基于模型模拟的植被NDVI与观测天顶角和LAI的关系

利用PROSPECT和SAIL模型模拟了不同叶绿素含量、不同LAI和不同观测天顶角下的植被冠层反射率,分析了NDVI随LAI、观测天顶角和叶绿素含量的变化规律。结果表明:叶绿素影响冠层反射率主要在可见光波段,冠层反射率随叶绿素含量的增加而下降;冠层反射率随观测天顶角的增加而增加,而LAI较高时,其受观测天顶角的影响较小。观测天顶角相同时,随叶绿素含量的增加NDVI呈上升趋势;叶绿素含量一定时,NDVI随LAI的增加而增加。LAI为1时,在不同叶绿素含量下,随观测天顶角的增加,NDVI呈先下降后上升的趋势,拐点在观测天顶角65°或70°处,而LAI为3、5和7时,NDVI呈现下降趋势。叶绿素含量较高时,NDVI受观测天顶角的影响较小。当LAI较大和叶绿素含量较低时,NDVI随观测天顶角的增加(>70°)下降较快。     

Non-destructive estimation of wheat leaf chlorophyll content from hyperspectral measurements through analytical model inversion

Optimizing nitrogen (N) fertilization in crop production by in-season measurements of crop N status may improve fertilizer N use efficiency. Hyperspectral measurements may be used to assess crop N status indirectly by estimating leaf and canopy chlorophyll content. This study evaluated the ability of the PROSAIL canopy-level reflectance model to predict leaf chlorophyll content of spring wheat (Triticum aestivum L.) during the growth stages between pre-tillering (Zadoks Growth Stage (ZGS 15)) to booting (ZGS50). Spring wheat was grown under different N fertility rates (0–200 kg N ha^?1) in 2002. Canopy reflectance, leaf chlorophyll content, N content and leaf area index (LAI) values were measured. There was a weakly significant trend for the PROSAIL model to over-estimate LAI and under-estimate leaf chlorophyll content. To compensate for this interdependency by the model, a canopy chlorophyll content parameter (the product of leaf chlorophyll content and LAI) was calculated. The estimation accuracy for canopy chlorophyll content was generally low earlier in the growing season. This failure of the PROSAIL model to estimate leaf and canopy variables could be attributed to model sensitivity to canopy architecture. Earlier in the growing season, full canopy closure was not yet achieved, resulting in a non-homogenous canopy and strong soil background interference. The canopy chlorophyll content parameter was predicted more accurately than leaf chlorophyll content alone at booting (ZGS 45). A strong relationship between canopy chlorophyll content and canopy N content at ZGS 45 indicates that the PROSAIL model may be used as a tool to predict wheat N status from canopy reflectance measurements at booting or later.     

Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland

Radiative transfer models have seldom been applied for studying heterogeneous grassland canopies. Here, the potential of radiative transfer modeling to predict LAI and leaf and canopy chlorophyll contents in a heterogeneous Mediterranean grassland is investigated. The widely used PROSAIL model was inverted with canopy spectral reflectance measurements by means of a look-up table (LUT). Canopy spectral measurements were acquired in the field using a GER 3700 spectroradiometer, along with simultaneous in situ measurements of LAI and leaf chlorophyll content. We tested the impact of using multiple solutions, stratification (according to species richness), and spectral subsetting on parameter retrieval. To assess the performance of the model inversion, the normalized RMSE and R² between independent in situ measurements and estimated parameters were used. Of the three investigated plant characteristics, canopy chlorophyll content was estimated with the highest accuracy (R² = 0.70, NRMSE = 0.18). Leaf chlorophyll content, on the other hand, could not be estimated with acceptable accuracy, while LAI was estimated with intermediate accuracy (R² = 0.59, NRMSE = 0.18). When only sample plots with up to two species were considered (n = 107), the estimation accuracy for all investigated variables (LAI, canopy chlorophyll content and leaf chlorophyll content) increased (NRMSE = 0.14, 0.16, 0.19, respectively). This shows the limits of the PROSAIL radiative transfer model in the case of very heterogeneous conditions. We also found that a carefully selected spectral subset contains sufficient information for a successful model inversion. Our results confirm the potential of model inversion for estimating vegetation biophysical parameters at the canopy scale in (moderately) heterogeneous grasslands using hyperspectral measurements.     

LAI retrieval from multiangular image classification and inversion of a ray tracing model

A non-conventional approach for the estimation of leaf area index (LAI) and leaf angle distribution (LAD), based on the use of information contained in multiangular images and the inversion of a canopy ray tracing model, is proposed in this work. As an alternative to the use of overall image reflectance data, the image fraction components, i.e. sunlit and shaded leaves and soil, are obtained by supervised classification of ground-based multiangular images acquired using an inexpensive colour infrared camera, the Dycam ADC. These data are used for the inversion of a numerical model of a vegetation canopy in which the latter is described as composed of randomly distributed disks (leaves). The model was developed using the POV-ray ray tracer. Model inversion is carried out using the look-up-table approach. The proposed methodology was tested using an extensive data set gathered on the potato crop during experimental trials carried out at Viterbo (Italy) for 3 years. The results show that LAI was successfully estimated with a RMSE varying from 0.29 to 0.75 in the different years. The potential sources of error in both estimated and measured LAI values are extensively discussed.     

Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates

Forest leaf area index (LAI), is an important variable in carbon balance models. However, understory vegetation is a recognized problem that limits the accuracy of satellite-estimated forest LAI. A canopy reflectance model was used to investigate the impact of the understory vegetation on LAI estimated from reflectance values estimated from satellite sensor data. Reflectance spectra were produced by the model using detailed field data as input, i.e. forest LAI, tree structural parameters, and the composition, distribution and reflectance of the forest floor. Common deciduous and coniferous forest types in southern Sweden were investigated. A negative linear relationship (r² = 0.6) was observed between field estimated LAI and the degree of understory vegetation, and the results indicated better agreement when coniferous and deciduous stands were analysed separately. The simulated spectra verified that the impact of the understory on the reflected signal from the top of the canopy is important; the reflectance values varying by up to ± 18% in the red and up to ± 10% in the near infra-red region of the spectra due to the understory. In order to predict the variation in LAI due to the understory vegetation, model inversions were performed where the input spectra were changed between the minimum, average and maximum reflectance values obtained from the forward runs. The resulting variation in LAI was found to be 1.6 units on average. The LAI of the understory could be predicted indirectly from simple stand data on forest characteristics, i.e. from allometric estimates, as an initial step in the process of estimating LAI. It is suggested here that compensation for the effect of the understory would improve the accuracy in the estimates of canopy LAI considerably.     

Application of hyperspectral vegetation indices to detect variations in high leaf area index temperate shrub thicket canopies

Accurate measurement of leaf area index (LAI), an important characteristic of plant canopies directly linked to primary production, is essential for monitoring changes in ecosystem C stocks and other ecosystem level fluxes. Direct measurement of LAI is labor intensive, impractical at large scales and does not capture seasonal or annual variations in canopy biomass. The need to monitor canopy related fluxes across landscapes makes remote sensing an attractive technique for estimating LAI. Many vegetation indices, such as Normalized Difference Vegetation Index (NDVI), tend to saturate at LAI levels > 4 although tropical and temperate forested ecosystems often exceed that threshold. Using two monospecific shrub thickets as model systems, we evaluated the potential of a variety of algorithms specifically developed to improve accuracy of LAI estimates in canopies where LAI exceeds saturation levels for other indices. We also tested the potential of indices developed to detect variations in canopy chlorophyll to estimate LAI because of the direct relationship between total canopy chlorophyll content and LAI. Indices were evaluated based on data from direct (litterfall) and indirect measurements (LAI-2000) of LAI. Relationships between results of direct and indirect ground-sampling techniques were also evaluated. For these two canopies, the indices that showed the highest potential to accurately differentiate LAI values > 4 were derivative indices based on red-edge spectral reflectance. Algorithms intended to improve accuracy at high LAI values in agricultural systems were insensitive when LAI exceeded 4 and offered little or no improvement over NDVI. Furthermore, indirect ground-sampling techniques often used to evaluate the potential of vegetation indices also saturate when LAI exceeds 4. Comparisons between hyperspectral vegetation indices and a saturated LAI value from indirect measurement may overestimate accuracy and sensitivity of some vegetation indices in high LAI communities. We recommend verification of indirect measurements of LAI with direct destructive sampling or litterfall collection, particularly in canopies with high LAI.     

双层植被结构冠层光谱特性的理论模拟

利用双层冠层反射率模型ACRM，模拟不同叶面积指数LAI、含水量Cw和结构参数N下，波长是820nm和1600nm冠层反射率的角度分布。结果说明，该模型能准确模拟出“热点”效应 |冠层反射率角度分布对LAI的敏感性小于LAI和Cw以及LAI和N的共同作用。其中LAI和Cw共同作用对波长1 600 nm反射率角度分布的影响非常显著，而LAI和N的共同作用在820 nm略微大于1 600 nm。另外，提取冠层含水量的土壤可调节水分指数SAWI受冠层结构的影响也较大。今后在模型选取中应该更好的考虑冠层结构影响。     

高光谱数据探讨水稻叶片叶绿素含量对叶片及冠层光谱反射特性的影响

为了考察叶绿素含量变化对叶片及冠层反射率的影响,利用PROSPECT模型和FCR模型,模拟叶片叶绿素含量变化(20μg/cm²、22μg/cm²、24μg/cm²、26μg/cm²、28μg/cm²、30μg/cm²)时的叶片反射率和叶面积指数变化(1、3、5)时的冠层反射率。叶片及冠层反射率的变化大小,用同一波段上反射率的标准差来衡量,研究的波段范围为400～940 nm,波段间隔为5 nm。研究发现叶绿素对叶片反射率的影响,在710 nm波长处最大,标准差变化曲线在550 nm、710 nm、795 nm和830 nm处形成4个峰值,对冠层反射率的影响,与叶面积指数大小有关,叶面积指数为1、3、5时,叶绿素对冠层反射率影响最大的波长分别为715 nm、720 nm和725 nm,标准差变化曲线分别在550 nm和715 nm、550 nm和720 nm、550 nm和725 nm形成了两个峰值。
     

Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale

This paper presents a physically-based approach for estimating critical variables describing land surface vegetation canopies, relying on remotely sensed data that can be acquired from operational satellite sensors. The REGularized canopy reFLECtance (REGFLEC) modeling tool couples leaf optics (PROSPECT), canopy reflectance (ACRM), and atmospheric radiative transfer (6SV1) model components, facilitating the direct use of at-sensor radiances in green, red and near-infrared wavelengths for the inverse retrieval of leaf chlorophyll content (C_ab) and total one-sided leaf area per unit ground area (LAI). The inversion of the canopy reflectance model is constrained by assuming limited variability of leaf structure, vegetation clumping, and leaf inclination angle within a given crop field and by exploiting the added radiometric information content of pixels belonging to the same field. A look-up-table with a suite of pre-computed spectral reflectance relationships, each a function of canopy characteristics, soil background effects and external conditions, is accessed for fast pixel-wise biophysical parameter retrievals. Using 1 m resolution aircraft and 10 m resolution SPOT-5 imagery, REGFLEC effectuated robust biophysical parameter retrievals for a corn field characterized by a wide range in leaf chlorophyll levels and intermixed green and senescent leaf material. Validation against in-situ observations yielded relative root-mean-square deviations (RMSD) on the order of 10% for the 1 m resolution LAI (RMSD = 0.25) and C_ab (RMSD = 4.4 μg cm^− 2) estimates, due in part to an efficient correction for background influences. LAI and C_ab retrieval accuracies at the SPOT 10 m resolution were characterized by relative RMSDs of 13% (0.3) and 17% (7.1 μg cm^− 2), respectively, and the overall intra-field pattern in LAI and C_ab was well established at this resolution. The developed method has utility in agricultural fields characterized by widely varying distributions of model variables and holds promise as a valuable operational tool for precision crop management. Work is currently in progress to extend REGFLEC to regional scales.     

Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies

Leaf chlorophyll content in coniferous forest canopies, a measure of stand condition, is the target of studies and models linking leaf reflectance and transmittance and canopy hyperspectral reflectance imagery. The viability of estimation of needle chlorophyll content from airborne hyperspectral optical data through inversion of linked leaf level and canopy level radiative transfer models is discussed in this paper. This study is focused on five sites of Jack Pine (Pinus banksiana Lamb.) in the Algoma Region (Canada), where field, laboratory and airborne data were collected in 1998 and 1999 campaigns. Airborne hyperspectral CASI data of 72 bands in the visible and near-infrared region and 2 m spatial resolution were collected from 20×20 m study sites of Jack Pine in 2 consecutive years. It was found that needle chlorophyll content could be estimated at the leaf level (r²=0.4) by inversion of the PROSPECT leaf model from needle reflectance and transmittance spectra collected with a special needle carrier apparatus coupled to the Li-Cor 1800 integrating sphere. The Jack Pine forest stands used for this study with LAI>2, and the high spatial resolution hyperspectral reflectance collected, allowed the use of the SPRINT canopy reflectance model coupled to PROSPECT for needle chlorophyll content estimation by model inversion. The optical index R750/R710 was used as the merit function in the numerical inversion to minimize the effect of shadows and LAI variation in the mean canopy reflectance from the 20×20 m plots. Estimates of needle pigment content from airborne hyperspectral reflectance using this linked leaf-canopy model inversion methodology showed an r²=0.4 and RMSE=8.1 μg/cm² when targeting sunlit crown pixels in Jack Pine sites with pigment content ranging between 26.8 and 56.8 μg/cm² (1570-3320 μg/g).     

Water content estimation in vegetation with MODIS reflectance data and model inversion methods

Statistical and radiative-transfer physically based studies have previously demonstrated the relationship between leaf water content and leaf-level reflectance in the near-infrared spectral region. The successful scaling up of such methods to the canopy level requires modeling the effect of canopy structure and viewing geometry on reflectance bands and optical indices used for estimation of water content, such as normalized difference water index (NDWI), simple ratio water index (SRWI) and plant water index (PWI). This study conducts a radiative transfer simulation, linking leaf and canopy models, to study the effects of leaf structure, dry matter content, leaf area index (LAI), and the viewing geometry, on the estimation of leaf equivalent water thickness from canopy-level reflectance. The applicability of radiative transfer model inversion methods to MODIS is studied, investigating its spectral capability for water content estimation. A modeling study is conducted, simulating leaf and canopy MODIS-equivalent synthetic spectra with random input variables to test different inversion assumptions. A field sampling campaign to assess the investigated simulation methods was undertaken for analysis of leaf water content from leaf samples in 10 study sites of chaparral vegetation in California, USA, between March and September 2000. MODIS reflectance data were processed from the same period for equivalent water thickness estimation by model inversion linking the PROSPECT leaf model and SAILH canopy reflectance model. MODIS reflectance data, viewing geometry values, and LAI were used as inputs in the model inversion for estimation of leaf equivalent water thickness, dry matter, and leaf structure. Results showed good correlation between the time series of MODIS-estimated equivalent water thickness and ground measured leaf fuel moisture (LFM) content (r²=0.7), demonstrating that these inversion methods could potentially be used for global monitoring of leaf water content in vegetation.     

Cover The wake of Hurricane Felix

Analytical canopy reflectance (CR) models have reached the level of adequacy that makes it possible to estimate vegetation parameters by inversion of such models. The growing efficiency of algorithms and the increasing power of computers urge the development of procedures for the estimation of vegetation phytometrical parameters on large areas using satellite data and inversion of theoretical CR models. In this article, clusterization of a Landsat Thematic Mapper (TM) quarter scene is performed in the space of spectral signatures, and the CR model is inverted for these clusters. Optical parameters of the atmosphere which are needed for the atmospheric correction are estimated on the same image. The estimated Leaf Area Index (LAI) pattern is in good accordance to the land use map. Estimated LAI and chlorophyll content of forests are systematically biased.     

土壤背景对冠层NDVI的影响分析

归一化差值植被指数NDVI是植被遥感中应用最为广泛的指数之一, 但它受土壤背景等因素的干扰比较强烈。结合实测的土壤数据以及公式推导、PROSAIL 模型模拟等方法分析了这种影响。首先, 假定与土壤线性混合且叶片呈水平分布的植被冠层, 根据土壤与植被分别在红光、近红外波段处的反射率值、植被覆盖度等参数, 利用公式推导了土壤背景对不同覆盖度下冠层NDVI的影响。其次, 利用PROSAIL冠层光谱模拟模型, 模拟分析了土壤背景对不同LAI下冠层NDVI的影响。分析的结果表明:LAI 越小, 土壤背景的影响越大; 暗土壤背景下的冠层NDVI值大于亮土壤背景下冠层的NDVI值; 并且,暗土壤条件下,NDVI值对土壤亮度的变化更敏感,而亮土壤下,NDVI值则对LAI或覆盖度的变化更敏感。最后利用实测的不同土壤背景下的冬小麦冠层光谱数据, 验证了公式推导和模型模拟的结果。     

Inversion of vegetation canopy reflectance models for estimating agronomic variables. V. Estimation of leaf area index and average leaf angle using measured canopy reflectances

The technique described earlier (Goel and Thompson, 1984b) for estimating agronomic parameters from bidirectional crop reflectance data is applied to a fully covered soybean canopy, using data measured in the field. This technique employs the inversion of a canopy reflectance model. It is shown that using the SAIL model one can estimate leaf area index (LAI) as well as average leaf angle (ALA) quite well, provided that the other canopy parameters (leaf reflectance and transmittance, soil reflectance, and fraction of diffused skylight) are known. Some suggestions are made for improving the SAIL model. This should improve the accuracy of estimation of not only LAI and ALA but should also allow the estimation of the complete leaf angle distribution.     

Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data

Accurate estimates of vegetation biophysical variables are valuable as input to models describing the exchange of carbon dioxide and energy between the land surface and the atmosphere and important for a wide range of applications related to vegetation monitoring, weather prediction, and climate change. The present study explores the benefits of combining vegetation index and physically based approaches for the spatial and temporal mapping of green leaf area index (LAI), total chlorophyll content (TC_ab), and total vegetation water content (VWC). A numerical optimization method was employed for the inversion of a canopy reflectance model using Terra and Aqua MODIS multi-spectral, multi-temporal, and multi-angle reflectance observations to aid the determination of vegetation-specific physiological and structural canopy parameters. Land cover and site-specific inversion modeling was applied to a restricted number of pixels to build multiple species- and environmentally dependent formulations relating the three biophysical properties of interest to a number of selected simpler spectral vegetation indices (VI). While inversions generally are computationally slow, the coupling with the simple and computationally efficient VI approach makes the combined retrieval scheme for LAI, TC_ab, and VWC suitable for large-scale mapping operations. In order to facilitate application of the canopy reflectance model to heterogeneous forested areas, a simple correction scheme was elaborated, which was found to improve forest LAI predictions significantly and also provided more realistic values of leaf chlorophyll contents.The inversion scheme was designed to enable biophysical parameter retrievals for land cover classes characterized by contrasting canopy architectures, leaf inclination angles, and leaf biochemical constituents without utilizing calibration measurements. Preliminary LAI validation results for the Island of Zealand, Denmark (57°N, 12°E) provided confidence in the approach with root mean square (RMS) deviations between estimates and in-situ measurements of 0.62, 0.46, and 0.63 for barley, wheat, and deciduous forest sites, respectively. Despite the independence on site-specific in-situ measurements, the RMS deviations of the automated approach are in the same range as those established in other studies employing field-based empirical calibration.Being completely automated and image-based and independent on extensive and impractical surface measurements, the retrieval scheme has potential for operational use and can quite easily be implemented for other regions. More validation studies are needed to evaluate the usefulness and limitations of the approach for other environments and species compositions.     

Effects of woody elements on simulated canopy reflectance: Implications for forest chlorophyll content retrieval

An important bio-indicator of actual plant health status, the foliar content of chlorophyll a and b (Cab), can be estimated using imaging spectroscopy. For forest canopies, however, the relationship between the spectral response and leaf chemistry is confounded by factors such as background (e.g. understory), canopy structure, and the presence of non-photosynthetic vegetation (NPV, e.g. woody elements)—particularly the appreciable amounts of standing and fallen dead wood found in older forests. We present a sensitivity analysis for the estimation of chlorophyll content in woody coniferous canopies using radiative transfer modeling, and use the modeled top-of-canopy reflectance data to analyze the contribution of woody elements, leaf area index (LAI), and crown cover (CC) to the retrieval of foliar Cab content. The radiative transfer model used comprises two linked submodels: one at leaf level (PROSPECT) and one at canopy level (FLIGHT). This generated bidirectional reflectance data according to the band settings of the Compact High Resolution Imaging Spectrometer (CHRIS) from which chlorophyll indices were calculated. Most of the chlorophyll indices outperformed single wavelengths in predicting Cab content at canopy level, with best results obtained by the Maccioni index ([R₇₈₀ − R₇₁₀] / [R₇₈₀ − R₆₈₀]). We demonstrate the performance of this index with respect to structural information on three distinct coniferous forest types (young, early mature and old-growth stands). The modeling results suggest that the spectral variation due to variation in canopy chlorophyll content is best captured for stands with medium dense canopies. However, the strength of the up-scaled Cab signal weakens with increasing crown NPV scattering elements, especially when crown cover exceeds 30%. LAI exerts the least perturbations. We conclude that the spectral influence of woody elements is an important variable that should be considered in radiative transfer approaches when retrieving foliar pigment estimates in heterogeneous stands, particularly if the stands are partly defoliated or long-lived.     

Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data

Reflectance data in the green, red and near-infrared wavelength region were acquired by the SPOT high resolution visible and geometric imaging instruments for an agricultural area in Denmark (56°N, 9°E) for the purpose of estimating leaf chlorophyll content (C_ab) and green leaf area index (LAI). SPOT reflectance observations were atmospherically corrected using aerosol data from MODIS and profiles of air temperature, humidity and ozone from the Atmospheric Infrared Sounder (AIRS), and used as input for the inversion of a canopy reflectance model. Computationally efficient inversion schemes were developed for the retrieval of soil and land cover-specific parameters which were used to build multiple species and site dependent formulations relating the two biophysical properties of interest to vegetation indices or single spectral band reflectances. Subsequently, the family of model generated relationships, each a function of soil background and canopy characteristics, was employed for a fast pixel-wise mapping of C_ab and LAI.The biophysical parameter retrieval scheme is completely automated and image-based and solves for the soil background reflectance signal, leaf mesophyll structure, specific dry matter content, Markov clumping characteristics, C_ab and LAI without utilizing calibration measurements.Despite the high vulnerability of near-infrared reflectances (ρ_nir) to variations in background properties, an efficient correction for background influences and a strong sensitivity of ρ_nir to LAI, caused LAI-ρ_nir relationships to be very useful and preferable over LAI-NDVI relationships for LAI prediction when LAI > 2. Reflectances in the green waveband (ρ_green) were chosen for producing maps of C_ab.The application of LAI-NDVI, LAI-ρ_nir and C_ab-ρ_green relationships provided reliable quantitative estimates of C_ab and LAI for agricultural crops characterized by contrasting architectures and leaf biochemical constituents with overall root mean square deviations between estimates and in-situ measurements of 0.74 for LAI and 5.0 μg cm^− 2 for C_ab.The results of this study illustrate the non-uniqueness of spectral reflectance relationships and the potential of physically-based inverse and forward canopy reflectance modeling techniques for a reasonably fast and accurate retrieval of key biophysical parameters at regional scales.     

Estimation of wheat leaf area index from IRS LISS-III data using Price model

A satellite sensor image based model suggested by Price was investigated for the estimation of Leaf Area Index (LAI) using data acquired by Linear Imaging Self Scanner-III (LISS-III) onboard Indian Remote Sensing Satellite-1C (IRS-1C) over two wheat growing sites in India (Karnal and Delhi) for crop seasons 1996-97 and 1997-98, respectively. Besides red and near-infrared (NIR) measurements over vegetation canopy, the model only requires a priori crop specific attentuation constants. These constants were computed for wheat using published and field ground reflectance measurements. Application of the model over 36 fields on which ground estimates of LAI were available, indicated a RMSE of 1.28 and 1.07 for the Karnal and Delhi sites, respectively.     

Retrieval of chlorophyll content and LAI of crops using hyperspectral techniques: application to PROBA/CHRIS data

Hyperspectral/multiangular data allow the retrieval of important vegetation properties at canopy level, such as the Leaf Area Index (LAI) and Leaf Chlorophyll Content. Current methods are based on the relationship between biophysical properties and retrievals from those spectral bands (from the complete hyperspectral/multiangular information) where specific absorption features are present within the considered spectral range. Furthermore, new sensors such as PROBA/CHRIS provide continuous hyperspectral reflectance measurements that can be considered as a continuous function of wavelength. The mathematical analysis of these continuous functions allows a new way of exploiting the relationships between spectral reflectance and biophysical variables by more powerful and stable mathematical tools, in particular for the retrieval of LAI and chlorophyll content. Within the overall context of the European Space Agency (ESA) Spectra Barrax Campaign (SPARC) experiment, an extensive field study was carried out in La Mancha, Spain, simultaneously to the overflight of airborne imaging spectrometers (AHS, HyMAP, ROSIS) and the overpass of CHRIS‐PROBA and MERIS sensors. During the SPARC‐2003 and SPARC‐2004 campaigns, numerous ground measurements were made in the Barrax study area (covering LAI, fCover, leaf chlorophyll a+b, leaf water content and leaf biomass), together with other complementary data, and a total of 17 CHRIS‐PROBA images were acquired. Representative points have been selected from a total of nine different crops, and also retrieved from the CHRIS‐PROBA images acquired within the days of the field campaign. About 250 reflectance spectra from five different observation angles have been analysed. Hyperspectral reflectance spectra have been adjusted by means of third‐degree polynomial functions between 500 nm and 750 nm, and correlations observed between LAI values and the coefficients of these polynomials yielded LAI as a result of the mathematical fitting. On the other hand, the area under the spectral reflectance curves has been calculated in the interval from 600 nm to 700 nm, the region of the red spectral interval where strong absorption features for chlorophyll have been observed, though areas under the curves are also strongly correlated to the chlorophyll content of the crops. Furthermore, a linear relationship between these areas and the chlorophyll content is proposed in this work. This relationship allows the retrieval of leaf chlorophyll by satellite data, based on the spectral information. Both of the proposed methods are almost independent of the observation angles employed. The high number of in situ measurements acquired simultaneously to satellite overpasses, and the broad available range of data, have allowed validation of both methods, with a large number of data and in a statistically consistent manner.     

Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations

The leaf area index (LAI) of fast-growing Eucalyptus plantations is highly dynamic both seasonally and inter-annually, and is spatially variable depending on pedo-climatic conditions. LAI is very important in determining the carbon and water balance of a stand, but is difficult to measure during a complete stand rotation and at large scales. Remote-sensing methods allowing the retrieval of LAI time series with accuracy and precision are therefore necessary. Here, we tested two methods for LAI estimation from MODIS 250m resolution red and near-infrared (NIR) reflectance time series. The first method involved the inversion of a coupled model of leaf reflectance and transmittance (PROSPECT4), soil reflectance (SOILSPECT) and canopy radiative transfer (4SAIL2). Model parameters other than the LAI were either fixed to measured constant values, or allowed to vary seasonally and/or with stand age according to trends observed in field measurements. The LAI was assumed to vary throughout the rotation following a series of alternately increasing and decreasing sigmoid curves. The parameters of each sigmoid curve that allowed the best fit of simulated canopy reflectance to MODIS red and NIR reflectance data were obtained by minimization techniques. The second method was based on a linear relationship between the LAI and values of the GEneralized Soil Adjusted Vegetation Index (GESAVI), which was calibrated using destructive LAI measurements made at two seasons, on Eucalyptus stands of different ages and productivity levels. The ability of each approach to reproduce field-measured LAI values was assessed, and uncertainty on results and parameter sensitivities were examined. Both methods offered a good fit between measured and estimated LAI (R² = 0.80 and R² = 0.62 for model inversion and GESAVI-based methods, respectively), but the GESAVI-based method overestimated the LAI at young ages.