Leaf Area Index Maps Research Articles

A Bayesian network algorithm for retrieving the characterization of land surface vegetation

A hybrid inversion technique based on Bayesian network is proposed for estimating the biochemical and biophysical parameters of land surface vegetation from remotely sensed data. A Bayesian network is a unified knowledge-inferring process that can incorporate information derived from multiple sources including remote sensing and information derived from a priori knowledge. Using this inversion approach, content of chlorophyll a and chlorophyll b (Cab) and leaf area index (LAI) of winter wheat were estimated from data derived from simulations as well as field measurements. Estimations from the simulated data proved accurate, with root mean square errors (RMSEs) of 0.54 m 2/m 2 in LAI and 4.5 μg/cm 2 in Cab. In validating the estimates against field measurements, it was found that prior knowledge of target parameters improved the accuracy of estimates, in terms of RMSEs from 0.73 to 0.22 m 2/m 2 in LAI and 9.6 to 4.0 μg/cm 2 in Cab. Bayesian inference in this hybrid inversion scheme produces a posterior probability distribution, which can reveal such properties of the inferred results as updated information contained in the inversion result. Using entropy, the revision of posterior information about the parameters of interest was calculated. Including more data may allow more information to be retrieved about parameters in general. Exceptions were also observed where data from some viewing angles slightly reduced the information on the parameters of interest. It was also found that data from these viewing angles were less sensitive to the parameters. The method proposed here was also validated using LandSat ETM+ imagery provided by the BigFoot project. When used for mapping LAI with ETM+ imagery, the proposed method with an RMSE of 0.70 and a correlation of 0.67 produced a slightly better result than that from empirical regression.

Evaluation of MODIS Land Cover and LAI Products in Cropland of North China Plain Using In Situ Measurements and Landsat TM Images

Global maps of land cover and leaf area index (LAI) from the moderate resolution imaging spectroradiometer (MODIS) are available from the earth resources observation system data center. Validation of these MODIS terrestrial products is crucial for scientific research. Numerous pre- and postlaunch validation activities have been completed during the last several years. However, such work was mainly located in North America, Europe, and Africa. The validated vegetation types were principally focused on woodland and savanna. In this paper, we evaluated the Collection 3 MODIS land cover (MOD12Q1) and the Collection 4 MODIS LAI (MOD15A2) products in the North China Plain (NCP). We placed the emphasis on croplands by: (1) developing an accurate land cover map, based on the MODIS LAI/fraction of photosynthetically active radiation land cover classification scheme; (2) deriving empirical LAI validation maps, based on <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ </i> measurement and landsat thematic mapper imagery; and (3) conducting an initial MODIS validation exercise to assess the quality of MODIS products. The results indicate that an apparent misclassification exists between grasses/cereal crop and broadleaf crop biomes in the MODIS land cover product. Although MODIS LAI could resemble the general pattern of the LAI seasonal vegetation variation in the NCP, the MODIS LAI algorithm markedly underestimated LAI values (down to 2-3 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) for the April 2004 winter wheat cropland. Biome misclassification and the atmospheric effect of cloud or aerosol were proven to decrease the value of MODIS LAI, but these impacts cannot fully explain the significant underestimation. Additional studies are needed to understand the main reasons for this underestimation in the study area.

Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data

The derivation of leaf area index (LAI) from satellite optical data has been the subject of a large amount of work. In contrast, few papers have addressed the effective model inversion of high resolution satellite images for a complete series of data for the various crop species in a given region. The present study is focused on the assessment of a LAI model inversion approach applied to multitemporal optical data, over an agricultural region having various crop types with different crop calendars. Both the inversion approach and data sources are chosen because of their wide use. Crops in the study region (Barrax, Castilla–La Mancha, Spain) include: cereal, corn, alfalfa, sugar beet, onion, garlic, papaver. Some of the crop types (onion, garlic, papaver) have not been addressed in previous studies. We use in-situ measurement sets and literature values as a priori data in the PROSPECT + SAIL models to produce Look Up Tables (LUTs). Those LUTs are subsequently used to invert Landsat-TM and Landsat-ETM+ image series (12 dates from March to September 2003). The Look Up Tables are adapted to different crop types, identified on the images by ground survey and by Landsat classification. The retrieved LAI values are compared to in-situ measurements available from the campaign conducted in mid July-2003. Very good agreement (a high linear correlation) is obtained for LAI values from 0.1 to 6.0. LAI maps are then produced for each of the 12 dates. The LAI temporal variation shows consistency with the crop phenological stages. The inversion method is favourably compared to a method relying on the empirical relationship between LAI and NDVI from Landsat data. This offers perspectives for future optical satellite data that will ensure high resolution and high temporal frequency.

The effect of spatial resolution on the accuracy of leaf area index estimation for a forest planted in the desert transition zone

An approach is presented for determining leaf area index (LAI) of a forest located at the desert fringe by using high spatial resolution imagery and by implementing values from a moderate spatial but high temporal resolution sensor. A 4-m spatial resolution multi-spectral IKONOS image was acquired under clear sky conditions on March 25, 2004. Normalized differences vegetation index (NDVI) and a linear mixture model were applied to calculate fractional vegetation cover (FVC). LAI was calculated using a non-linear relationship to FVC and then compared with ground truth measurements made in ten 1000 m 2 plots using the tracing radiation and architecture of canopies (TRAC) canopy analyzer under bright and clear sky conditions during March and April, 2004. Calculated LAI, corrected with a measured clumping index, was highly correlated with measured LAI ( R 2 = 0.79, p < 0.01). This approach was used to produce a 4-m resolution LAI map of the forest. The procedure was then applied to the MODIS 250-m resolution surface reflectance product, where MODIS LAI and VI products were used to calculate the extinction coefficient by inversion of the LAI-FVC relationship, and the extinction coefficient was then used to calculate LAI for moderate resolution. Histograms of resulting LAI distributions and descriptive statistics at the different spatial resolutions are compared. LAI spatial distribution at lower resolution was similar to that obtained at higher resolution and remained close to being normally distributed.

Mapping tree and shrub leaf area indices in an ombrotrophic peatland through multiple endmember spectral unmixing

Leaf area index (LAI) is an important parameter used by most process-oriented ecosystem models. LAI of forest ecosystems has routinely been mapped using spectral vegetation indices (SVI) derived from remote sensing imagery. The application of SVI-based approaches to map LAI in peatlands presents a challenge, mainly due to peatlands characteristic multi-layer canopy comprising shrubs and open, discontinuous tree canopies underlain by a continuous ground cover of different moss species, which reduces the greenness contrast between the canopy and the background. Our goal is to develop a methodology to map tree and shrub LAI in peatlands and similar ecosystems based on multiple endmember spectral mixture analysis (MESMA). This new mapping method is validated using LAI field measurements from a precipitation-fed (ombrotrophic) peatland near Ottawa, Ontario, Canada. We demonstrate first that three commonly applied SVI are not suitable for tree and shrub LAI mapping in ombrotrophic peatlands. Secondly, we demonstrate for a three-endmember model the limitations of traditional linear spectral mixture analysis (SMA) due to the unique and widely varying spectral characteristics of Sphagnum mosses, which are significantly different from vascular plants. Next, by using a geometric-optical radiative transfer model, we determine the nature of the equation describing the empirical relationship between shadow fraction and tree LAI using nonlinear ordinary least square (OLS) regression. We then apply this equation to describe the empirical relationships between shadow and shrub fractions obtained from mixture decomposition with SMA and MESMA, respectively, and tree and shrub LAI, respectively. Less accurate fractions obtained from SMA result in weaker relationships between shadow fraction and tree LAI ( R 2 = 0.61) and shrub fraction and shrub LAI ( R 2 = 0.49) compared to the same relationships based on fractions obtained from MESMA with R 2 = 0.75 and R 2 = 0.68, respectively. Cross-validation of tree LAI ( R 2 = 0.74; RMSE = 0.48) and shrub LAI ( R 2 = 0.68; RMSE = 0.42) maps using fractions from MESMA shows the suitability of this approach for mapping tree and shrub LAI in ombrotrophic peatlands. The ability to account for a spectrally varying, unique Sphagnum moss ground cover during mixture decomposition and a two layer canopy is particularly important.

Leaf area index estimates obtained for mixed forest using hemispherical photography and HyMap data

The Leaf Area Index (LAI) is an important measure in many ecological applications because vegetation-atmosphere processes of the canopy, such as photosynthesis are controlled by the foliage and play an essential role in the carbon cycle. Therefore accurate determination of LAI is of great interest. Forest LAI is difficult to estimate due to the complex structure of the canopy and its high variability. Previous studies have shown that hemispherical photography is a useful technique to determine LAI by involving different gap fraction models but exposure seems to affect LAI estimates. Hemispherical photography and LAI-2000 plant canopy analyzer ground measurements were taken to capture the LAI at selected plots in the study site. The photographs were captured with two different exposure settings, namely manual and automatic, to examine the effects on LAI. Subsequently, we analyzed the photographs with the Software Hemisfer that allows the calculation of LAI with five different mathematical methods. Hemisfer was developed by the Swiss Federal Research Institute WSL. In order to obtain an LAI map of the study area, data of the airborne imaging spectrometer HyMap were used, acquired in summer 2004. Three images were recorded approximately at the same time, whereas two of them were North-South oriented and the third perpendicular to them. All HyMap data were preprocessed and across- track illumination variations were corrected. Six two-band Vegetation indices (eg. PVI, SAVI2) were exploited by developing regression models between ground-based LAI and VI’s. The VI with the best performance was then applied on HyMap data. The objectives of this study were (1) the evaluation of different mathematical methods to calculate LAI from hemispherical photographs, (2) the investigation of camera exposure influencing LAI estimates and (3) the examination of illumination effects on LAI when applying VI’s on HyMap data. The evaluation of the five different LAI calculation methods from hemispherical photographs showed that the coefficients of variation ranged from 10.69 % to 15.43 %. LAI derived with manual camera exposure settings correlated better (R2=0.97) with LAI-2000 values than LAI from automatically exposed photographs (R2=0.85). A comparison of the VI’s showed that good results were achieved with SAVI2 as well as with PVI. Investigating illumination effects on LAI indicated that although a correction has been performed, influences on LAI can still be observed.

Accuracy of Neural Network and Regression Leaf Area Estimators for the Amazon Basin

The ability to map and monitor leaf area index throughout the Amazon Basin is important to assess and predict environmental degradation. This study measured leaf area index (LAI) values at 64 locations characteristic of many different cover types including mature forest, secondary succession, pasture, cropped land, barren land, and urban area throughout the Santarem, Brazil area. The field data were used in conjunction with ASTER visible and infrared data to compare the accuracy of multiple regression and artificial neural network models for LAI prediction. Assessments of model accuracy were determined by calculating RMSE. Results indicate that artificial neural networks are more accurate than traditional statistical techniques to estimate LAI in this part of the Amazon Basin. In addition, it was found that lower LAI values modeled the poorest because of a mix of spectral signatures.

Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America

A new set of recently developed leaf area index (LAI) algorithms has been employed for producing a global LAI dataset at 1 km resolution and in time-steps of 10 days, using data from the Satellite pour l'observation de la terre (SPOT) VEGETATION (VGT) sensor. In this paper, this new LAI product is compared with the global MODIS Collection 4 LAI product over four validation sites in North America. The accuracy of both LAI products is assessed against seven high resolution ETM+ LAI maps derived from field measurements in 2000, 2001, and 2003. Both products were closely matched outside growing season. The MODIS product tended to be more variable than the VGT product during the summer period when the LAI was maximum. VGT and ETM+ LAI maps agreed well at three out of the four sites. The median relative absolute error of the VGT LAI product varied from 24% to 75% at 1 km scale and it ranged from 34% to 88% for the MODIS LAI product. The importance of correcting field measurements for the clumping effect is illustrated at the deciduous broadleaf forest site (HARV). Inclusion of the sub-pixel land cover information improved the quality of LAI estimates for the prairie grassland KONZ site. Further improvement of the global VGT LAI product is suggested by production and inclusion of pixel-specific global foliage clumping index and forest background reflectance maps that would serve as an input into the VGT LAI algorithms.

An integrated Earth sensing sensorweb for improved crop and rangeland yield predictions

This paper outlines research and development towards an integrated Earth sensing sensorweb system for improved crop and rangeland yield predictions. The paper introduces the concepts of integrated Earth sensing and in situ sensorwebs and describes the key aspects and innovations of an intelligent sensorweb for integrated Earth sensing (ISIES). ISIES incorporates a sensorweb that provides automatic and continuous in situ measurements and advanced crop growth models and leading-edge sensorweb-enabled OpenGIS-compliant Web services. A key component of each in situ sensorweb node is SmartCore, a compact device developed to control sensor data traffic autonomously and to communicate wirelessly in two-way mode with the ISIES central server. The system server automatically integrates the in situ sensorweb data with remote sensing data and crop models to provide maps of leaf area index and soil moisture and biomass and improved predictions of crop and rangeland yield.

View angle effects on relationships between MISR vegetation indices and leaf area index in a recently burned ponderosa pine forest

While nadir-viewing passive multispectral sensors have limited utility for characterizing the full dimensionality of forest canopies, multi-angle remote sensors such as the Multi-angle Imaging SpectroRadiometer (MISR) may improve detection of canopy architecture, canopy cover, and leaf area index (LAI) of forest canopy versus understory vegetation. Our objective was to determine whether data from the MISR sensor could improve estimates of LAI across a post-fire ponderosa pine forest located in the Black Hills of South Dakota. We measured LAI during the 2002 and 2003 growing seasons and created continuous LAI maps using Landsat TM and ETM+ data (mean R 2 = 0.81). We fit linear regression models of total and canopy LAI, using a series of MISR at-nadir and off-nadir or anisotropic vegetation indices as predictor variables, for each of five sampling periods. We found the best LAI model fits using either a new hotspot-adjusted normalized difference vegetation index, NDVI HS, NDVI calculated from the − 60° view angle, or NDVI at-nadir and either the Hotspot–DarkSpot Index (HDS) or the normalized difference anisotrophic index (NDAX) ( R 2 = 0.56–0.91). However, differences in fits of these best models and those including NDVI at-nadir ranged from only 1 to 8%. Reflectance anisotropy patterns related strongly to understory vegetation phenology. We found that the relationships among NDVI or the enhanced vegetation index (EVI) and canopy or total LAI showed little variation across view angles when the understory vegetation was senesced and significant anisotropy when understory green LAI was greatest. These findings demonstrate the value of multi-temporal measurements during periods of understory phenological change, even if the overstory LAI is relatively stable. We also evaluated the performance of the MISR LAI product and found moderate fits between the MISR LAI and our field- and Landsat-derived canopy LAI ( R 2 = 0.21–0.44).

Combining remote sensing imagery and forest age inventory for biomass mapping

Aboveground biomass (AGB) of forests is an important component of the global carbon cycle. In this study, Landsat ETM + images and field forest inventory data were used to estimate AGB of forests in Liping County, Guizhou Province, China. Three different vegetation indices, including simple ratio (SR), reduced simple ratio (RSR), and normalized difference vegetation index (NDVI), were calculated from atmospherically corrected ETM + reflectance images. A leaf area index (LAI) map was produced from the RSR map using a regression model based on measured LAI and RSR. The LAI map was then used to develop an initial AGB map, from which forest stand age was deduced. Vegetation indices, LAI, and forest stand age were together used to develop AGB estimation models for different forest types through a stepwise regression analysis. Significant predictors of AGB changed with forest types. LAI and NDVI were significant predictors of AGB for Chinese fir ( R 2=0.93). The model using LAI and stand age as predictors explained 94% of the AGB variance for coniferous forests. Stand age captured 79% of the AGB variance for broadleaved forests ( R 2=0.792). AGB of mixed forests was predicted well by LAI and SR ( R 2=0.931). Without differentiating among forest types, the model with SR and LAI as predictors was able to explain 90% of AGB variances of all forests. In Liping County, AGB shows a strong gradient that increases from northeast to southwest. About 64% of the forests have AGB in the range from 90 to 180 t ha −1.

LAI inversion algorithm based on directional reflectance kernels

Leaf area index (LAI) is an important ecological and environmental parameter. A new LAI algorithm is developed using the principles of ground LAI measurements based on canopy gap fraction. First, the relationship between LAI and gap fraction at various zenith angles is derived from the definition of LAI. Then, the directional gap fraction is acquired from a remote sensing bidirectional reflectance distribution function (BRDF) product. This acquisition is obtained by using a kernel driven model and a large-scale directional gap fraction algorithm. The algorithm has been applied to estimate a LAI distribution in China in mid-July 2002. The ground data acquired from two field experiments in Changbai Mountain and Qilian Mountain were used to validate the algorithm. To resolve the scale discrepancy between high resolution ground observations and low resolution remote sensing data, two TM images with a resolution approaching the size of ground plots were used to relate the coarse resolution LAI map to ground measurements. First, an empirical relationship between the measured LAI and a vegetation index was established. Next, a high resolution LAI map was generated using the relationship. The LAI value of a low resolution pixel was calculated from the area-weighted sum of high resolution LAIs composing the low resolution pixel. The results of this comparison showed that the inversion algorithm has an accuracy of 82%. Factors that may influence the accuracy are also discussed in this paper.

Spatial scaling between leaf area index maps of different resolutions

We developed algorithms for spatial scaling of leaf area index (LAI) using sub-pixel information. The study area is located near Liping County, Guizhou Province, in China. Methods for LAI spatial scaling were investigated on LAI images with 960 m resolution derived in two ways. LAI from distributed calculation (LAID) was derived using Landsat ETM+ data (30 m), and LAI from lumped calculation (LAIL) was obtained from the coarse (960 m) resolution data derived through resampling the ETM+ data. We found that lumped calculations can be considerably biased compared to the distributed (ETM+) case, suggesting that global and regional LAI maps can be biased if surface heterogeneity within the mapping resolution is ignored. Based on these results, we developed algorithms for removing the biases in lumped LAI maps using sub-pixel land cover-type information, and applied these to correct one coarse resolution LAI product which greatly improved its accuracy.

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.

Algorithm for global leaf area index retrieval using satellite imagery

Leaf area index (LAI) is one of the most important Earth surface parameters in modeling ecosystems and their interaction with climate. Based on a geometrical optical model (Four-Scale) and LAI algorithms previously derived for Canada-wide applications, this paper presents a new algorithm for the global retrieval of LAI where the bidirectional reflectance distribution function (BRDF) is considered explicitly in the algorithm and hence removing the need of doing BRDF corrections and normalizations to the input images. The core problem of integrating BRDF into the LAI algorithm is that nonlinear BRDF kernels that are used to relate spectral reflectances to LAI are also LAI dependent, and no analytical solution is found to derive directly LAI from reflectance data. This problem is solved through developing a simple iteration procedure. The relationships between LAI and reflectances of various spectral bands (red, near infrared, and shortwave infrared) are simulated with Four-Scale with a multiple scattering scheme. Based on the model simulations, the key coefficients in the BRDF kernels are fitted with Chebyshev polynomials of the second kind. Spectral indices - the simple ratio and the reduced simple ratio - are used to effectively combine the spectral bands for LAI retrieval. Example regional and global LAI maps are produced. Accuracy assessment on a Canada-wide LAI map is made in comparison with a previously validated 1998 LAI map and ground measurements made in seven Landsat scenes

Evaluation of national and global LAI products derived from optical remote sensing instruments over Canada

Leaf area index (LAI) is an important surface variable for monitoring the status of vegetation and as input in a number of ecosystem process models. There are currently several coarse-resolution LAI maps over Canada, including a Canada Centre for Remote Sensing ten-day, 1-km resolution, Canada-wide product based on SPOT-4 VEGETATION (VGT), a MODIS eight-day, 1-km resolution, global product and a monthly, 7-km resolution, global map produced using POLDER-1. These products are difficult to validate because of their large spatial extent and coarse resolution. In this study we use in situ LAI measurements collected over a wide range of forest types and ecological zones in Canada to derive 30-m resolution reference LAI maps based on robust error-in-measurement regressions to Landsat Enhanced Thematic Mapper Plus vegetation indices. The reference maps and LAI products were aggregated to a coarser resolution (3 km for MODIS and VGT and 7 km for POLDER) before comparison to account for registration errors, and variability in sensor projected point spread functions. Spatially corresponding aggregated pixels with both high-quality reference and coarse scale LAI retrievals were compared. The comparison shows reasonable agreement (biases less than 25% or one LAI) between the VGT and reference LAI. The MODIS LAI product showed weak correlations (R2<0.25) over all sites at the scale of comparison and typically overestimated reference LAI in mixed forests by approximately 200%. The POLDER LAI product, only available in June 1997, showed almost no correlation to the summer reference LAI datasets. It underestimated reference LAI for an early growing season with an extent, in some cases, greater than the seasonal differences in LAI. This independent validation of three large area LAI products suggests that there may be substantial biases due to the lack of regional tuning of retrieval algorithms. These biases are far larger than the uncertainties in the reference-based LAI scenes in the case of the MODIS product. This suggests that reliable LAI maps may require regional calibration to meet the Global Terrestrial Observing System mapping requirements of plusmn15% uncertainties

Leaf area index retrieval using IRS LISS-III sensor data and validation of the MODIS LAI product over central India

This paper reports results on the LAI Retrieval and Validation Experiment (LRVE) that was conducted for two agricultural areas in Central India during the winter season of 2001-2002. The study aimed at relating field measurements of leaf area index (LAI) to spaceborne Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning Sensor-III (LISS-III) data, preparation of site-level LAI maps, and validation of Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km LAI global fields. Measurements of field-level LAI, aerosol optical thickness and water vapor were carried out on the day of LISS-III overpasses. Empirical models based on the site-specific LAI-vegetation index relation were developed and used to generate 23-m resolution LAI maps for two sites (Indore and Bhopal) covering 30 kmtimes30 km. These LAI images were degraded to 1-km spatial resolution and used for validation of the version 3 and 4 MODIS LAI products (MOD15A2). The results indicate a positive correlation (r=0.78) between LAI derived from LISS-III data and MODIS data. However an overestimate by a factor of 1.6 to 2.5 in the version 3 MODIS product is observed with root mean square error (RMSE) ranging from 0.20 to 1.26. The factor of overestimation reduces significantly by 50% and RMSE by 40% when version 4 MODIS LAI was analyzed. The improvement in accuracy was observed to be associated with the change in algorithm path adopted for retrieving version 3 and 4 MODIS LAI. Analysis of the MODIS land cover product that is an input in the MODIS LAI retrieval algorithm indicated errors in assigning land cover classes for the study sites, which could be one of the sources of error in MODIS LAI product

MODIS land cover and LAI collection 4 product quality across nine sites in the western hemisphere

Global maps of land cover and leaf area index (LAI) derived from the Moderate Resolution Imaging Spectrometer (MODIS) reflectance data are an important resource in studies of global change, but errors in these must be characterized and well understood. Product validation requires careful scaling from ground and related measurements to a grain commensurate with MODIS products. We present an updated BigFoot project protocol for developing 25-m validation data layers over 49-km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2 </sup> study areas. Results from comparisons of MODIS and BigFoot land cover and LAI products at nine contrasting sites are reported. In terms of proportional coverage, MODIS and BigFoot land cover were in close agreement at six sites. The largest differences were at low tree cover evergreen needleleaf sites and at an Arctic tundra site where the MODIS product overestimated woody cover proportions. At low leaf biomass sites there was reasonable agreement between MODIS and BigFoot LAI products, but there was not a particular MODIS LAI algorithm pathway that consistently compared most favorably. At high leaf biomass sites, MODIS LAI was generally overpredicted by a significant amount. For evergreen needleleaf sites, LAI seasonality was exaggerated by MODIS. Our results suggest incremental improvement from Collection 3 to Collection 4 MODIS products, with some remaining problems that need to be addressed

MODIS leaf area index products: from validation to algorithm improvement

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

The importance of measurement errors for deriving accurate reference leaf area index maps for validation of moderate-resolution satellite LAI products

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