提取方法,并对比分析了纯净像元光谱、模拟像元光谱和实测像元光谱与冬小麦叶片氮积累量（LNA）的定量关系.结果表明： 模拟像元光谱对叶片氮积累量的反演效果较好,纯净像元光谱反演效果次之,实测像元光谱最差;但基于模拟像元光谱的LNA监测模型不能直接外推至空间尺度.模型检验结果表明,基于纯净像元光谱的LNA监测模型在2个小麦生态区均具有较好的精度和稳定性,该方法综合利用了地-空遥感的优点,可以推广应用到其他不同空间分辨率和光谱分辨率的遥感数据,从而为区域性冬小麦氮素营养状况的遥感监测提供技术依据.     

滨海湿地植被地上生物量遥感反演

盐沼植被生物量是滨海湿地生态系统碳循环研究的重要参数,是湿地生态系统健康评价、资源可持续利用的关键指标,开展盐沼植被地上生物量监测方法研究具有重要意义。目前,遥感技术在湿地生物量监测领域已经得到广泛应用,但反演方法仍以统计模型为主,模型构建需要实测数据支撑,时空拓展性不强。选择江苏盐城丹顶鹤保护区为研究区,基于冠层辐射(PROSAIL)传输模型,通过局部和全局敏感性分析,对模型参数本地化,构建了互花米草地上生物量半经验反演模型,应用于Landsat 8 OLI遥感影像,获得了互花米草地上生物量的时空分布。研究结果表明,利用PROSAIL模型模拟互花米草冠层反射率,叶面积指数(LAI)、叶片干物质含量(Cm)、叶倾角分布参数(LIDF)、等效水厚度(Cw)、叶绿素含量(Cab)、叶片结构参数(N)为高敏感性参数,类胡萝卜素含量(Car)、土壤参数(P_soil)为低敏感性参数;利用不同时刻的遥感影像反演了地上生物量,遥感反演结果与实测数据对比,拟合度R²为0.83,均方根误差(RMSE)为0.43kg/m²,平均相对误差(MRE)为15.7%,精度较高,模型具有较好的时空普适性。研究发展了盐沼植被地上生物量遥感反演方法,解决了以往过于依赖现场实测数据构建反演模型的局限性,该方法可以为研究滨海湿地生态系统碳循环以及准确估算其碳汇潜力提供技术支持。     

基于遥感的光合有效辐射吸收比率(FPAR) 估算方法综述

光合有效辐射吸收比率(FPAR)是反映植被生长过程的重要生理参数,是陆地生态系统模型的关键参数,是反映全球气候变化的重要因子。基于遥感的FPAR估算方法是获取区域乃至全球尺度FPAR的有效方法。目前,主要形成了植被指数法和机理法两类方法,植被指数法是建立FPAR与植被指数的经验统计模型,简单、计算效率高;机理法则从物理模型上进行FPAR的求解与反演,机理明晰、可行性强。然而,由于FPAR本身的复杂性以及环境因素、遥感数据质量的影响,导致了估算方法面临诸多不确定性问题。为了解决这些不确定性问题以及满足生态过程深入研究的需求,将进一步注重FPAR的机理研究、先验知识的获取与积累,构建长时间序列FPAR以及高时空的FPAR算法研究。     

Developing and validating novel hyperspectral indices for leaf area index estimation: Effect of canopy vertical heterogeneity

Leaf area index (LAI) is one of the key biophysical parameters for understanding land surface photosynthesis, transpiration, and energy balance processes. Estimation of LAI from remote sensing data has been a premier method for a large scale in recent years. Recent studies have revealed that the within-canopy vertical variations in LAI and biochemical properties greatly affect canopy reflectance and significantly complicate the retrieval of LAI inversely from reflectance based vegetation indices, which has yet been explicitly addressed. In this study, we have used both simulated datasets (dataset I with constant vertical profiles of LAI and biochemical properties, dataset II with varied vertical profile of LAI but constant vertical biochemical properties, and dataset III with both varied vertical profiles) generated from the multiple-layer canopy radiative transfer model (MRTM) and a ground-measured dataset to identify robust spectral indices that are insensitive to such within canopy vertical variations for LAI prediction. The results clearly indicated that published indices such as normalized difference vegetation index (NDVI) had obvious discrepancies when applied to canopies with different vertical variations, while the new indices identified in this study performed much better. The best index for estimating canopy LAI under various conditions was D(920,1080), with overall RMSEs of 0.62–0.96 m²/m² and biases of 0.42–0.55 m²/m² for all three simulated datasets and an RMSE of 1.22 m²/m² with the field-measured dataset, although it was not the most conservative one among all new indices identified. This index responded mostly to the quantity of LAI but was insensitive to within-canopy variations, allowing it to aid the retrieval LAI from remote sensing data without prior information of within-canopy vertical variations of LAI and biochemical properties.     

Hyperspectral indices based on first derivative spectra closely trace canopy transpiration in a desert plant

A clear understanding of transpiration of arid ecosystems and its underlying mechanisms is critical for accurate prediction of long-term water and energy fluxes in such ecosystems, which have gradually been recognized to play major roles on a global scale. Unlike traditional measurements of transpiration that are generally time-consuming, expensive, and often unfeasible, remote sensing techniques such as hyperspectral indices are widely utilized as the only approach to obtain such information on a large scale. However, compared with other biochemical and biophysical parameters, few studies on hyperspectral indices have been applied to estimate canopy transpiration. In this study, we focused on a native dominant plant in the arid land of central Asia, Haloxylon ammodendron, to explore the featured spectra and to develop proper hyperspectral indices for estimating transpiration. This was based on a simultaneous dataset of original canopy-reflectance spectra as well as its first derivatives with transpiration estimated from sap flow. The results indicated that the derivative spectra-based indices are more effective for tracing canopy transpiration compared with its counterpart that was based on the original reflectance. The identified best index for estimating canopy transpiration was dSR(660,1040) based on the first-derivative spectra, which had a coefficient of determination (R²) of 0.54. The index is also relatively stable concerning spectral resolutions. Results obtained in this study should help lay the basis for using remote sensing data to estimate transpiration.     

叶冠尺度野鸭湖湿地植物群落含水量的高光谱估算模型

利用高光谱遥感技术定量估测野鸭湖湿地植被含水量,对于监测和诊断野鸭湖湿地植被的生理状况及生长趋势具有重要意义,也能够为高光谱遥感影像在野鸭湖湿地植被含水量诊断中的实际应用提供理论依据和技术支持.采用Field Spec 3野外高光谱辐射仪,获取了野鸭湖典型湿地植被冠层和叶片的光谱,并测定了对应的含水量.以上述实测数据为基础,首先以芦苇为例初步探明了不同含水量水平下典型湿地植被冠层和叶片光谱反射率的响应模式,然后采用相关性及单变量线性与非线性拟合分析技术,从冠层和叶片两种层次,对不同尺度下的含水量与“三边”参数及高光谱植被指数进行了分析拟合,并采用交叉检验中的3K-CV方法对估算模型进行了测试和检验,确立了不同尺度下野鸭湖湿地植被含水量的定量监测模型.结果表明:(1)随着含水量水平的增加,芦苇冠层与叶片光谱在可见光波段(350-760 nm)和红外波段(760-2500 nm)的反射率均呈逐渐降低趋势.(2)不同尺度含水量与选取的光谱特征参数整体上相关性较强,与“三边”参数基本上都呈极显著相关,相关系数最大达到0.906;与高光谱指数全部呈极显著相关,相关系数最小为0.455,最大达到0.919,并通过选取不同尺度上相关性最佳的光谱特征参数,分别基于“三边”参数和高光谱植被指数构建了不同尺度下的含水量估算模型.其中,冠层尺度下,黄边面积(SDy)与SRWI( Simple Ratio Water Index)的估算效果最好,估算模型分别为y=-9.462x2 -2.671x+0.608和y=0.219e1.010x;叶片尺度下,红边面积(SDr)与WI( Water Index)的估算效果最好,估算模型分别为y=0.562x+0.376和y=2.028x2 -0.476x-1.009.通过3K-CV的交叉验证,不同尺度下的含水量估算模型均取得了较为理想的预测精度,预测精度的最小值为94.92％,最大值为97.06％,表明估测模型具有较高的可靠性与普适性.(3)高光谱植被指数与含水量拟合方程的拟合度相对高于“三边”参数与之建立方程的拟合度,说明多波段组合的光谱特征参数更适合含水量的判别.     

基于HJ1B和ALOS/PALSAR数据的森林地上生物量遥感估算

森林地上生物量的精确估算能够减小碳储量估算的不确定性。为了探寻一种有效地提高森林生物量估算精度的方法,探讨了基于遥感物理模型和经验统计模型估算山地森林地上生物量的方法。首先,基于Li-Strahler几何光学模型和多元前向模式(MFM)进行模型模拟,结合查找表算法(LUT)从多光谱图像HJ1B估算贺兰山研究区的森林地上生物量。其次,采用统计方法建立了2种回归模型:(1)多光谱图像HJ1B进行混合像元分解(SMA),并与雷达图像ALOS/PALSAR进行图像融合建立生物量回归模型;(2)雷达图像ALOS/PALSAR后向散射系数和实测生物量建立了生物量回归模型。用实测数据对3种算法估算结果进行精度验证。研究结果表明:采用几何光学模型和MFM算法估算的森林地上生物量精度最好(决定系数R2=0.61,均方根误差RMSE=8.33 t/hm2,P0.001),其估算地上生物量与实测值一致性较好,估算生物量精度略优于SMA估算的精度(R2=0.60,RMSE=9.417 t/hm2);ALOS/PALSAR多元回归估算的精度最差(R2=0.39,RMSE=14.89 t/hm2)。由此可见,采用几何光学模型和混合像元分解SMA适合估算森林地上生物量,利用这2种方法进行森林地上生物量遥感监测研究具有一定的应用潜力。     

基于小波分析的大豆叶绿素a含量高光谱反演模型

 2003和2004年分别在长春市良种场和中国科学院海伦黑土生态实验站实测了大田耕作与水肥耦合作用下大豆（Glycine max）冠层高光谱反射率 与叶绿素a含量数据,对光谱反射率、微分光谱与叶绿素a含量进行了相关分析;采用归一化植被指数(Normalized diffe rence vegetation index, NDVI)、土壤调和植被指数(Soil-adjusted vegetation index, SAVI)、再归一植被指数(Renormalized difference vegetation index, RDVI)、第二修正比值植被指数(Modified second ratio index, MSRI)等建立了大豆叶绿素a反演模型;应用小波分析对采集的光谱反 射率数据进行了能量系数提取,并以小波能量系数作为自变量进行了单变量与多变量回归分析,对大豆叶绿素a进行了估算。研究结果表明,大 豆叶绿素a 与可见光光谱反射率相关性较好,并在红光波段取得最大值(R²>0.70),但在红边处,微分光谱与大豆叶绿素a的相关性较反射率好 得多,在其它波段则相反;由NDVI、SAVI、RDVI、MSRI等植被指数建立的估算模型可以提高大豆叶绿素a的估算精度（R²>0.75）;小波能量系 数回归模型可以进一步提高大豆叶绿素a含量的估算水平,以一个特定小波能量系数作为自变量的回归模型,大豆叶绿素a回归决定系数R²高达 0.78;多变量回归分析结果表明,大豆叶绿素a实测值与预测值的线性回归决定系数R²均高达0.85。以上结果表明, 小波分析可以对高光谱进 行特征变量提取,并可在一定程度上提高大豆生理参数反演精度。     

Hyperspectral remote sensing of plant pigments

The dynamics of pigment concentrations are diagnostic of a range of plant physiological properties and processes. This paper appraises the developing technologies and analytical methods for quantifying pigments non-destructively and repeatedly across a range of spatial scales using hyperspectral remote sensing. Progress in deriving predictive relationships between various characteristics and transforms of hyperspectral reflectance data are evaluated and the roles of leaf and canopy radiative transfer models are reviewed. Requirements are identified for more extensive intercomparisons of different approaches and for further work on the strategies for interpreting canopy scale data. The paper examines the prospects for extending research to the wider range of pigments in addition to chlorophyll, testing emerging methods of hyperspectral analysis and exploring the fusion of hyperspectral and LIDAR remote sensing. In spite of these opportunities for further development and the refinement of techniques, current evidence of an expanding range of applications in the ecophysiological, environmental, agricultural, and forestry sciences highlights the growing value of hyperspectral remote sensing of plant pigments.     

An analytical model for bidirectional reflectance factor of multicomponent vegetation canopies

Based on radiative transfer theory in vegetation and geometric-optical principles, an analytical physi-cal mode] for calculating multiangular, multispectral reflectance over a non-random, multiple component vegetation canopy is developed. This model is derived by taking advantages of the previous leaf canopy and multicomponent canopy BRF models. It quantitatively accounts for both the impact of foliage elements' orientation on the canopy hotspot through an innovative algorithm to estimate the hotspot function for any arbitrarily oriented foliage element and contributions of all foliage elements to the reflectance by multiple scattering. Thus, it is characterized by more com-pletely considering the integrative influence of spatial variations in optical and structural properties of all foliage ele-ments on canopy reflectance than any previous analytical BRF models. Simulation results from this model demonstrate that canopy hotspot becomes strongest when the mean inclination angle of foliage elements is ar     

Comparison of Vegetation Indices and Red-edge Parameters for Estimating Grassland Cover from Canopy Reflectance Data

There has been a great deal of Interests in the estimation of grassland biophysical parameters such as percentage of vegetation cover （PVC）, aboveground biomass, and leaf-area index with remote sensing data at the canopy scale. In this paper, the percentage of vegetation cover was estimated from vegetation indices using Moderate Resolution Imaging Spectroradiometer （MODIS） data and red-edge parameters through the first derivative spectrum from in situ hypserspectral reflectance data. Hyperspectral reflectance measurements were made on grasslands in Inner Mongolia, China, using an Analytical Spectral Devices spectroradiometer. Vegetation indices such as the difference, simple ratio, normalized difference, renormalized difference, soil-adjusted and modified soil-adjusted vegetation indices （DVI, RVI, NDVI, RDVI, SAVI L=0.5 end MSAVI2） were calculated from the hyperspectral reflectance of various vegetation covers. The percentage of vegetation cover was estimated using an unsupervised spectral-contextual classifier automatically. Relationships between percentage of vegetation cover and various vegetation indices and red-edge parameters were compared using a linear and second-order polynomial regression. Our analysis indicated that MSAVI2 and RVI yielded more accurate estimations for a wide range of vegetation cover than other vegetation indices and red-edge parameters for the linear and second-order polynomial regression, respectively.