The evaluation and validation of climate data records (CDRs) derived from remote sensing play crucial roles in their generation and applications. However, many existing evaluation schemes rely on simplistic, spatiotemporally invariant metrics to assess the products’ overall quality, which leads to the long-term neglect of intra-product inconsistencies stemming from observation conditions, algorithmic differences, and sensor degradation. Leaf area index (LAI) is a crucial variable for land surface and climate modeling, and the intra-product inconsistency will increase the uncertainty in related studies. In order to improve the evaluation scheme of LAI products and ensure their reliability, we propose a new perspective for evaluating global LAI time series. In this study, we utilize the Moderate Resolution Imaging Spectroradiometer (MODIS) C6.1 LAI product as an example to infer its internal consistency through cross-comparisons among different sensors and spatiotemporal correlations between two adjacent years of the product. We found that compared to the main algorithm, the backup algorithm of the MODIS LAI product tends to underestimate the retrieval results. This inconsistency is particularly pronounced in tropical regions but relatively minor in most other areas. Additionally, these inconsistencies can lead to unusual fluctuations in the LAI time series, impacting the magnitude and direction of short-term vegetation monitoring. However, the influence on long-term trend analyses is negligible. Therefore, special attention should be given to the intra-product consistency in certain studies. In conclusion, the evaluation perspective proposed in this study is of great significance for improving the LAI evaluation scheme and ensuring the use and improvement of remote sensing products.

The leaf area index (LAI) is a key structural parameter of vegetation canopies. Accordingly, several moderate-resolution global LAI products have been produced and widely used in the field of remote sensing. However, the accuracy of the current moderate-resolution global LAI products cannot satisfy the requirements recommended by the LAI application communities, especially in heterogeneous areas composed of mixed land cover types. In this study, we propose a mixed-pixel correction (MPC) method to improve the accuracy of LAI retrievals over heterogeneous areas by considering the influence of heterogeneity caused by the mixture of different biome types with the help of high-resolution land cover maps. The DART-simulated LAI, the aggregated Landsat LAI, and the site-based high-resolution LAI reference maps are used to evaluate the performance of the MPC method. The results indicate that the MPC method can reduce the influences of spatial heterogeneity and biome misclassification to obtain the LAI with much better accuracy than the Moderate Resolution Imaging Spectroradiometer (MODIS) main algorithm, given that the high-resolution land cover map is accurate. The root mean square error (RMSE) (bias) decreases from 0.749 (0.486) to 0.414 (0.087), while the R2 increases from 0.084 to 0.524, and the proportion of pixels that fulfill the uncertainty requirement of the GCOS increases from 38.2% to 84.6% for the results of site-based high-resolution LAI reference maps. Spatially explicit information about vegetation fractional cover can further reduce uncertainties induced by variations in canopy density for the results of DART simulated data. The proposed method shows potential for improving global moderate-resolution LAI products.

The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) product is critical for global terrestrial carbon monitoring and ecosystem modeling. However, MODIS LAI is calculated on a pixel-by-pixel and day-by-day basis without using spatial or temporal correlation information, which leads to its high sensitivity of LAI to uncertainties in observed reflectance resulting in an increased noise level in time series. While exploiting prior knowledge is a common practice to fill gaps in observations, little research has been conducted on reducing noisy fluctuations and improving the overall quality of the MODIS LAI product. To address this issue, we proposed a Spatio-Temporal Information Composition Algorithm (STICA), which directly introduces prior Spatio-temporal correlation and Multiple Quality Assessment (MQA) information into the existing MODIS LAI product. STICA reduces the noise level and improves the quality of the product while maintaining the original physically-based (Radiative Transfer Model, RTM) LAI production process. In our analysis, the R2 increased from 0.79 to 0.81, and the RMSE decreased from 0.81 to 0.68 compared to the ground-based LAI reference. The improvement was more pronounced with the degradation of the data quality. STICA reduced noisy fluctuations in the LAI time series to varying degrees among eight biome types. In the Amazon Forest, STICA significantly improved the time-series stability of LAI. Moreover, STICA can effectively eliminate abnormal declines in time series and correct for extreme outliers in LAI. We expect that the MODIS LAI Reanalyzed product generated by this method will better support the application of high-quality LAI datasets.

Bidirectional reflectance distribution function (BRDF) models are used to correct surface bidirectional effects and estimate land surface albedo. Many operational BRDF/albedo algorithms adopt a Roujean linear kernel-driven BRDF (RLKB) model because of its simple form and good performance in fitting multidirectional surface reflectance values. However, this model does not explicitly consider topographic effects, resulting in errors when applied over rugged terrain. To address this issue, we proposed a hybrid algorithm suitable for both flat and rugged terrain, called topographical kernel-driven (Topo-KD). First, we constructed a linear kernel-driven BRDF model considering terrain (LKB_T) which describes the topographic effects with a mountain radiative transfer (MRT) model. Then, the Topo-KD algorithm adaptively selects the most suitable model (RLKB or LKB_T) according to the terrain conditions and fitting residuals. The performances of Topo-KD and RLKB using the RossThick–LiSparseReciprocal (RTLSR) kernel are compared using simulated data sets and moderate-resolution imaging spectroradiometer (MODIS) observations. The results show that the BRDF of the pixel is affected by topography. But the RTLSR model does not specifically account for it, resulting in larger biases over rugged terrain than the Topo-KD algorithm in both the red and near-infrared (NIR) bands. The experiment using MODIS data sets demonstrates that the Topo-KD algorithm reduces fitting residuals in the red and NIR bands by 21.5% and 27.4% compared with the RTLSR model. These results indicate that the Topo-KD algorithm can be a better choice for retrieving land surface parameters and describing the radiative transfer process in mountainous areas.

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation have been successfully generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) data since early 2000. As the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard, the Suomi National Polar-orbiting Partnership (SNPP) has inherited the scientific role of MODIS, and the development of a continuous, consistent, and well-characterized VIIRS LAI/FPAR data set is critical to continue the MODIS time series. In this paper, we build the radiative transfer-based VIIRS-specific lookup tables by achieving minimal difference with the MODIS data set and maximal spatial coverage of retrievals from the main algorithm. The theory of spectral invariants provides the configurable physical parameters, i.e., single scattering albedos (SSAs) that are optimized for VIIRS-specific characteristics. The effort finds a set of smaller red-band SSA and larger near-infrared-band SSA for VIIRS compared with the MODIS heritage. The VIIRS LAI/FPAR is evaluated through comparisons with one year of MODIS product in terms of both spatial and temporal patterns. Further validation efforts are still necessary to ensure the product quality. Current results, however, imbue confidence in the VIIRS data set and suggest that the efforts described here meet the goal of achieving the operationally consistent multisensor LAI/FPAR data sets. Moreover, the strategies of parametric adjustment and LAI/FPAR evaluation applied to SNPP-VIIRS can also be employed to the subsequent Joint Polar Satellite System VIIRS or other instruments.

Using airborne full-waveform LiDAR metrics derived by 3-D tree segmentation, this study estimated single tree's diameter at breast height (DBH) and stem volume (STV). Four regression models were used, including multilinear regression and three up-to-date regression models (i.e., least square boosting trees regression, random forest, and ε-support vector regression) from the machine learning field. This study aimed to comparatively evaluate these regression models in predicting DBH and STV at single-tree level and find some clues to regression model's selection. The study sites were located in the Bavarian Forest National Park, Germany, a mixed temperate mountain forest. Our comparisons were performed across different tree species types (coniferous and deciduous) and foliage conditions (leaf-on/leaf-off seasons). The importance of predictor variables was also examined. Experimental results revealed that the best accuracy from machine learning methods outperformed the multilinear model by 1.5 cm for DBH and 0.18 m3for STV in terms of rmse. Through comparative analysis, our work provided some clues to the performance variation of regression models for extracting 3-D tree parameters.

In this study, we developed the Allometric Scaling and Resource Limitations (ASRL) model by using the best GLAS tree heights to optimize the ASRL. At first, we obtained the best metric of GLAS tree heights by comparing with LVIS tree heights in six sites. Then, the best metric GLAS tree heights were separately used to optimize ASRL model and test the accuracy of prediction heights from optimized ASRL model in sites scale and country scale. Validation result showed that predicted tree heights from optimized ASRL model had high accuracy.

LiDAR data contains the elevation and brightness information of land surface, vegetation cover and construction. Ground filtering and interpolation method are the key for extracting the DEM accuracy based on the point clouds. This paper takes Zhangye City, Gansu Province in western mountainous areas as the study area, based on the point clouds of 0.7 points/m2, uses 5 m * 5 m grid screening method and the lowest Thiessen polygon point screening method to extract the ground point. Ordinary kriging interpolation method was used to retrieve Digital Elevation Model (DEM). Referring to the elevations of 1466 sample points, we analysed the accuracy for extracting DEM by the two selected methods of extracting ground point. The results showed that the DEM extracting accuracy by the near lowest point screening method is better than the grid screening method.

A quantitative index is applied to monitor crop growth and predict agricultural yield in continental USA. The Climate-Variability Impact Index (CVII), defined as the monthly contribution to overall anomalies in growth during a given year, is derived from 1-km MODIS Leaf Area Index. The growing-season integrated CVII can provide an estimate of the fractional change in overall growth during a given year. In turn these estimates can provide fine-scale and aggregated information on yield for various crops. Trained from historical records of crop production, a statistical model is used to produce crop yield during the growing season based upon the strong positive relationship between crop yield and the CVII. By examining the model prediction as a function of time, it is possible to determine when the in-season predictive capability plateaus and which months provide the greatest predictive capacity.

Ecological and climate models require high-quality consistent biophysical parameters as inputs and validation sources. NASA's moderate resolution imaging spectroradiometer (MODIS) biophysical products provide such data and have been used to improve our understanding of climate and ecosystem changes. However, the MODIS time series contains occasional lower quality data, gaps from persistent clouds, cloud contamination, and other gaps. Many modeling efforts, such as those used in the North American Carbon Program, that use MODIS data as inputs require gap-free data. This letter presents the algorithm used within the MODIS production facility to produce temporally smoothed and spatially continuous biophysical data for such modeling applications. We demonstrate the algorithm with an example from the MODIS-leaf-area-index (LAI) product. Results show that the smoothed LAI agrees with high-quality MODIS LAI very well. Higher R-squares and better linear relationships have been observed when high-quality retrieval in each individual tile reaches 40% or more. These smoothed products show similar data quality to MODIS high-quality data and, therefore, can be substituted for low-quality retrievals or data gaps.

We have developed and tested a physically based approach, rooted in the theory of stochastic radiative transfer within vegetation, for generating a seamless time series of LAI and FPAR fields from AVHRR NDVI data and MODIS reflectance data. Our goal is to: (i) develop a theoretical formulation that results in consistent LAI and FPAR fields from satellite data of varying spectral and spatial resolutions, and (ii) assess the uncertainties in the generated fields due to differences in quality and information content of AVHRR and MODIS sensors. Our results suggests that single scattering albedo and overall relative uncertainty of input surface reflectances and/ or NDVI are configurable parameters in governing the accuracy of LAI/FPAR retrievals from NDVI as an input. Single scattering albedo accounts for differences in both spatial and spectral resolution and the implementation of the algorithm is performed over test areas such that: (i) the consistency requirements are met, (ii) the retrieval index is maximized, and (iii) the difference between reference LAI and LAI obtained from AVHRR NDVI is minimized.

Recent empirical studies have shown that multi-angle data can be useful for predicting canopy height, but the physical reason for this correlation was not understood. The research presented here puts forth a physical explanation for this phenomenon. We employ the use of Radiative Transfer, more specifically canopy spectral invariants, which can decouple spectral and structural parameters in a vegetation canopy. As a case study we compare canopy heights predicted from a multivariate analysis of 28 (7 cameras* 4 bands) and LVIS canopy heights and a multivariate analysis of 7 directional escape probabilities and LVIS canopy heights. We find that the 7 directional escape probabilities can provide approximately the same amount of information about canopy height as 28 spectral/angular reflectances from AirMISR. Finally we speculate that multi-angle data does not allow for extraction of canopy height but in fact requires synergy between Lidar sensors.

The metabolism and phenology of Amazon rainforests significantly influence global dynamics of climate, carbon and water, but remain poorly understood. In this study we utilized Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial ecosystem variables to analyze Amazon rainforest dynamics utilizing the satellite products; vegetation indices (VI), leaf area index (LAI), fraction of absorbed photosynthetically- active radiation (FPAR), and gross primary production (GPP). We found the MODIS products to greatly facilitate analyses and monitoring of ecosystem metabolism in both intact and disturbed rainforests.

This study investigates the representativeness of land cover and leaf area index (LAI) sampled by a global network of sites to be used for the evaluation of land biophysical products, such as LAI or fAPAR, derived from current satellite systems. The networks of sites considered include 100 sites where ground measurements of LAI or fAPAR have been performed for the validation of medium resolution satellite land biophysical products, 188 FLUXNET sites and 52 AERONET sites. All the sites retained had less than 25% of water bodies within a 8times8 km2window, and were separated by more than 20 km. The ECOCLIMAP global classification was used to quantify the representativeness of the networks. It allowed describing the Earth's surface with seven main types and proposed a climatology for monthly LAI values at a spatial resolution around 1 km. The site distribution indicates a large over representation of the northern midlatitudes relative to other regions, and an under-representation of bare surfaces, grass, and evergreen broadleaf forests. These three networks represent all together 295 sites after elimination of sites that were too close. They were thus completed by 76 additional sites to improve the representativeness in latitude, longitude, and surface type. This constitutes the BELMANIP network proposed as a benchmark for intercomparison of land biophysical products. Suitable approaches to conducting intercomparison at the sites are recommended

The validation of moderate-resolution satellite leaf area index (LAI) products such as those operationally generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor data requires reference LAI maps developed from field LAI measurements and fine-resolution satellite data. Errors in field measurements and satellite data determine the accuracy of the reference LAI maps. This paper describes a method by which reference maps of known accuracy can be generated with knowledge of errors in fine-resolution satellite data. The method is demonstrated with data from an international field campaign in a boreal coniferous forest in northern Sweden, and Enhanced Thematic Mapper Plus images. The reference LAI map thus generated is used to assess modifications to the MODIS LAI/fPAR algorithm recently implemented to derive the next generation of the MODIS LAI/fPAR product for this important biome type

Initiated in 1984, the Committee Earth Observing Satellites' Working Group on Calibration and Validation (CEOS WGCV) pursues activities to coordinate, standardize and advance calibration and validation of civilian satellites and their data. One subgroup of CEOS WGCV, Land Product Validation (LPV), was established in 2000 to define standard validation guidelines and protocols and to foster data and information exchange relevant to the validation of land products. Since then, a number of leaf area index (LAI) products have become available to the science community at both global and regional extents. Having multiple global LAI products and multiple, disparate validation activities related to these products presents the opportunity to realize efficiency through international collaboration. So the LPV subgroup established an international LAI intercomparison validation activity. This paper describes the main components of this international validation effort. The paper documents the current participants, their ground LAI measurements and scaling techniques, and the metadata and infrastructure established to share data. The paper concludes by describing plans for sharing both field data and high-resolution LAI products from each site. Many considerations of this global LAI intercomparison can apply to other products, and this paper presents a framework for such collaboration

The analysis of two years of Collection 3 and five years of Collection 4 Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) data sets is presented in this article with the goal of understanding product quality with respect to version (Collection 3 versus 4), algorithm (main versus backup), snow (snow-free versus snow on the ground), and cloud (cloud-free versus cloudy) conditions. Retrievals from the main radiative transfer algorithm increased from 55% in Collection 3 to 67% in Collection 4 due to algorithm refinements and improved inputs. Anomalously high LAI/FPAR values observed in Collection 3 product in some vegetation types were corrected in Collection 4. The problem of reflectance saturation and too few main algorithm retrievals in broadleaf forests persisted in Collection 4. The spurious seasonality in needleleaf LAI/FPAR fields was traced to fewer reliable input data and retrievals during the boreal winter period. About 97% of the snow covered pixels were processed by the backup Normalized Difference Vegetation Index-based algorithm. Similarly, a majority of retrievals under cloudy conditions were obtained from the backup algorithm. For these reasons, the users are advised to consult the quality flags accompanying the LAI and FPAR product.

Global products of vegetation green Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are being operationally produced from Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS) at 1-km resolution and eight-day frequency. This paper summarizes the experience of several collaborating investigators on validation of MODIS LAI products and demonstrates the close connection between product validation and algorithm refinement activities. The validation of moderate resolution LAI products includes three steps: 1) field sampling representative of LAI spatial distribution and dynamic range within each major land cover type at the validation site; 2) development of a transfer function between field LAI measurements and high resolution satellite data to generate a reference LAI map over an extended area; and 3) comparison of MODIS LAI with aggregated reference LAI map at patch (multipixel) scale in view of geo-location and pixel shift uncertainties. The MODIS LAI validation experiences, summarized here, suggest three key factors that influence the accuracy of LAI retrievals: 1) uncertainties in input land cover data, 2) uncertainties in input surface reflectances, and 3) uncertainties from the model used to build the look-up tables accompanying the algorithm. This strategy of validation efforts guiding algorithm refinements has led to progressively more accurate LAI products from the MODIS sensors aboard NASA's Terra and Aqua platforms

Broadleaf forest is a major type of Earth's land cover with the highest observable vegetation density. Retrievals of biophysical parameters, such as leaf area index (LAI), of broadleaf forests at global scale constitute a major challenge to modern remote sensing techniques in view of low sensitivity (saturation) of surface reflectances to such parameters over dense vegetation. The goal of the performed research is to demonstrate physical principles of LAI retrievals over broadleaf forests with the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI algorithm and to establish a basis for algorithm refinement. To sample natural variability in biophysical parameters of broadleaf forests, we selected MODIS data subsets covering deciduous broadleaf forests of the eastern part of North America and evergreen broadleaf forests of Amazonia. The analysis of an annual course of the Terra MODIS Collection 4 LAI product over broadleaf forests indicated a low portion of best quality main radiative transfer-based algorithm retrievals and dominance of low-reliable backup algorithm retrievals during the growing season. We found that this retrieval anomaly was due to an inconsistency between simulated and MODIS surface reflectances. LAI retrievals over dense vegetation are mostly performed over a compact location in the spectral space of saturated surface reflectances, which need to be accurately modeled. New simulations were performed with the stochastic radiative transfer model, which poses high numerical accuracy at the condition of saturation. Separate sets of parameters of the LAI algorithm were generated for deciduous and evergreen broadleaf forests to account for the differences in the corresponding surface reflectance properties. The optimized algorithm closely captures physics of seasonal variations in surface reflectances and delivers a majority of LAI retrievals during a phenological cycle, consistent with field measurements. The analysis of the optimized retrievals indicates that the precision of MODIS surface reflectances, the natural variability, and mixture of species set a limit to improvements of the accuracy of LAI retrievals over broadleaf forests.

This paper reports on the analysis of Pathfinder AVHRR land (PAL) data set that spans the period July 1981 to September 1994. The time series of normalized difference vegetation index (NDVI) data for land areas north of 45/spl deg/ N assembled by correcting the PAL data with spectral methods confirms the northerly greening trend and extension of the photosynthetically active growing season. Analysis of the channel reflectance data indicates that the interannual changes in red and near-infrared reflectances are similar to seasonal changes in the spring time period when green leaf area increases and photosynthetic activity ramps up. Model calculations and theoretical analysis of the sensitivity of NDVI to background reflectance variations confirm the hypothesis that warming driven reductions in snow cover extent and earlier onset of greening are responsible for the observed changes in spectral reflectances over vegetated land areas.

This paper presents empirical and theoretical analyses of spectral hemispherical reflectances and transmittances of individual leaves and the entire canopy sampled at two sites representative of equatorial rainforests and temperate coniferous forests. The empirical analysis indicates that some simple algebraic combinations of leaf and canopy spectral transmittances and reflectances eliminate their dependencies on wavelength through the specification of two canopy-specific wavelength-independent variables. These variables and leaf optical properties govern the energy conservation in vegetation canopies at any given wavelength of the solar spectrum. The presented theoretical development indicates these canopy-specific wavelength-independent variables characterize the capacity of the canopy to intercept and transmit solar radiation under two extreme situations, namely, when individual leaves 1) are completely absorptive and 2) totally reflect and/or transmit the incident radiation. The interactions of photons with the canopy at red and near-infrared (IR) spectral bands approximate these extreme situations well. One can treat the vegetation canopy as a dynamical system and the canopy spectral interception and transmission as dynamical variables. The system has two independent states: canopies with totally absorbing and totally scattering leaves. Intermediate states are a superposition of these pure states. Such an interpretation provides powerful means to accurately specify changes in canopy structure both from ground-based measurements and remotely sensed data. This concept underlies the operational algorithm of global leaf area index (LAI), and the fraction of photosynthetically active radiation absorbed by vegetation developed for the moderate resolution imaging spectroradiometer (MODIS) and multiangle imaging spectroradiometer (MISR) instruments of the Earth Observing System (EOS) Terra mission.

This paper assesses the effect of changes in solar zenith angle (SZA) and sensor changes on reflectances in channel 1, channel 2, and normalized difference vegetation index (NDVI) from the advanced very high resolution radiometer (AVHRR) Pathfinder land data set for the period July 1981 through September 1994. First, the effect of changes in SZA on channel reflectances and NDVI is derived from equations of radiative transfer in vegetation media. Starting from first principles, it is rigorously shown that the NDVI of a vegetated surface is a function of the maximum positive eigenvalue of the radiative transfer equation within the framework of the theory used and its assumptions. A sensitivity analysis of this relation indicates that NDVI is minimally sensitive to SZA changes, and this sensitivity decreases as leaf area increases. Second, statistical methods are used to analyze the relationship between SZA and channel reflectances or NDVI. It is shown that the use of ordinary least squares can generate spurious regressions because of the nonstationary property of time series. To avoid such a confusion, the authors use the notion of cointegration to analyze the relation between SZA and AVHRR data. Results are consistent with the conclusion of theoretical analysis from equations of radiative transfer. NDVI is not related to SZA in a statistically significant manner except for biomes with relatively low leaf area. From the theoretical and empirical analysis, they conclude that the NDVI data generated from the AVHRR Pathfinder land data set are not contaminated by trends introduced from changes in solar zenith angle due to orbital decay and changes in satellites (NOAA-7, 9, 11). As such, the NDVI data can be used to analyze interannual variability of global vegetation activity.

This paper describes results from prototyping of the moderate resolution imaging spectroradiometer (MODIS) radiative transfer-based synergistic algorithm for the estimation of global leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation using land surface reflectances (LASUR) and Landsat data. The algorithm uses multispectral surface reflectances and a land cover classification map as input data to retrieve global LAI and FPAR fields. The authors' objectives are to evaluate its performance as a function of spatial resolution and uncertainties in surface reflectances and the land cover map. They analyzed reasons the algorithm can or cannot retrieve a value of LAI/FPAR from the reflectance data and justified the use of more complex algorithms, instead of NDVI-based methods. The algorithm was tested to investigate the effects of vegetation misclassification on LAI/FPAR retrievals. Misclassification between distinct biomes can fatally impact the quality of the retrieval, while the impact of misclassification between spectrally similar biomes is negligible. Comparisons of results from the coarse and fine resolution retrievals show that the algorithm is dependent on the spatial resolution of the data. By evaluating the data density distribution function, they can adjust the algorithm for data resolution and utilize the algorithm with data from other sensors.

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 vegetation canopy green leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by vegetation (FPAR). These products are produced using a peer-reviewed algorithm documented in the EOS-AM1 (Terra) special issue of the Journal of Geophysical Research. This paper presents results on spatial distributions of LAI and FPAR of vegetated land surfaces derived from the MISR LAI/FPAR algorithm with bidirectional reflectance data from the polarization and directionality of the Earth's reflectance (POLDER) instrument over Africa. The results indicate that the proposed algorithm reflects the physical relationships between surface reflectances and biophysical parameters and demonstrates the advantages of using multiangle data instead of single-angle data. A new method for evaluating bihemispherical reflectance (BHR) from multi-angle measurements of hemispherical directional reflectance factor (HDRF) was developed to prototype the algorithm with POLDER data. The accuracy of BHR evaluation and LAI/FPAR estimation is also presented. To authors demonstrate the advantages of using multi-angle data over single-angle data of surface reflectance.

The Multi-angle Imaging SpectroRadiometer (MISR) instrument is scheduled for launch aboard the first of the Earth Observing System (EOS) spacecraft, EOS-AM1. MISR will provide global, radiometrically calibrated, georectified, and spatially coregistered imagery at nine discrete viewing angles and four visible/near-infrared spectral bands. Algorithms specifically developed to capitalize on this measurement strategy will be used to retrieve geophysical products for studies of clouds, aerosols, and surface radiation. This paper provides an overview of the as-built instrument characteristics and the application of MISR to remote sensing of the Earth.

The first Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is planned for launch by NASA in 1998. This instrument will provide a new and improved capability for terrestrial satellite remote sensing aimed at meeting the needs of global change research. The MODIS standard products will provide new and improved tools for moderate resolution land surface monitoring. These higher order data products have been designed to remove the burden of certain common types of data processing from the user community and meet the more general needs of global-to-regional monitoring, modeling, and assessment. The near-daily coverage of moderate resolution data from MODIS, coupled with the planned increase in high-resolution sampling from Landsat 7, will provide a powerful combination of observations. The full potential of MODIS will be realized once a stable and well-calibrated time-series of multispectral data has been established. In this paper the proposed MODIS standard products for land applications are described along with the current plans for data quality assessment and product validation.

Knowledge of the directional and hemispherical reflectance properties of natural surfaces, such as soils and vegetation canopies, is essential for classification studies and canopy model inversion. The Multi-angle Imaging SpectroRadiometer (MISR), an instrument to be launched in 1998 onboard the EOS-AM1 platform, will make global observations of the Earth's surface at 1.1-km spatial resolution, with the objective of determining the atmospherically corrected reflectance properties of most of the land surface and the tropical ocean. The algorithms to retrieve surface directional reflectances, albedos, and selected biophysical parameters using MISR data are described. Since part of the MISR data analyses includes an aerosol retrieval, it is assumed that the optical properties of the atmosphere (i.e. aerosol characteristics) have been determined well enough to accurately model the radiative transfer process. The core surface retrieval algorithms are tested on simulated MISR data, computed using realistic surface reflectance and aerosol models, and the sensitivity of the retrieved directional and hemispherical reflectances to aerosol type and column amount is illustrated. Included is a summary list of the MISR surface products.

A simple method for the estimation of global leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by the vegetation (FAPAR) from atmospherically corrected Normalized Difference Vegetation Index (NDVI) observations is described. Recent improvements to the authors' three dimensional radiative transfer model of a vegetated surface are described. Example simulation results and a validation exercise are discussed. The model was utilized to derive land cover specific NDVI-LAI and NDVI-FAPAR relations. The method therefore requires stratification of global vegetation into cover types that are compatible with the radiative transfer model. Such a classification based on vegetation structure is proposed and a simple method for its derivation is presented. Proof-of-concept results are given to illustrate the feasibility of the proposed method.

The sensitivity of grassland bidirectional reflectance to soil, vegetation, irradiance, and sensor parameters is assessed. Based on these results, a vegetation bidirectional reflectance distribution function (BRDF) model is inverted with ground reflectance data from the First ISLSCP Field Experiment (FIFE). Results suggest leaf area index (LAI) is most accurately retrieved from data gathered in near-infrared bands at low solar zenith angles (SZA), and leaf angle distribution is best retrieved from data gathered in near-infrared bands at SZA. Generally, leaf optical properties are more accurately estimated from data acquired at high SZA. Canopy albedo and fraction of absorbed photosynthetically active radiation (fAPAR) are also estimated and compared to measured values. Albedo estimates are accurate to about /spl plusmn/0.01 (4% relative) when model parameters are determined from reflectance data gathered under preferred conditions. Estimates of fAPAR are less accurate. These results provide a guide for efficiently sampling surface reflectance and accurately retrieving parameters for use in climate ecosystem models.

Empirical studies report several plausible correlations between transforms of spectral reflectance, called vegetation indexes, and parameters descriptive of vegetation leaf area, biomass and physiological functioning. However, most indexes can be generalized to show a derivative of surface reflectance with respect to wavelength. This derivative is a function of the optical properties of leaves and soil particles. In the case of optically dense vegetation, the spectral derivative, and thus the indexes, can be rigorously shown to be indicative of the abundance and activity of the absorbers in the leaves. Therefore, the widely used broad-band &near-infrared vegetation indexes are a measure of chlorophyll abundance and energy absorption.

The problem of remotely sensing the amount of solar radiation absorbed and reflected by vegetated land surfaces was investigated with the aid of one- and three-dimensional radiative transfer models. Desert vegetation was modeled as clumps of leaves randomly distributed on a bright dry soil with a ground cover of generally less than 100%. Surface albedo (ALB), fraction of photosynthetically active radiation absorbed by the canopy (FAPAR), fractions of solar radiation absorbed by the canopy (FASOLAR) and soil (FASOIL), and normalized difference vegetation index (NDVI) were calculated for various illumination conditions. The magnitude of errors involved in the estimation of surface albedo from broadband monodirectional measurements was assessed. The nature of the relationships between NDVI vs. FASOLAR, FAPAR, FASOIL, and ALB and their sensitivity to all problem parameters were investigated in order to develop simple predictive models. The relationship between NDVI measured above the atmosphere and that sensed above the canopy at the ground surface was studied to characterize atmospheric effects.>