True Leaf Area Index Research Articles

Exploring the Effects of Topography on Leaf Area Index Retrieved from Remote Sensing Data at Various Spatial Scales over Rugged Terrains

Topography significantly affects remotely sensed reflectance data and subsequently impacts the retrieval of the leaf area index (LAI) from surface reflectance data over rugged terrains. However, most LAI inversion algorithms ignore the influence of terrain. This paper quantitatively analyzes the topographic effects on LAI values retrieved from remote sensing data at various spatial scales (30, 90, 270, 540, 1080, and 5400 m) over rugged terrains. The PRO4SAILT (PROSPECT + 4SAILT) model and the Proy algorithm were used to simulate multiscale surface reflectance for different LAI values over rugged terrains. Based on Gaussian process regression (GPR), an LAI inversion algorithm that ignores terrain effects was first developed. The simulated multiscale reflectance data were subsequently input into the inversion algorithm to retrieve LAI values. Finally, the retrieved LAI values were compared with the corresponding reference LAI values. The results demonstrate that the finer the spatial resolution is, the more significant the topographic effects on the retrieved LAI values are. When the reference LAI is five, as the spatial resolution increases from 5400 m to 30 m, the mean percentage error (MPE) of the retrieved LAI increases from 10.46% to 13.72%, and the root mean square error (RMSE) increases from 0.5376 to 1.005. Regardless of the spatial resolution, the error in the retrieved LAI values increases with an increasing terrain slope. When the reference LAI is five and the spatial resolution is 30 m, the MPE at a slope of 15°–30° is close to 5%, and the RMSE is close to 0.3. The MPE at a slope of 30°–45° is close to 20%, and the RMSE is close to one. In addition, the accuracy of the retrieved LAI values is closely related to the sky view factor (SVF). In general, the larger the SVF is, the smaller the error in the retrieved LAI values. In addition, the conversion relationships between the retrieved LAI values using the algorithm that ignores terrain effects and the true LAI values are provided in this study.

CO2-assimilation, sequestration, and storage by urban woody species growing in parks and along streets in two climatic zones

Using two cities, Rimini (Italy, Cfa climate) and Krakow (Poland, Cfb), as living laboratories, this research aimed at measuring in situ the capacity of 15 woody species to assimilate, sequester, and store CO2. About 1712 trees of the selected species were identified in parks or along streets of the two cities, and their age, DBH, height, and crown radius were measured. The volume of trunk and branches was measured using a terrestrial LiDAR. The true Leaf Area Index was calculated by correcting transmittance measurements conducted using a plant-canopy-analyser for leaf angle distribution, woody area index, and clumping. Dendrometric traits were fitted using age or DBH as independent variable to obtain site- and species-specific allometric equations. Instantaneous and daily net CO2-assimilation per unit leaf area was measured using an infra-red gas-analyser on full-sun and shaded leaves and upscaled to the unit crown-projection area and to the whole tree using both a big-leaf and a multilayer approach. Results showed that species differed for net CO2-assimilation per unit leaf area, leaf area index, and for the contribution of shaded leaves to overall canopy carbon gain, which yielded significant differences among species in net CO2-assimilation per unit crown-projection-area (AcpaML(d)). AcpaML(d) was underestimated by 6–30 % when calculated using the big-leaf, compared to the multilayer model. While maximizing AcpaML(d) can maximize CO2-assimilation for a given canopy cover, species which matched high AcpaML(d) and massive canopy spread, such as mature Platanus x acerifolia and Quercus robur, provided higher CO2-assimilation (Atree) at the individual tree scale. Land use (park or street), did not consistently affect CO2-assimilation per unit leaf or crown-projection area, although Atree can decline in response to specific management practices (e.g. heavy pruning). CO2-storage and sequestration, in general, showed a similar pattern as Atree, although the ratio between CO2-sequestration and CO2-assimilation decreased at increasing DBH.

Correcting for the clumping effect in leaf area index calculations using one-dimensional fractal dimension

The clumping effect is the main issue causing the heterogeneity in vegetation canopies and the underestimation of leaf area index (LAI) obtained using indirect measurement methods. Significant efforts have been exerted to correct for the clumping effect and derive the true LAI. Recent research has shown that the fractal dimension (FD) is directly related to the clumping effect of foliage, yet practical methods are needed to calculate field estimates. Considering that widely used LAI applications such as digital hemispherical photography (DHP), tracing radiation and architecture of canopies (TRAC), and digital cover photography (DCP) estimate LAI with one-dimensional (1D) gap probability and gap size data, we propose a method to correct for the clumping effect using 1D FD. Resulting formulae describing the relationship between LAI, CI, and 1D FD were based on the box-counting method (BCM) and a binomial distribution model. Sixty-four simulated scenes including four RAdiation transfer Model Intercomparison (RAMI) actual canopies and field measurements from nine plots (four orchard plots and five coniferous forest plots) were used to validate the novel method. Results showed good agreement with reference LAI values for simulated scenes (R2 = 0.96 and RMSE = 0.35). The 1DFD method generated higher LAI estimates compared with the LAI measured using TRAC at the four orchard plots especially at high canopy closure, while its results were more consistent with LAI obtained by litter collection than those of comparable methods at coniferous forest plots (bias from −13.5% to 9.9% for DCP images, from −3.0% to 19.7% for DHP images, and from −3.8% to 17.0% for TRAC transects). Our validation efforts indicate that the method proposed herein corrects for the clumping effect of vegetated canopies more effectively with DCP images, DHP images, and TRAC measurement when compared with traditional indirect optical methods. The 1DFD method is expected to improve indirect measurement accuracy of LAI.

Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration

In terrestrial ecosystems, leaves are aggregated into different spatial structures and their spatial distribution is non-random. Clumping index (CI) is a key canopy structural parameter, characterizing the extent to which leaf deviates from the random distribution. To assess leaf clumping effects on global terrestrial ET, we used a global leaf area index (LAI) map and the latest version of global CI product derived from MODIS BRDF data as well as the Boreal Ecosystem Productivity Simulator (BEPS) to estimate global terrestrial ET. The results show that global terrestrial ET in 2015 was 511.9 ± 70.1 mm yr−1 for Case I, where the true LAI and CI are used. Compared to this baseline case, (1) global terrestrial ET is overestimated by 4.7% for Case II where true LAI is used ignoring clumping; (2) global terrestrial ET is underestimated by 13.0% for Case III where effective LAI is used ignoring clumping. Among all plant functional types (PFTs), evergreen needleleaf forests were most affected by foliage clumping for ET estimation in Case II, because they are most clumped with the lowest CI. Deciduous broadleaf forests are affected by leaf clumping most in Case III because they have both high LAI and low CI compared to other PFTs. The leaf clumping effects on ET estimation in both Case II and Case III is robust to the errors in major input parameters. Thus, it is necessary to consider clumping effects in the simulation of global terrestrial ET, which has considerable implications for global water cycle research.

Comparative Evaluation of Algorithms for Leaf Area Index Estimation from Digital Hemispherical Photography through Virtual Forests

The in situ leaf area index (LAI) measurement plays a vital role in calibrating and validating satellite LAI products. Digital hemispherical photography (DHP) is a widely used in situ forest LAI measurement method. There have been many software programs encompassing a variety of algorithms to estimate LAI from DHP. However, there is no conclusive study for an accuracy comparison among them, due to the difficulty in acquiring forest LAI reference values. In this study, we aim to use virtual (i.e., computer-simulated) broadleaf forests for the accuracy assessment of LAI algorithms in commonly used LAI software programs. Three commonly used DHP programs, including Can_Eye, CIMES, and Hemisfer, were selected since they provide estimates of both effective LAI and true LAI. Individual tree models with and without leaves were first reconstructed based on terrestrial LiDAR point clouds. Various stands were then created from these models. A ray-tracing technique was combined with the virtual forests to model synthetic DHP, for both leaf-on and leaf-off conditions. Afterward, three programs were applied to estimate PAI from leaf-on DHP and the woody area index (WAI) from leaf-off DHP. Finally, by subtracting WAI from PAI, true LAI estimates from 37 different algorithms were achieved for evaluation. The performance of these algorithms was compared with pre-defined LAI and PAI values in the virtual forests. The results demonstrated that without correcting for the vegetation clumping effect, Can_Eye, CIMES, and Hemisfer could estimate effective PAI and effective LAI consistent with each other (R2 > 0.8, RMSD < 0.2). After correcting for the vegetation clumping effect, there was a large inconsistency. In general, Can_Eye more accurately estimated true LAI than CIMES and Hemisfer (with R2 = 0.88 > 0.72, 0.49; RMSE = 0.45 < 0.7, 0.94; nRMSE = 15.7% < 24.21%, 32.81%). There was a systematic underestimation of PAI and LAI using Hemisfer. The most accurate algorithm for estimating LAI was identified as the P57 algorithm in Can_Eye which used the 57.5° gap fraction inversion combined with the finite-length averaging clumping correction. These results demonstrated the inconsistency of LAI estimates from DHP using different algorithms. It highlights the importance and provides a reference for standardizing the algorithm protocol for in situ forest LAI measurement using DHP.

Leaf area index estimation using top-of-canopy airborne RGB images

Leaf Area Index (LAI) is one of the most important biophysical properties of a crop, used in detecting long-term water stress, estimating biomass, and identifying crop growth stage. Remote sensing based LAI estimation techniques perform well for early growth stages but tend to produce high error during the crop reproductive stage due to canopy closure. Moreover, estimation of the true LAI from individual leaf measurements remains a challenge. Consequently, two alternate methods have been developed and compared for estimating the LAI of a maize crop using top-of-canopy RGB images collected throughout the growing season using a hexacopter. Both methods used the RGB images to estimate the canopy height and the green-canopy cover together with a ‘vertical leaf area distribution factor’ (VLADF) from allometric relations (using crop height from RBG images and days after sowing). The first method used an empirical approach to estimate the LAI from training a linear function of the above inputs to Licor canopy analyser values of LAI. The method was trialled for a research farm located in a semi-arid area of southern peninsula India and found to have validation results with an R2 of 0.84 and RMSE of 0.36 for the unused portion of the Rabi (post-monsoon) season data of 2018–19, and R2 of 0.77 and RMSE of 0.45 for the Rabi 2019–20 season data when compared with Licor LAI values. While seemingly acceptable, the Licor canopy analyser gives a foliage area index and so the accuracy of this model was very low (R2 of 0.56 and RMSE of 1.34) when evaluated with true LAI from manual measurements of the leaf area. Consequently, a refinement was introduced using only VLADF, green-canopy cover estimates from the RBG images, and a field measured top leaf angle. The output derived from this conceptual model had an R2 of ~0.6 and RMSE of 0.73 when compared with the true LAI values. Importantly, the LAI from this conceptual model was found to be unaffected by canopy closure during the reproductive stage of the crop.

Comparison of LiDAR-based Models for True Leaf Area Index and Effective Leaf Area Index Estimation in Young Beech Forests

The leaf area index (LAI) is one of the most common leaf area and canopy structure quantifiers. Direct LAI measurement and determination of canopy characteristics in larger areas is unrealistic due to the large number of measurements required to create the distribution model. This study compares the regression models for the ALS-based calculation of LAI, where the effective leaf area index (eLAI) determined by optical methods and the LAI determined by the direct destructive method and developed by allometric equations were used as response variables. LiDAR metrics and the laser penetration index (LPI) were used as predictor variables. The regression models of LPI and eLAI dependency and the LiDAR metrics and eLAI dependency showed coefficients of determination (R2) of 0.75 and 0.92, respectively; the advantage of using LiDAR metrics for more accurate modelling is demonstrated. The model for true LAI estimation reached a R2 of 0.88.

Estimation of forest leaf area index and clumping index from the Global Positioning System (GPS) satellite carrier-to-noise-density ratio (C/N0)

ABSTRACTForest leaf area index (LAI) and clumping index (CI) are critical structural parameters to study the biomass, biogeochemical cycles, ecological simulation, and radiation transfer in a forest system. The ground-based measurements by traditional methods are costly, time-consuming, and are sensitive to variations in solar illumination conditions. Global Positioning System (GPS) satellites transfer L-band signals, and the presence of the forest canopy leads to an increase in noise. The GPS satellite’s signal strength is generally described by the carrier-to-noise-density ratio (C/N0). This study explores the potential of using the GPS satellite C/N0 to estimate forest canopy structural parameters. We performed experiments at 16 forest plots in northeastern China. LAI, CI, and canopy closure were estimated by digital hemispherical photography (DHP) using the CAN_EYE software. The GPS satellite C/N0 was recorded with a modified smartphone application, GPSTest. The relationship between the forest canopy structural parameters and satellite C/N0 was analyzed for those observations with satellite elevation angle (SEA) > 30°. The results show that both the true LAI and the effective LAI have strong linear and polynomial correlation with C/N0 and the coefficient of determination (R2) is larger than 0.7. CI is linearly related to C/N0(R2 = 0.56). The linear relationship between the canopy closure and C/N0 is much lower (R2= 0.28). Accounting for the SEA effect, LAI can be estimated with R2 > 0.8, and CI with R2 = 0.6. LAI and CI can be well estimated from C/N0 when the SEA effect is accounted for. In general, the GPS C/N0 provides an alternative method to estimate forest canopy structural parameters.

Modeling of Mixed-Pixel Clumping Index From Remote Sensing Data and Its Evaluation

The clumping index (CI) is a canopy structure parameter that describes the dispersion or grouping of leaves. Previously, it has been estimated based on the normalized difference between hotspot and darkspot (NDHD), which is derived from multi-angle remote sensing data. However, currently it is impossible to derive CI from NDHD for a large area at a spatial resolution finer than 275 m since such fine multi-angle data are unavailable. In this study, an algorithm of the mixed-pixel clumping index (MPCI) was implemented, and an MPCI map of China's landmass at 1 km resolution was derived from the HJ-1A/1B data at 30 m resolution. The MPCI map was compared with the previous NDHD CI derived from the moderate resolution imaging spectroradiometer (MODIS). The correlation of these two datasets was greater than 0.9, and the mean bias was approximately 0.1. Indirectly, the MPCI map was applied to an effective leaf area index (LAI) product to derive true LAI. Using the MODIS LAI product as a reference, we found that the coefficient of determination was improved from 0.72 to 0.80, and the root mean squared error was reduced from 0.53 to 0.35 m2/m2 after the effective LAI is corrected by this MPCI map, suggesting that this MPCI map is comparable to the NDHD CI. Although our algorithm is currently tested at 1 km resolution, potentially, it can be applied to higher spatial resolution than 275 m for mapping LAI and carbon cycle modeling before these multi-angle data at higher resolution are available.

Estimación de parámetros biofísicos de la vegetación en praderas y cultivos en Chile mediante fotografía digital hemisférica obtenidas por una cámara GoPro

<p>The estimation of the biophysical parameters of vegetation such as LAI (Leaf Area Index), FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) and FCOVER (Fraction of Green Vegetation) have many climatic, hydrologic, ecosystem and silvo-agricultural applications. Despite the various satellite products that estimate these parameters continuously and globally, it’s necessary to continue generating <em>in situ</em> estimations to validate these remote data. It’s in this context where Digital Hemispheric Photography (DHP) technique stands out as being one of the most accurate an adaptable to operate continuously with diverse photographic equipment and field scenarios. The objective of this paper is to estimate effective LAI (LAI<sub>eff</sub>), true LAI (LAI<sub>true</sub>), FAPAR and FCOVER through the DHP method on several agricultural land covers in Chile, between the years 2015 and 2016 using a GoPro camera and the CAN-EYE software to process hemispheric photographs. The results obtained were initially compared with those provided by a CANON EOS 6D camera mounted together with a SIGMA 8mm F3.5-EX DG fisheye lens and subsequently with satellite products provided by the Copernicus Global Land service, derived from PROBA-V mission at 333 m<sup>2</sup> spatial resolution. The comparison between the CANON and GoPro shows similar values and R<sup>2 </sup>over 0,72 for all parameters. The comparison with PROBA-V resulted in values over 0,52 of R<sup>2</sup> for the parameters, and similar multitemporal patterns. It’s concluded that it’s possible to estimates LAI<sub>eff</sub>, FAPAR and FCOVER like other fish eyes cameras. Concerning PROBA-V, except for FAPAR, the estimates with the GoPro do not show much correlation. In both campaigns significant discrepancies were observed in the LAI<sub>true</sub>, which could be related to the calculation of CAN-EYE canopy clumping with the characteristics of the camera itself.</p>

Estimation of forest leaf area index using terrestrial laser scanning data and path length distribution model in open-canopy forests

Terrestrial Laser Scanning (TLS) is an active technology that can acquire the finest characteristics of canopy structure and plays an increasing role in estimating Leaf Area Index (LAI) in forest canopies. However, 3D information is not directly used in conventional TLS-based methods using the gap fraction theory. In addition, quantifying clumping effect within canopies is still a difficult task. In this paper, we presented a method to reduce clumping effect and estimate LAI using TLS data. Our recently proposed path length distribution model was applied to TLS data. Instead of converting 3D points to 2D image, the path length distribution can be extracted using the TLS-recorded 3D data and the crown models built with the alpha shapes algorithm. Two simulated scenes and one actual forest plot were utilized for validation. The results of the proposed method agree well with both the true LAI (in the simulated scenes) and the extracted PAI by the digital hemispherical photography (in the actual plot). This LAI estimation method using TLS and the path length distribution model provides a novel way for ground-based LAI measurements and shows its great potential.

A method of analyzing LAI underestimation for dense vegetation based on the vertical distribution of the leaf area density

ABSTRACTCanopy reflectance saturation is a major cause of leaf area index (LAI) underestimation, especially for vegetation types with large LAI values. For a dense canopy, the vertical distribution of the leaf area density is a factor that cannot be ignored. The multilayer Scattering by Arbitrary Inclined Leaves model considering Hotspot effects (SAILH) was combined with vertical profiles of the leaf area density to simulate the layer reflectance and corresponding layer contributions of a dense corn canopy. Canopy reflectance saturation in this paper was defined as a layer reflectance contribution less than 5%, and the sum of the LAI values of these layers directly resulted in LAI underestimation. The results showed that the degrees of canopy reflectance saturation and LAI underestimation increased with increasing LAI and strongly depended on the vertical distribution of the leaf area density. The LAI was underestimated by 8.7% and 10.2% on average for true LAIs of 4.23 and 4.69, respectively. The simulated LAI underestimations were validated based on LAI inversion from field spectral measurements for corn. LAI underestimation was compared between the model simulations and inversions. The differences in LAI underestimation were 0.19 and 0.13 for true LAIs of 4.23 and 4.69, respectively.

Derivation, Validation, and Sensitivity Analysis of Terrestrial Laser Scanning-Based Leaf Area Index

. Terrestrial laser scanning (TLS) is a promising tool, used to retrieve leaf area index (LAI). However, the accuracy of LAI estimations based on TLS is still difficult to validate, because high-fidelity destructive measurements of leaf area are lacking. A comprehensive analysis of the sensitivity of TLS-based LAI estimates against various influencing factors (e.g., noise points, woody points, and voxel size) has yet to be reported. We acquired the true LAI by destructively measuring all leaves of 17 magnolia trees. We also improved a voxel-based method to estimate the LAI from the TLS data. We further assessed the sensitivity of LAI estimates against denoising, separation of woody points from foliage points, and voxel size. Our results showed that TLS-based LAI estimations were significantly related to the destructively sampled LAI (R2 = 0.832, RMSE = 0.693). Denoising improved the TLS-based LAI accuracy with a decrease of 0.415 in RMSE. Conversely, wood-leaf separation showed little effect on the accuracy of LAI estimation. The voxel size was an important parameter affecting the accuracy of TLS-based LAI, and our new method for determining voxel size (R2 = 0.832) proved to be more effective than the existing 2 methods (R2 = 0.661 and 0.581).

Determining woody-to-total area ratio using terrestrial laser scanning (TLS)

Accurately determining woody-to-total area ratio (WTA) is a key step to indirectly retrieve leaf area index (LAI) from terrestrial laser scanning (TLS) data. In this work, we first collected both individual tree and forest plot point cloud data (PCD) from broadleaf and coniferous tree species and leaf characteristics using both side-lateral and full field-of-view TLS field setups with scan distances between 2.5 to 28m. Using a local geometrical feature-based algorithm, the generated PCD were automatically classified into three different categories including photosynthetic canopy components, non-photosynthetic canopy components, and bare earth. To convert each classified point into a surface area, we then developed and validated a novel approach that considers sampling space, laser incidence angle, and leaf orientation information. The estimated surface areas from this approach showed strong agreements with validation datasets for single leaf (91.44%), photosynthetic (95.64%), and non-photosynthetic canopy components (89.60%) of an artificial tree and stems of an old-growth coniferous tree (93.53%), two individual broadleaf trees (98.31% and 97.46%) and a broadleaf forest plot (90.26%). By doing this, we computed the parameter WTA for an individual artificial tree (10.90%), an old-growth coniferous tree (29.97%), two individual broadleaf tree (14.83% and 4.27%) and four natural forest stands ranging from 7.74%–15.57%, respectively. The proposed method can effectively improve the accuracy of retrieving true LAI by removing the effects of woody components and converting each point into a surface area.

Spatial-temporal characteristics of foliage clumping index in China during 2000–2013

The distribution of natural vegetation foliage is often clumped at different scales of three-dimensional structures such as shoots, branches, whorls, tree crowns, and tree groups. Clumping index (CI) quantifies the level of foliage grouping relative to a random distribution in space. As an additional vegetation structural parameter of comparable importance to leaf area index (LAI), CI strongly influences canopy radiation regimes, photosynthesis and land-atmosphere interactions. It is a correction factor to convert the effective LAI which assumes a random distribution of leaves in space to the true LAI, and is also needed to separate the canopy into sunlit and shaded leaves for accurate simulations of process-based ecological and land surface models. However, due to the lack of clumping index available, clumping index values are often assumed to vary only with land cover types in most operational LAI retrieval algorithms and ecological models, which might induce large uncertainties in these model outputs. Therefore, it is highly desirable to map the spatial and temporal variations of the clumping for different land cover types.

Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC)

Leaf area index (LAI) and leaf chlorophyll content (Chll) represent key biophysical and biochemical controls on water, energy and carbon exchange processes in the terrestrial biosphere. In combination, LAI and Chll provide critical information on vegetation density, vitality and photosynthetic potentials. However, simultaneous retrieval of LAI and Chll from space observations is extremely challenging. Regularization strategies are required to increase the robustness and accuracy of retrieved properties and enable more reliable separation of soil, leaf and canopy parameters. To address these challenges, the REGularized canopy reFLECtance model (REGFLEC) inversion system was refined to incorporate enhanced techniques for exploiting ancillary LAI and temporal information derived from multiple satellite scenes. In this current analysis, REGFLEC is applied to a time-series of Landsat data.A novel aspect of the REGFLEC approach is the fact that no site-specific data are required to calibrate the model, which may be run in a largely automated fashion using information extracted entirely from image-based and other widely available datasets. Validation results, based upon in-situ LAI and Chll observations collected over maize and soybean fields in central Nebraska for the period 2001–2005, demonstrate Chll retrieval with a relative root-mean-square-deviation (RMSD) on the order of 19% (RMSD=8.42μgcm−2). While Chll retrievals were clearly influenced by the version of the leaf optical properties model used (PROSPECT), the application of spatio-temporal regularization constraints was shown to be critical for estimating Chll with sufficient accuracy. REGFLEC also reproduced the dynamics of in-situ measured LAI well (r2=0.85), but estimates were biased low, particularly over maize (LAI was underestimated by ~36 %). This disparity may be attributed to differences between effective and true LAI caused by significant foliage clumping not being properly accounted for in the canopy reflectance model (SAIL). Additional advances in the retrieval of canopy biophysical and leaf biochemical constituents will require innovative use of existing remote sensing data within physically realistic canopy reflectance models along with the ability to exploit the enhanced spectral and spatial capabilities of upcoming satellite systems.

Indirect measurement of leaf area index on the basis of path length distribution

Accurate leaf area index (LAI) measurements are important for understanding and evaluating global mass and energy cycle. Foliage clumping serves an important function in traditional indirect LAI measurement methods. The clumping index has been used to modify effective LAI for decades. However, the change in path length within canopies is often the most uncertain factor in indirect LAI estimation using Beer's law. A simple clumping index is incapable of describing the heterogeneity of a canopy and may cause large errors in calculating true LAI values. We proposed a new LAI estimation method by using path length distribution functions in optical measurement. Both simulation and field measurements show that the path length-based method can effectively characterize the LAI values of heterogeneous canopies. Deviation is less than 10% for all the validations. One of the advantages of path length distribution theory is that it can characterize and handle crown shape-induced non-randomness within canopies. Such non-randomness, which may cause underestimation of up to 25%, has not been well addressed by existing algorithms. Path length theory is expected to improve the indirect measurement accuracy of LAI significantly with the use of current optical instruments.

Improving LAI Mapping by Integrating MODIS and CYCLOPES LAI Products Using Optimal Interpolation

Leaf area index (LAI) is an important land surface biophysical variable used to characterize vegetation amount and activity. Current satellite LAI products, however, do not satisfy the requirements of the modeling community due to their large uncertainties and frequent missing values. There is an urgent need for advanced methods to integrate multiple LAI products to improve the product's accuracy and integrity for various applications. To meet this need, this study proposes a method based on Optimal Interpolation (OI) to integrate MODIS true LAI and CYCLOPES effective LAI retrievals. Multiple years' LAI means and variances (LAI climatology) are pre-calculated and used as the baseline for data integration. The locally adjusted cubic-spline capping algorithm is used to smooth the climatology data. An LAI normalization scheme, based on a linear measurement error model, is developed to account for the systematic difference between the two products and to generate the LAI predictions. This integration process removes the unrealistically large temporal variations of the original LAI products. The data gaps are filled with information from adjacent pixels and the prior knowledge acquired from multiyear climatology. A spatially and temporally continuous true LAI data set is generated. High resolution reference maps of true LAI are collected to validate the two true LAI data sets: MODIS LAI product and the integrated LAI values. The validation results suggest that the integrated results agree better with the LAI reference maps than the MODIS LAI product, showing higher R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , smaller bias and root mean squared error.

Improvement of spatially continuous forest LAI retrieval by integration of discrete airborne LiDAR and remote sensing multi-angle optical data

Forest leaf area index (LAI) is a critical variable in modeling climates and ecosystems, and is required on regional and global scales for models. However, forest LAI has proven to be difficult to obtain. In this study, we sought to improve forest LAI retrieval in a large study area in the Dayekou forest, Gansu province, by combining airborne discrete LiDAR, MODIS, and MISR data. In our retrieval scheme, canopy height is the key parameter, and the canopy height precision is of great importance when estimating LAI. To address this issue, we introduced LiDAR data and combined it with the MODIS and MISR products. First, the canopy height for the LiDAR data coverage was calculated using a local maximum filtering algorithm with a variable window size. Then, a multivariate linear regression model was developed to extrapolate the LiDAR-derived canopy height to the whole study area using the MODIS BRDF/Albedo product. In addition, the bi-directional reflectances from MODIS and MISR were used to invert the geometric-optical mutual-shadowing (GOMS) model structural parameters (nR2, b/R, h/b, Δh/b) of the forest. These structural parameters were then combined with the forest canopy height and field measurements to retrieve the LAI of the continuous forest area at a 500-m resolution. After comparison with the true LAI measured by LAI-2000 combined with TRAC, and by TRAC alone, the highest R2 values of the estimated LAI were 0.73 and 0.69, respectively. The results indicate that the LiDAR canopy height derived from the optical multi-angle remote sensing data can be used to retrieve the large-scale forest LAI when combined with the canopy structure information derived from GOMS model.

Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance

Leaf area index (LAI) products at regional and global scales are being routinely generated from individual instrument data acquired at a specific time. As a result of cloud contamination and other factors, these LAI products are spatially and temporally discontinuous and are also inaccurate for some vegetation types in many areas. A better strategy is to use multi-temporal data. In this paper, a method was developed to estimate LAI from time-series remote sensing data using general regression neural networks (GRNNs). A database was generated from Moderate-Resolution Imaging Spectroradiometer (MODIS) and CYCLOPES LAI products as well as MODIS reflectance products of the BELMANIP sites during the period from 2001-2003. The effective CYCLOPES LAI was first converted to true LAI, which was then combined with the MODIS LAI according to their uncertainties determined from the ground-measured true LAI. The MODIS reflectance was reprocessed to remove remaining effects. GRNNs were then trained over the fused LAI and reprocessed MODIS reflectance for each biome type to retrieve LAI from time-series remote sensing data. The reprocessed MODIS reflectance data from an entire year were inputted into the GRNNs to estimate the 1-year LAI profiles. Extensive validations for all biome types were carried out, and it was demonstrated that the method is able to estimate temporally continuous LAI profiles with much improved accuracy compared with that of the current MODIS and CYCLOPES LAI products. This new method is being used to produce the Global Land Surface Satellite LAI products in China.