基于MODIS数据的玉米植被参数估算方法的对比分析

基于实测数据建立了FPAR、LAI的植被指数估算模型（NDVI、RVI、NDWI）,并将其应用于MODIS BRDF数据对德惠地区玉米FPAR、LAI进行估算,然后将MODIS 15A2 FPAR/LAI产品值分别与BRDF估算值、地面实测值进行对比分析。主要得出以下结论：植被指数NDVI、RVI都能较好地用于实测数据和MODIS BRDF数据的FPAR、LAI估算;NDWI虽然在实测数据中估算玉米FPAR、LAI的效果优于NDVI、RVI,但其应用于MODIS BRDF数据估算FPAR、LAI时,效果却较差。BRDF数据估算FPAR与MODIS 15A2 FPAR值的关系因生长时期不同而异,在玉米生长前期,前者高于后者,而生长后期两者却较相近;BRDF估算LAI值一直都高于MODIS 15A2 LAI产品值。生长季前期,MOD15A2 FPAR、LAI值接近实测值,而在后期却高于实测值。通过分析也表明,玉米苗期MODIS 15A2 FPAR数值变化范围较小,产品算法对实际FPAR变化尚不够敏感,这可能是影响MODIS FPAR产品精度的一个原因。     

Performance of the MISR LAI and FPAR algorithm: a case study in Africa

The Multi-angle Imaging SpectroRadiometer (MISR) instrument is designed to provide global imagery at nine discrete viewing angles and four visible/near-infrared spectral bands. The MISR standard products include green leaf area index (LAI) of vegetation and fraction of photosynthetically active radiation absorbed by vegetation (FPAR). These parameters are being routinely processed from MISR data at the Langley Atmospheric Sciences Data Center (ASDC) since October 2002. This paper describes the research basis for transitioning the MISR LAI/FPAR product from beta to provisional status. The quality and spatial coverage of MISR land surface reflectances that are input to the algorithm determine the quality and spatial coverage of the LAI and FPAR products. Therefore, considerable efforts have been expended to analyze the performance of the algorithm as a function of uncertainties of MISR surface reflectances and to establish the convergence property of the MISR LAI/FPAR algorithm, namely, that the reliability and accuracy of the retrievals increase with increased input information content and accuracy. An additional objective of the MISR LAI/FPAR algorithm is classification of global vegetation into biome types—information that is usually an input to remote sensing algorithms that use single-angle observations. An upper limit of uncertainties of MISR surface reflectances that allows discrimination between biomes, minimizes the impact of biome misidentification on LAI retrievals, and maximizes the spatial coverage of retrievals was estimated. Algorithm performance evaluated on a limited set of MISR data from Africa suggests valid LAI retrievals and correct biome identification in about 20% of the pixels, on an average, given the current level of uncertainties in the MISR surface reflectance data. The other 80% of the LAI values are retrieved using incorrect information about the type of biome. However, the use of multi-angle data minimizes the impact of biome misidentification on LAI retrievals; that is, with a probability of about 70%, uncertainties in LAI retrievals due to biome misclassification do not exceed uncertainties in the observations. We also discuss in depth the parameters that characterize LAI/FPAR product quality—such as quality assessment (QA) that is available to the users along with the product. The analysis of the MISR LAI/FPAR product presented here demonstrates the physical basis of the radiative transfer algorithm used in the retrievals and, importantly, that the reliability and accuracy of the retrievals increase with increased input information content and accuracy. Further improvements in the quality of MISR surface reflectances are therefore expected to lead to LAI and FPAR retrievals of increasing quality.     

高光谱遥感在植被监测中的研究综述

高光谱遥感数据已成为地表植被地学过程中对地观测的强有力的工具。综述了利用高光谱遥感数据进行植被监测的研究进展,主要包括以下三个部分:(1)高光谱遥感信息的处理方法;(2)高光谱遥感数据用于植被参数估算与分析;(3)高光谱遥感数据在植被生长监测中的作用。     

黑河综合遥感联合试验研究进展:水文与生态参量遥感反演与估算

介绍了“黑河综合遥感联合试验”在水文和生态变量与参数反演、估算和模型应用方面取得的进展。在水文变量遥感方面,利用车载双偏振多普勒雷达在黑河上游和中游分别开展了高精度降水观测,获取了后向散射系数和极化信息与降水强度之间的定量关系。在综合利用多源观测信息,改进和发展蒸散发估算模型方面取得了实质性的进展。发展了利用K和Ka波段机载微波辐射计数据反演山区积雪深度的方法。针对SAR观测数据反演土壤水分中地表粗糙度的显著干扰,发展了消除粗糙度影响的反演方法。在生态过程遥感参量估算方面,提出了一种基于机载激光雷达和高分辨率光学影像的高精度地物信息分类方法。发展了从高光谱航空遥感提取植被自然光照下的荧光,并与NDVI结合的C3/C4植被分类方法。发展和改进了使用多角度、多光谱观测反演叶面积指数的方法,挖掘了激光雷达在植被垂直结构探测上的潜力,探索了叶面积指数遥感中的尺度转换规律。发展了利用高光谱数据中的荧光信息反演光能利用率的新方法;建立了考虑土壤反射率、冠层结构等因素的光合作用有效辐射比率反演模型;改进了利用遥感估计生态系统生产力的模型。发展了利用高光谱遥感数据提取叶绿素含量和叶绿素荧光强度的方法。     

Extracting forest canopy structure from spatial information of high resolution optical imagery: tree crown size versus leaf area index

Leaves are the primary interface where energy, water and carbon exchanges occur between the forest ecosystems and the atmosphere. Leaf area index (LAI) is a measure of the amount of leaf area in a stand, and the tree crown size characterizes how leaves are clumped in the canopy. Both LAI and tree crown size are of essential ecological and management value. There is a lot of interest in extracting both canopy structural parameters from remote sensing. The LAI is generally estimated with spectral information from remotely sensed images at relatively coarse spatial resolution. There has been much less success in estimating tree crown size with remote sensing. The recent availability of abundant high spatial resolution imagery from space offers new potential for extracting LAI and tree crown size, particularly in the spatial domain. This study found that the spatial information in Ikonos imagery is highly valuable in estimating both tree crown size and LAI. When the conifer‐ and hardwood‐dominated stands are pooled, tree crown sizes of conifer stands relate best to the ratio of image variance at 2×2 m spatial resolution to that at 3×3 m spatial resolution, while LAI relates best to image variance at 4×4 m spatial resolution. When the conifer‐ and hardwood‐dominated stands are separated, image spatial information estimates tree crown size much better for conifer‐dominated stands than for the hardwood‐dominated stands, while the relationship between image spatial information and LAI is strengthened after the two types of stands are combined. Tree crown size is more sensitive to image spatial resolution than LAI. Image variance is more useful in estimating LAI than normalized difference vegetation index (NDVI) and simple ratio vegetation index (SRVI). Combining both spatial and spectral information provides some improvement in estimating LAI compared with using spatial information alone. Therefore, future efforts to estimate canopy structure with high resolution imagery should also use image spatial information.     

Book reviews

Proximal and remote sensing measurements were used to calculate different vegetation indices that were applied as predictors of gross primary production (GPP), total ecosystem respiration (TER), net ecosystem production (NEP) and leaf area index (LAI). Reflectance data and carbon fluxes were collected during the 2005 growing season at a mountain grassland site in the Italian Alps. Significant relationships were found between GPP, TER, NEP, LAI and the most commonly used spectral vegetation indices, the Normalized Difference Vegetation Index (NDVI) and Green‐NDVI. Saturation of the spectral indices was evident in the estimation of both biophysical and ecophysiological parameters. Among the different indices, Green‐NDVI was less affected by saturation on both a spatial and a temporal basis. Therefore, the use of an additional green‐band sensor for spectral measurements at eddy covariance grassland sites is recommended. Concerning the bandwidth for the calculation of the indices, the highest predictive capacities among the sensor simulations included in the analysis were those of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the high‐resolution hyperspectral instrument Hyperion, indicating the advantage of narrow bands for the prediction of plant parameters. Further analyses are, however, required to investigate the relationships between NEP, GPP and vegetation indices retrieved from satellite platforms, using the bands available on MODIS and Hyperion sensors.     

Albedo and LAI estimates from FORMOSAT-2 data for crop monitoring

This paper aimed at estimating albedo and Leaf Area Index (LAI) from FORMOSAT-2 satellite that offers a unique source of high spatial resolution (eight meters) images with a high revisit frequency (one to three days). It mainly consisted of assessing the FORMOSAT-2 spectral and directional configurations that are unusual, with a single off nadir viewing angle over four visible-near infra red wavebands. Images were collected over an agricultural region located in South Eastern France, with a three day frequency from the growing season to post-harvest. Simultaneously, numerous ground based measurements were performed over various crops such as wheat, meadow, rice and maize. Albedo and LAI were estimated using empirical approaches that have been widely used for usual directional and spectral configurations (i.e. multidirectional or single nadir viewing angle over visible-near infrared wavebands). Two methods devoted to albedo estimation were assessed, based on stepwise multiple regression and neural network (NNT). Although both methods gave satisfactory results, the NNT performed better (relative RMSE = 3.5% versus 7.3%), especially for low vegetation covers over dark or wet soils that corresponded to albedo values lower than 0.20. Four approaches for LAI estimation were assessed. The first approach based on a stepwise multiple regression over reflectances had the worst performance (relative RMSE = 65%), when compared to the equally performing NDVI based heuristic relationship and reflectance based NNT approach (relative RMSE ≈ 34%). The NDVI based neural network approach had the best performance (relative RMSE = 27.5%), due to the combination of NDVI efficient normalization properties and NNT flexibility. The high FORMOSAT-2 revisit frequency allowed next replicating the dynamics of albedo and LAI, and detecting to some extents cultural practices like vegetation cuts. It also allowed investigating possible relationships between albedo and LAI. The latter depicted specific trends according to vegetation types, and were very similar when derived from ground based data, remotely sensed observations or radiative transfer simulations. These relationships also depicted large albedo variabilities for low LAI values, which confirmed that estimating one variable from the other would yield poor performances for low vegetation cover with varying soil backgrounds. Finally, this empirical study demonstrated, in the context of exhaustively describing the spatiotemporal variability of surface properties, the potential synergy between 1) ground based web-sensors that continuously monitor specific biophysical variables over few locations, and 2) high spatial resolution satellite with high revisit frequencies.     

Landsat8卫星影像大气校正及其植被指数与SEVI性能比较

遥感影像受大气的吸收散射以及地形起伏变化的影响,使得传感器接收到的辐射信号既包含了地物的信息,同时也包含了大气以及地形的信息。为了提高地表反射率的反演精度,需要去除遥感影像中大气和地形的影响。提出了一种基于查找表的Landsat8-OLI遥感影像的大气校正方法,该方法由6S辐射传输模型生成查找表,其中输入的参数包括大气水蒸汽含量、臭氧浓度和气溶胶光学厚度等MODIS大气参数产品。利用传统方法建立的大气参数查找表通常只考虑一部分因素,这对于以MODIS产品为输入参数的大气校正是不适用的。本文建立了一个包括大部分输入参数的高维大气校正查找表,对于Landsat-8 OLI传感器具有很高的通用性,通过进行光谱分析、与USGS地表反射率产品交叉验证等方式来验证模型的精度。验证结果表明该方法能有效地反演精确可靠的地表反射率。最后,采用目视解译、统计分析将校正结果与SEVI做对比分析,比较地形影响消减的效果。结果表明该模型与SEVI在地形消减的效果上作用相当。     

Landsat8卫星影像大气校正及其植被指数与SEVI性能比较

遥感影像受大气的吸收散射以及地形起伏变化的影响，使得传感器接收到的辐射信号既包含了地物的信息，同时也包含了大气以及地形的信息。为了提高地表反射率的反演精度，需要去除遥感影像中大气和地形的影响。提出了一种基于查找表的Landsat8-OLI遥感影像的大气校正方法，该方法由6S辐射传输模型生成查找表，其中输入的参数包括大气水蒸汽含量、臭氧浓度和气溶胶光学厚度等MODIS大气参数产品。利用传统方法建立的大气参数查找表通常只考虑一部分因素，这对于以MODIS产品为输入参数的大气校正是不适用的。本文建立了一个包括大部分输入参数的高维大气校正查找表，对于Landsat-8 OLI传感器具有很高的通用性，通过进行光谱分析、与USGS地表反射率产品交叉验证等方式来验证模型的精度。验证结果表明该方法能有效地反演精确可靠的地表反射率。最后，采用目视解译、统计分析将校正结果与SEVI做对比分析，比较地形影响消减的效果。结果表明该模型与SEVI在地形消减的效果上作用相当。     

全球典型植被叶片光谱特征及其对LAI反演的影响分析

在全球范围长时间序列LAI遥感产品反演算法中,植被冠层反射率模型仅使用少量叶片光谱特征代表全球植被全年的典型植被光谱特征,叶片光谱的不确定性导致LAI遥感产品存在一定的误差。目前全球已经构建了多个典型植被叶片波谱数据集,这些数据集包含多个植被物种、不同空间地域及多时相叶片光谱数据,为定量分析叶片光谱特征提供了数据支持。主要利用LOPEX’93、ANGERS’03、中国典型地物波谱数据库和野外实测的叶片光谱数据,以黄边参数、红边参数和叶片光谱指数作为分析指标,探讨不同植被物种、不同气候区和不同物候期的叶片光谱特征差异,及其对植被冠层反射率、LAI反演的影响,为发展考虑现实叶片光谱差异的LAI反演算法提供研究基础。结果表明:植被叶片光谱存在多样性,叶片光谱特征差异主要影响MODIS传感器近红外波段和绿波段反射率值,其中,绿波段反射率值对叶片光谱变化最为敏感;在LAI反演算法中,如果只考虑植被类型而不考虑物种叶片光谱差异,可能会给LAI反演带来大于3的误差。     

Land cover mapping in support of LAI and FPAR retrievals from EOS-MODIS and MISR: Classification methods and sensitivities to errors

Land cover maps are used widely to parameterize the biophysical properties of plant canopies in models that describe terrestrial biogeochemical processes. In this paper, we describe the use of supervised classification algorithms to generate land cover maps that characterize the vegetation types required for Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) retrievals from MODIS and MISR. As part of this analysis, we examine the sensitivity of remote sensing-based retrievals of LAI and FPAR to land cover information used to parameterize vegetation canopy radiative transfer models. Specifically, a decision tree classification algorithm is used to generate a land cover map of North America from Advanced Very High Resolution Radiometer (AVHRR) data with 1 km spatial resolution using a six-biome classification scheme. To do this, a time series of normalized difference vegetation index data from the AVHRR is used in association with extensive site-based training data compiled using Landsat Thematic Mapper (TM) and ancillary map sources. Accuracy assessment of the map produced via decision tree classification yields a cross-validated map accuracy of 73%. Results comparing LAI and FPAR retrievals using maps from different sources show that disagreement in land cover labels generally do not translate into strong disagreement in LAI and FPAR maps. Further, the main source of disagreement in LAI and FPAR maps can be attributed to specific biome classes that are characterized by a continuum of fractional cover and canopy structure.     

Effect of foliage spatial heterogeneity in the MODIS LAI and FPAR algorithm over broadleaf forests

This paper presents the analysis of radiative transfer assumptions underlying moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) algorithm for the case of spatially heterogeneous broadleaf forests. Data collected by a Boston University research group during the July 2000 field campaign at the Earth Observing System (EOS) core validation site, Harvard Forest, MA, were used for this purpose. The analysis covers three themes. First, the assumption of wavelength independence of spectral invariants of transport equation, central to the parameterization of the MODIS LAI and FPAR algorithm, is evaluated. The physical interpretation of those parameters is given and an approach to minimize the uncertainties in its retrievals is proposed. Second, the theoretical basis of the algorithm was refined by introducing stochastic concepts which account for the effect of foliage clumping and discontinuities on LAI retrievals. Third, the effect of spatial heterogeneity in FPAR was analyzed and compared to FPAR variation due to diurnal changes in solar zenith angle (SZA) to asses the validity of its static approximation.     

SMAP卫星后向散射系数与海表面风场关系

海面风是海气互相作用的重要参数之一,如何通过雷达后向散射数据有效提取海表面风场信息,对于海洋动力环境遥感监测具有重要的研究意义。使用SMAP卫星L波段真实孔径雷达数据和国家环境预测中心再分析风场数据进行匹配,利用地球物理模型函数分析了SMAP卫星数据的后向散射系数与海表面风场之间的关系, 讨论了不同风速和不同相对风向角时SMAP卫星数据反演海表面风场的潜力。研究显示,水平极化和垂直极化的后向散射系数与风速的关系紧密,适于海表面风场的反演;SMAP卫星数据存在正-侧风不对称现象和逆正-侧风不对称现象;在相对风向角为90°和270°时后向散射系数与风场的关系较为模糊;随着风速的增加,后向散射系数与相对风向角的规律关系也越来越明显,振幅也随风速增大而增大。GMF函数计算的风速偏差为1.19 m/s（水平极化）和1.51 m/s（垂直极化）,均方根误差为1.58 m/s（水平极化）和1.67 m/s（垂直极化）。     

A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery

Plant foliage density expressed as leaf area index (LAI) is used in many ecological, meteorological, and agronomic models, and as a means of quantifying crop spatial variability for precision farming. LAI retrieval using spectral vegetation indices (SVI) from optical remotely sensed data usually requires site-specific calibration values from the surface or the use of within-scene image information without surface calibrations to invert radiative transfer models. An evaluation of LAI retrieval methods was conducted using (1) empirical methods employing the normalized difference vegetation index (NDVI) and a new SVI that uses green wavelength reflectance, (2) a scaled NDVI approach that uses no calibration measurements, and (3) a hybrid approach that uses a neural network (NN) and a radiative transfer model without site-specific calibration measurements. While research has shown that under a variety of conditions NDVI is not optimal for LAI retrieval, its continued use for remote sensing applications and in analysis seeking to develop improved parameter retrieval algorithms based on NDVI suggests its value as a “benchmark” or standard against which other methods can be compared. Landsat-7 ETM+ data for July 1 and July 8 from the Soil Moisture EXperiment 2002 (SMEX02) field campaign in the Walnut Creek watershed south of Ames, IA, were used for the analysis. Sun photometer data collected from a site within the watershed were used to atmospherically correct the imagery to surface reflectance. LAI validation measurements of corn and soybeans were collected close to the dates of the Landsat-7 overpasses. Comparable results were obtained with the empirical SVI methods and the scaled SVI method within each date. The hybrid method, although promising, did not account for as much of the variability as the SVI methods. Higher atmospheric optical depths for July 8 leading to surface reflectance errors are believed to have resulted in overall poorer performance for this date. Use of SVIs employing green wavelengths, improved method for the definition of image minimum and maximum clusters used by the scaled NDVI method, and further development of a soil reflectance index used by the hybrid NN approach are warranted. More importantly, the results demonstrate that reasonable LAI estimates are possible using optical remote sensing methods without in situ, site-specific calibration measurements.     

Using hyperspectral vegetation indices to estimate the fraction of photosynthetically active radiation absorbed by corn canopies

The fraction of photosynthetically active radiation (FPAR) absorbed by vegetation – a key parameter in crop biomass and yields as well as net primary productivity models – is critical to guiding crop management activities. However, accurate and reliable estimation of FPAR is often hindered by a paucity of good field-based spectral data, especially for corn crops. Here, we investigate the relationships between the FPAR of corn (Zea mays L.) canopies and vegetation indices (VIs) derived from concurrent in situ hyperspectral measurements in order to develop accurate FPAR estimates. FPAR is most strongly (positively) correlated to the green normalized difference vegetation index (GNDVI) and the scaled normalized difference vegetation index (NDVI*). Both GNDVI and NDVI* increase with FPAR, but GNDVI values stagnate as FPAR values increase beyond 0.75, as previously reported according to the saturation of VIs – such as NDVI – in high biomass areas, which is a major limitation of FPAR-VI models. However, NDVI* shows a declining trend when FPAR values are greater than 0.75. This peculiar VI–FPAR relationship is used to create a piecewise FPAR regression model – the regressor variable is GNDVI for FPAR values less than 0.75, and NDVI* for FPAR values greater than 0.75. Our analysis of model performance shows that the estimation accuracy is higher, by as much as 14%, compared with FPAR prediction models using a single VI. In conclusion, this study highlights the feasibility of utilizing VIs (GNDVI and NDVI*) derived from ground-based spectral data to estimate corn canopy FPAR, using an FPAR estimation model that overcomes limitations imposed by VI saturation at high FPAR values (i.e. in dense vegetation).     

Downscaling real-time vegetation dynamics by fusing multi-temporal MODIS and Landsat NDVI in topographically complex terrain

Canopy phenology is an important factor driving seasonal patterns of water and carbon exchange between land surface and atmosphere. Recent developments of real-time global satellite products (e.g., MODIS) provide the potential to assimilate dynamic canopy measurements with spatially distributed process-based ecohydrological models. However, global satellite products usually are provided with relatively coarse spatial resolutions, averaging out important spatial heterogeneity of both terrain and vegetation. Therefore, bias can result from lumped representation of ecological and hydrological processes especially in topographically complex terrain. Successful downscaling of canopy phenology to high spatial resolution would be indispensable for catchment-scale distributed ecohydrological modeling, aiming at understanding complex patterns of water, carbon and nutrient cycling in mountainous watersheds. Two downscaling approaches are developed in this study to overcome this issue by fusing multi-temporal MODIS and Landsat TM data in conjunction with topographic information to estimate high spatio-temporal resolution biophysical parameters over complex terrain. MODIS FPAR (fraction of absorbed photosynthetically active radiation) is used to provide medium spatial resolution phenology, while the variability of vegetation within a MODIS pixel is characterized by Landsat NDVI. The algorithms depend on the scale-invariant linear relationship between FPAR and NDVI, which is verified in this study. Downscaled vegetation dynamics are successfully validated both temporally and spatially with ground-based continuous FPAR and leaf area index measurements. Topographic correction during the downscaling process has a limited effect on downscaled FPAR products except for the period around the winter solstice in the study area.     

多尺度植被信息提取模型研究*

针对遥感影像中植被信息的波谱特征,提出了整体—局部植被信息多尺度迭代转换提取模型。首先在基于植被指数的基础上对影像进行分割,并通过样本的自动选择,对影像进行大尺度分类;然后对分类结果进行缓冲区分析,建立局部区域对象,再进行小尺度的局部分割与分类;最后通过迭代,重复整体—局部的过程,使得植被与非植被信息的边界得到最优化分离,从而提高了植被信息提取的精度。选取江汉平原地区的LANDSAT ETM＋影像进行实验,并与常规方法得到的结果进行了对比,实验证明,多尺度迭代提取方法可以有效地提高植被信息提取的精度。     

An analytical algorithm for the determination of vegetation Leaf Area Index from TRMM/TMI data

In this paper, an analytical algorithm for the determination of land surface vegetation Leaf Area Index (LAI) with the passive microwave remote sensing data is developed. With the developed algorithm and the Tropical Rainfall Measuring Mission/Microwave Imager (TRMM/TMI) remote sensing data collected during the Global Energy and Water Experiment (GEWEX) Asian Monsoon Experiment in Tibet (GAME/Tibet) Intensive Observation Period (IOP'98), the regional and temporal distributions of the land surface vegetation LAI have been evaluated. To validate the developed algorithm and the retrieval results, the maximum-composite Normalized Difference Vegetation Index (NDVI) data over the same study area and period are used in this study; the cloud contaminated NDVI values have been replaced by the cloud-free values reconstructed by the Harmonic ANnalysis of Time Series (HANTS) technique. The results show that the retrieved LAI is in good agreement with the cloud-free NDVI in regional and temporal distributions and in their statistical characteristics; the vegetation characteristics can be clearly assessed from the regional distribution of the retrieved LAI. As lower frequency microwave radiation can penetrate atmosphere and thin cloud layer, with the application of the passive microwave remote sensing data, the developed algorithm can be used to monitor the land surface vegetation condition more effectively.     

Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS

The long term Advanced Very High Resolution Radiometer (AVHRR)‐Normalized Difference Vegetation Index (NDVI) record provides a critical historical perspective on vegetation dynamics necessary for global change research. Despite the proliferation of new sources of global, moderate resolution vegetation datasets, the remote sensing community is still struggling to create datasets derived from multiple sensors that allow the simultaneous use of spectral vegetation for time series analysis. To overcome the non‐stationary aspect of NDVI, we use an artificial neural network (ANN) to map the NDVI indices from AVHRR to those from MODIS using atmospheric, surface type and sensor‐specific inputs to account for the differences between the sensors. The NDVI dynamics and range of MODIS NDVI data at 1° is matched and extended through the AVHRR record. Four years of overlap between the two sensors is used to train a neural network to remove atmospheric and sensor specific effects on the AVHRR NDVI. In this paper, we present the resulting continuous dataset, its relationship to MODIS data, and a validation of the product.     

Estimation of absorbed PAR across Scandinavia from satellite measurements. Part II: Modeling and evaluating the fractional absorption

The aim of this study is to generate a quality-controlled sub-kilometer dataset of the fraction of absorbed photosynthetically active radiation (FAPAR) across Scandinavia from satellite. FAPAR is required for estimating the amount of PAR absorbed (APAR) by vegetation, which in turn allows for estimation of carbon uptake. In this study, FAPAR was modeled from normalized difference vegetation index (NDVI) which was obtained from the MODIS VI product (MOD13Q1) at 250 m spatial resolution. Modeled FAPAR was evaluated against in-situ measurements of fractional interception of PAR (FIPAR) and FAPAR at nine plots in six forested sites across Sweden and Denmark from 2001 to 2005. High resolution remote sensing data were used to investigate the representativeness of the measurement areas. Furthermore, FAPAR from the MODIS LAI/FPAR product at 1 km spatial resolution (MOD15A2) was investigated and compared the measured and modeled FAPAR. There was good agreement between modeled and measured FAPAR (6.9% average RMSE of the means). A linear relationship between daily values of NDVI and FAPAR was found (R² = 0.82), and it is concluded that seasonally adjusted NDVI can be used for accurate FAPAR estimations over forested areas in Scandinavia. However, it was found that the error was correlated with average FAPAR and that it is important to take the understory vegetation into account when measuring FAPAR in open canopies. The observed difference between FIPAR and FAPAR was 2.3 and 1.4 percentage units for coniferous and deciduous stands, respectively. MODIS FAPAR performed well although a few unrealistic values were present, highlighting the necessity to filter out low quality values using the quality-control datasets.