Leaf Area Index Datasets Research Articles

Spatially and temporally continuous LAI datasets based on the mixed pixel decomposition method

The leaf area index (LAI) is a key biophysical parameter that determines the state of plant growth. A global LAI has been routinely produced by the Moderate Resolution Imaging Spectro-radiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). However, the MODIS and AVHRR LAI products cannot be synchronized with the same spatial and temporal resolution. The LAI features are not discernible when a global LAI product is implemented at the regional scale because it has low resolution and different land cover types. To obtain high spatial and temporal resolution of LAI products, an empirical model based on the pixel scale was developed. The approach to generate a long (multi-decade) time series of a 1-km spatial resolution LAI normally integrates both AVHRR and MODIS datasets for different land cover types. In this paper, a regression-based model for generating a vegetation LAI was developed using the AVHRR Global Inventory Modelling and Mapping Studies Normalized Difference Vegetation Index (NDVI), MODIS LAI and land cover as input data; the model was evaluated by using relevant data from the same period data from 2000 to 2006. The results of this method show a good consistency in LAI values retrieved from the AVHRR NDVI and MODIS LAI. This simple method has no specific-limited data requirements and can provide improved spatial and temporal resolution in a region without ground data.

Spatio-temporal variation in terminal drought over western India using dryness index derived from long-term MODIS data

Drought has always been one of the major hazards in semi-arid and arid regions of the world. This study was undertaken to investigate spatial trends in terminal drought over western India composed of the states of Rajasthan and Gujarat, which are characterized by semi-arid to arid climate. These two states have always been vulnerable to drought since they receive highly variable moderate to scanty rainfall. Leaf area index (LAI)-based temperature vegetation dryness index (TVDI) for the period 2002–2012 was computed using 8-day composites of Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and LAI datasets with 1-km spatial resolution. TVDI maps were prepared for all days of the year (DOY) for the months of September and October, which facilitated our understanding of the spatial extent of drought during different 8-day periods. Based on the analysis, it was found that the LAI-based TVDI could capture drought conditions over space and time. Temporal evolution of the observed crop moisture index (CMI) against TVDI reveals that the TVDI exhibited an increasing trend as the CMI decreased or became negative during the 2002 drought year. A significant and negative relationship (R2=0.23) with TVDI and CMI indicates more sensitivity of TVDI to soil moisture in sparse vegetation canopies under drought conditions. Spatial trends in terminal drought intensity were analyzed using linear regression and Mann–Kendall trend test. It was revealed that a majority of the northern part of the study area witnessed an increase in terminal drought intensity and the southern portion registered negative trends. With 80% confidence, it can be stated that the terminal drought intensity increased at scattered locations over southwestern and central Gujarat. However, over central and southwestern Rajasthan, decreasing trends were registered.

The effect of year-to-year variability of leaf area index on Variable Infiltration Capacity model performance and simulation of runoff

This study assessed the effect of using observed monthly leaf area index (LAI) on hydrological model performance and the simulation of runoff using the Variable Infiltration Capacity (VIC) hydrological model in the Goulburn–Broken catchment of Australia, which has heterogeneous vegetation, soil and climate zones. VIC was calibrated with both observed monthly LAI and long-term mean monthly LAI, which were derived from the Global Land Surface Satellite (GLASS) leaf area index dataset covering the period from 1982 to 2012. The model performance under wet and dry climates for the two different LAI inputs was assessed using three criteria, the classical Nash–Sutcliffe efficiency, the logarithm transformed flow Nash–Sutcliffe efficiency and the percentage bias. Finally, the deviation of the simulated monthly runoff using the observed monthly LAI from simulated runoff using long-term mean monthly LAI was computed. The VIC model predicted monthly runoff in the selected sub-catchments with model efficiencies ranging from 61.5% to 95.9% during calibration (1982–1997) and 59% to 92.4% during validation (1998–2012). Our results suggest systematic improvements, from 4% to 25% in Nash–Sutcliffe efficiency, in sparsely forested sub-catchments when the VIC model was calibrated with observed monthly LAI instead of long-term mean monthly LAI. There was limited systematic improvement in tree dominated sub-catchments. The results also suggest that the model overestimation or underestimation of runoff during wet and dry periods can be reduced to 25 mm and 35 mm respectively by including the year-to-year variability of LAI in the model, thus reflecting the responses of vegetation to fluctuations in climate and other factors. Hence, the year-to-year variability in LAI should not be neglected; rather it should be included in model calibration as well as simulation of monthly water balance.

Comprehensive study on the influence of evapotranspiration and albedo on surface temperature related to changes in the leaf area index

Many studies have investigated the influence of evapotranspiration and albedo and emphasize their separate effects but ignore their interactive influences by changing vegetation status in large amplitudes. This paper focuses on the comprehensive influence of evapotranspiration and albedo on surface temperature by changing the leaf area index (LAI) between 30°–90°N. Two LAI datasets with seasonally different amplitudes of vegetation change between 30°–90°N were used in the simulations. Seasonal differences between the results of the simulations are compared, and the major findings are as follows. (1) The interactive effects of evapotranspiration and albedo on surface temperature were different over different regions during three seasons [March–April–May (MAM), June–July–August (JJA), and September–October–November (SON)], i.e., they were always the same over the southeastern United States during these three seasons but were opposite over most regions between 30°–90°N during JJA. (2) Either evapotranspiration or albedo tended to be dominant over different areas and during different seasons. For example, evapotranspiration dominated almost all regions between 30°–90°N during JJA, whereas albedo played a dominant role over northwestern Eurasia during MAM and over central Eurasia during SON. (3) The response of evapotranspiration and albedo to an increase in LAI with different ranges showed different paces and signals. With relatively small amplitudes of increased LAI, the rate of the relative increase in evapotranspiration was quick, and positive changes happened in albedo. But both relative changes in evapotranspiration and albedo tended to be gentle, and the ratio of negative changes of albedo increased with relatively large increased amplitudes of LAI.

Inter-Comparison and Evaluation of the Global LAI Product (LAI3g) and the Regional LAI Product (GGRS-LAI) over the Area of Kazakhstan

Long-term global datasets of the Leaf Area Index (LAI) are important for monitoring global vegetation dynamics and are an important input for Earth system models (ESM). The comparison of long-term datasets is based on two recently available datasets both derived from AVHRR (Advanced Very High Resolution Radiometer) time series. The LAI3g dataset is developed from the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) from AVHRR sensors and best-quality MODIS LAI data. The second long-term LAI dataset is based on the 8-km spatial resolution GIMMS-AVHRR data (Goettingen GIS & Remote Sensing, GGRS dataset). The GGRS-LAI product uses a satellite-based LAI. This algorithm uses a three-dimensional physical radiative transfer model, which establishes the relationship between LAI, vegetation fractional cover and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation. The model incorporates a number of site-/region-specific parameters, including the vegetation architecture variables, such as leaf angle distribution, clumping index and light extinction coefficient. For the application of the model to Kazakhstan, the vegetation architecture variables were computed at the local (pixel) level based on extensive field surveys of the biophysical properties of vegetation in representative grassland areas of Kazakhstan. As a main result of our study, we could summarize that the differences between both products are most pronounced at the start and the end of the growing season. During the spring and autumn months, the LAI difference maps showed a considerable difference of LAI GGRS and LAI3g. LAI3g is characterized by a considerably earlier start and a later finish to the growing season than LAI GGRS. Moreover, LAI3g showed LAI > 0 during the winter months when any green vegetation is absent in all land covers of Kazakhstan. A direct cause for this could be a too high base level of the LAI3g during the leafless phase.

Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model

To predict regional-scale winter wheat yield, we developed a crop model and data assimilation framework that assimilated leaf area index (LAI) derived from Landsat TM and MODIS data into the WOFOST crop growth model. We measured LAI during seven phenological phases in two agricultural cities in China’s Hebei Province. To reduce cloud contamination, we applied Savitzky–Golay (S–G) filtering to the MODIS LAI products to obtain a filtered LAI. We then regressed field-measured LAI on Landsat TM vegetation indices to derive multi-temporal TM LAIs. We developed a nonlinear method to adjust LAI by accounting for the scale mismatch between the remotely sensed data and the model’s state variables. The TM LAI and scale-adjusted LAI datasets were assimilated into the WOFOST model to allow evaluation of the yield estimation accuracy. We constructed a four-dimensional variational data assimilation (4DVar) cost function to account for the observations and model errors during key phenological stages. We used the shuffled complex evolution–University of Arizona algorithm to minimize the 4DVar cost function between the remotely sensed and modeled LAI and to optimize two important WOFOST parameters. Finally, we simulated winter wheat yield in a 1-km grid for cells with at least 50% of their area occupied by winter wheat using the optimized WOFOST, and aggregated the results at a regional scale. The scale adjustment substantially improved the accuracy of regional wheat yield predictions (R2=0.48; RMSE=151.92kgha−1) compared with the unassimilated results (R2=0.23; RMSE=373.6kgha−1) and the TM LAI results (R2=0.27; RMSE=191.6kgha−1). Thus, the assimilation performance depends strongly on the LAI retrieval accuracy and the scaling correction. Our research provides a scheme to employ remotely sensed data, ground-measured data, and a crop growth model to improve regional crop yield estimates.

Large-scale retrieval of leaf area index and vertical foliage profile from the spaceborne waveform lidar (GLAS/ICESat)

Leaf area index (LAI) and canopy vertical profiles are important descriptors of ecosystem structure. The Geoscience Laser Altimeter System (GLAS) on board ICESat (Ice, Cloud, and land Elevation Satellite) provided three-dimensional observations that can be used to derive these canopy structure parameters globally. While several canopy height products have been produced globally from GLAS, no comparable data sets for LAI and canopy profiles exist across large areas. In this study we develop a physically based method of retrieving LAI and vertical foliage profiles (VFPs) from GLAS observations over the entire state of California, USA. This method refines lidar derived LAI and VFP through a recursive analysis of GLAS waveforms using ancillary data obtained from existing remote sensing products. Those supplemental inputs include canopy clumping index derived from POLDER, 500m land cover type and 1km LAI data derived from MODIS. Implementation of our method created state-level LAI and VFP data for the existing GLAS transects over California. We then analyzed the variability of LAI and VFP data sets across environmental gradients and as a function of land cover type and elevation. Both LAI and VFP showed strong variability across elevational gradients and among land cover types. We compared our results at the scale of GLAS footprints with an LAI map derived from Landsat (at 30m) and found moderate agreement (r2=0.34, bias=0.26, RMSD (Root Mean Square Difference)=1.85) between the two. In particular, Landsat LAI not only appeared to saturate relative to GLAS LAI at around LAI=5, but also showed an overestimation for LAI less than about 2. Best agreement between the two LAI data sets was shown to occur in areas with slope less than 20°. Results from our study suggest the possibility of retrieving global LAI and VFP data from GLAS data and the potential for synergetic observation of lidar and passive optical remote sensing data such as Landsat.

Development of a remotely sensing seasonal vegetation‐based Palmer Drought Severity Index and its application of global drought monitoring over 1982–2011

AbstractVegetation effects are currently disregarded in Palmer Drought Severity Index (PDSI), and the sensitivity of PDSI to the choice of potential evaporation (EP) parameterization is often a concern. We developed a revised self‐calibrating PDSI model that replaces EP with leaf area index‐based total evapotranspiration (ARTS E0). It also included a simple snowmelt module. Using a unique satellite leaf area index data set and climate data, we calculated and compared ARTS E0, three other types of EP (i.e., Thornthwaite EP_Th, Allen EP_Al, and Penman‐Monteith EP_PM), and corresponding PDSI values (i.e., PDSI_ARTS, PDSI_Th, PDSI_Al, and PDSI_PM) for the period 1982–2011. The results of PDSI_ARTS, PDSI_Al, and PDSI_PM show that global land became wetter mainly due to increased precipitation and El Niño–Southern Oscillation (ENSO) effect for the period, which confirms the ongoing intensification of global hydrologic cycle with global temperature increase. However, only PDSI_Th gave a trend of global drying, which confirms that PDSI_Th overestimates the global drying in response to global warming; i.e., PDSI values are sensitive to the parameterizations for Ep. Thus, ARTS E0, EP_Al, and EP_PM are preferred to EP_Th in global drought monitoring. In short, global warming affects global drought condition in two opposite ways. One is to contribute to the increases of EP and hence drought; the other is to increase global precipitation that contributes to global wetting. These results suggest that precipitation trend and its interaction with global warming and ENSO should be given much attention to correctly quantify past and future trends of drought.

A New Equation for Deriving Vegetation Phenophase from Time Series of Leaf Area Index (LAI) Data

Accurately modeling the land surface phenology based on satellite data is very important to the study of vegetation ecological dynamics and the related ecosystem process. In this study, we developed a Sigmoid curve (S-curve) function by integrating an asymmetric Gaussian function and a logistic function to fit the leaf area index (LAI) curve. We applied the resulting asymptotic lines and the curvature extrema to derive the vegetation phenophases of germination, green-up, maturity, senescence, defoliation and dormancy. The new proposed S-curve function has been tested in a specific area (Shangdong Province, China), characterized by a specific pattern in leaf area index (LAI) time course due to the dominant presence of crops. The function has not yet received any global testing. The identified phenophases were validated against measurement stations in Shandong Province. (i) From the site-scale comparison, we find that the detected phenophases using the S-curve (SC) algorithm are more consistent with the observations than using the logistic (LC) algorithm and the asymmetric Gaussian (AG) algorithm, especially for the germination and dormancy. The phenological recognition rates (PRRs) of the SC algorithm are obviously higher than those of two other algorithms. The S-curve function fits the LAI curve much better than the logistic function and asymmetric Gaussian function; (ii) The retrieval results of the SC algorithm are reliable and in close proximity to the green-up observed data whether using the AVHRR LAI or the improved MODIS LAI. Three inversion algorithms shows the retrieval results based on AVHRR LAI are all later than based on improved MODIS LAI. The bias statistics reveal that the retrieval results based on the AVHRR LAI datasets are more reasonable than based on the improved MODIS LAI datasets. Overall, the S-curve algorithm has the advantage of deriving vegetation phenophases across time and space as compared to the LC algorithm and the AG algorithm. With the SC algorithm, the vegetation phenophases can be extracted more effectively.

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.

Performance of LAI‐MODIS and the influence on drought simulation in a Mediterranean forest

ABSTRACTThis study investigates the benefits and methodological issues to integrate weekly 1 km Leaf Area Index (LAI) Moderate Resolution Imaging Spectroradiometer (LAI‐MODIS) (MOD15A2) satellite product in a distributed water budget model over a Mediterranean forested ecosystem of about 2553 km2 for drought assessment at regional scale. The high overestimation of LAI‐MODIS compared to field measurements was corrected based on the calibration of high‐resolution Satellites Pour l'Observation de la Terre (SPOT) images combined with ground measurements. The LAI‐MODIS time series over the 2003 to 2009 period has been calibrated and integrated into a spatially explicit water budget model at the regional level using spatial information on vegetation, soil types and topoclimates. Actual LAI‐MODIS scenario was tested against temporal and spatial null models to assess for the benefits of the regional heterogeneity from MODIS and its intra‐annual and interannual variation in a water budget model. From water budget analyses at local and regional scales, we concluded that calibration of LAI‐MODIS images was mandatory to enhance the correlation coefficient between measured and simulated daily actual transpiration. We estimated daily bias in the resulting ‘Water Stress Index’ to be slightly affected between constant and seasonally‐varying LAI datasets. However, the interannual variability in LAI detected by MODIS followed LAI adjustments expected from the ecohydrological equilibrium hypothesis. This can significantly affect the simulated annual drought period features, so that integrating LAI‐MODIS spatial and temporal variability into water budget models for evergreen Mediterranean vegetation can be a useful dataset when carbon allocation schemes in dynamic vegetation models are lacking, but should be carefully calibrated particularly in transitions towards semi‐arid zones. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Assessment of the SeaWinds scatterometer for vegetation phenology monitoring across China

Vegetation phenology tracks plants' lifecycle events, revealing the response of vegetation to global climate changes. Changes in vegetation phenology also influence fluxes of carbon, water, and energy at local and global scales. In this study, we analysed a time series of Ku-band radar backscatter measurements from the SeaWinds scatterometer on board the Quick Scatterometer (QuickSCAT) to examine canopy phenology from 2003 to 2005 across China. The thaw season SeaWinds backscatter and Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) time series were significantly correlated in 20 of the 22 sites (p < 0.05). A weighted curve-fitting method was applied to detect the start of season and end of season from both data sets. The SeaWinds scatterometer generally detected earlier timing of spring leaf-out and later fall senescence than the MODIS LAI data sets. The SeaWinds backscatter detected phenological metrics in 75.85% of mainland China. Similar spatial patterns were observed from the SeaWinds backscatter and MODIS LAI time series; however, the average standard deviation of the scatterometer-detected metrics was lower than that of MODIS LAI products. Overall, the phenological information from the SeaWinds scatterometer could provide an alternative view on the growth dynamics of land-surface vegetation.

Intercomparison of Leaf Area Index Products for a Gradient of Sub-Humid to Arid Environments in West Africa

The Leaf Area Index (LAI) is a key variable in many land surface and climate modeling studies. To date, a number of LAI datasets have been developed based on time series of medium resolution optical remote sensing observations. Global validation exercises show the high value of these datasets, but at the same time they point out shortcomings, particularly in the presence of persistent cloud coverage and dense vegetation. For regional modeling studies, the choice of an ideal LAI input dataset is not straightforward as global validation, and intercomparison studies do not necessarily allow conclusions on data quality at regional scale. This paper provides a comprehensive relative intercomparison of four freely available LAI products for a wide gradient of ecosystems in Africa. The region of investigation, West Africa, comprises typical African sub-humid to arid landscapes. The selected LAI time series are the Satellite Pour l’Observation de la Terre-VEGETATION (SPOT-VGT)-based Carbon Cycle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) LAI, the SPOT-VGT-based Bio-geophysical Parameters (BioPar) LAI product GEOV1, the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD15A2, and the Meteosat-SEVIRI-based Satellite Application Facility on Land Surface Analysis (LSA-SAF) LAI. The comparative analyses focus on data gap occurrence, on the consistency of temporal LAI profiles, on their ability to adequately reproduce the phenological cycle and on the plausibility of LAI magnitudes for major land cover types in West Africa. A detailed quantitative validation of the LAI datasets, however, was not possible due to insufficient ground LAI measurements in the study region.

A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems

Leaf area index (LAI) is a key biophysical parameter for the monitoring of agroecosystems. Conventional two-band vegetation indices based on red and near-infrared relationships such as the normalized difference vegetation index (NDVI) are well known to suffer from saturation at moderate-to-high LAI values (3–5). To bypass this saturation effect, in this work a robust alternative has been proposed for the estimation of green LAI over a wide variety of crop types. By using data from European Space Agency (ESA) campaigns SPARC 2003 and 2004 (Barrax, Spain) experimental LAI values over 9 different crop types have been collected while at the same time spaceborne imagery have been acquired using the hyperspectral CHRIS (Compact High Resolution Imaging Spectrometer) sensor onboard PROBA (Project for On-Board Autonomy) satellite. This extensive dataset allowed us to evaluate the optimal band combination through spectral indices based on normalized differences. The best linear correlation against the experimental LAI dataset was obtained by combining the 674nm and 712nm wavebands. These wavelengths correspond to the maximal chlorophyll absorption and the red-edge position region, respectively, and are known to be sensitive to the physiological status of the plant. Contrary to the NDVI (r2: 0.68), the red-edge NDI correlated strongly (r2: 0.82) with LAI without saturating at larger values. The index has been subsequently validated against field data from the 2009 SEN3EXP campaign (Barrax, Spain) that again spanned a wide variety of crop types. A linear relationship over the full LAI range was confirmed and the regression equation was applied to a CHRIS/PROBA image acquired during the same campaign. A LAI map has been derived with an RMSE accuracy of 0.6. It is concluded that the red-edge spectral index is a powerful alternative for LAI estimation and may provide valuable information for precision agriculture, e.g. when applied to high spatial resolution imagery.

LAI Improved to Dry Forest in Semiarid of the Brazil

Savannas are globally important ecosystems of great significance to human economies. Savannas exist in water-limited regions which forces tree canopies open and heterogeneous. The open canopy structure allows grass to co-dominate in the savannas by occupying different niches in space and time. Leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by vegetation (FPAR) characterize vegetation canopy functioning and energy absorption capacity. LAI and FPAR are key parameters in most ecosystem productivity models and global models of climate, hydrology, biogeochemistry and ecology. Given the above, this study aimed to develop an equation of LAI calibrated by savannah in semiarid northeastern Brazil and proposed a model to better estimate the LAI for dry forest, such as the savanna (Caatinga). The model developed in this study may be used to improve the estimates of Leaf Area Index [LAI] in dry forest with NDVI. One model for savanna-specific of leaf area index (LAI) has been developed. The use of S Curve statistical methods to calibrate the leaf area index (LAI) proved to be an efficient method. The model development gives good results in most of the LAI range known for Caatinga stands in Northeast of Brazil. The Root Mean Square Error (RMSE) calculated on an independent LAI dataset was 0.10, which is about 6% of the average measured LAI. This method offers a simple and operational alternative to application of complex and computationally intensive techniques, and could be used to design other species-specific LAIs. This study reinforces the importance of developing models to better estimate the LAI in different ecosystems since there are no similarities of the LAI between dry and humid climate.

Impact of a satellite-derived leaf area index monthly climatology in a global numerical weather prediction model

The leaf area index (LAI), defined as the one-sided green leaf area per unit ground area, is used in many numerical weather prediction (NWP) models as an indicator of the vegetation development state, which is of paramount importance to characterize land evaporation, photosynthesis, and carbon-uptake processes. LAI is often simply represented by lookup tables, dependent on the vegetation type and seasons. However, global LAI datasets derived from remote sensing observations have more recently become available. These products are based on sensors such as the Advanced Very High Resolution Radiometer (AVHRR) or the Moderate Resolution Imaging Spectroradiometer (MODIS), onboard polar orbiting satellites that can cover the entire globe within typically 3 days and with a spatial resolution of the order of 1 km. We examine the meteorological impact of satellite-derived LAI products on near-surface air temperature and humidity, which comes both from the stomatal transpiration of leaves and from the intercepted water on the surface of leaves, re-evaporating into the atmosphere. Two distinct monthly LAI climatology datasets derived respectively from AVHRR and MODIS sensors are tested. A set of forecasts and data assimilation experiments with the integrated forecasting system of the European Centre for Medium-range Weather Forecasts is performed with the monthly LAI climatology datasets as opposed to a vegetation-dependent constant LAI. The monthly LAI is shown to improve the forecasts of near-surface (screen-level) air temperature and relative humidity through its effect on evapotranspiration, with the largest impact obtained over needleleaf forests, crops, and grassland. At longer time-scales, the introduction of the monthly LAI is shown to have a positive impact on the model climate particularly during the boreal spring, where the LAI climatology has a large seasonal cycle.

Retrieval of Coarse-Resolution Leaf Area Index over the Republic of Kazakhstan Using NOAA AVHRR Satellite Data and Ground Measurements

A new multi-decade national-wide coarse-resolution data set of leaf area index (LAI) over the Republic of Kazakhstan has been developed based on data from the Advanced Very High Resolution Radiometer (AVHRR) and in situ measurements of vegetation structure. The Kazakhstan-wide LAI product has been retrieved using an algorithm based on a physical radiative transfer model establishing a relationship between LAI and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation at the per-pixel scale. The results revealed high consistencies between the produced AVHRR LAI data set and ground truth information and the 30-m resolution Landsat ETM+ LAI estimated using the similar algorithm. Differences in LAI between the AVHRR-based product and the Landsat ETM+-based product are lower than 0.4 LAI units in terms of RMSE. The produced Kazakhstan-wide LAI was also compared with the global 8-km AVHRR LAI (LAI_PAL_BU_V3) and 1-km MODIS LAI (MOD15A2 LAI) products. Results show remarkable consistency of the spatial distribution and temporal dynamics between the new LAI product and both examined global LAI products. However, the results also revealed several discrepancies in LAI estimates when comparing the global and the Kazakhstan-wide products. The discrepancies in LAI estimates were outlined and discussed.

Review of Available Products of Leaf Area Index and Their Suitability over the Formerly Soviet Central Asia

Leaf area index (LAI) is a key biophysical variable for environmental process modelling. Remotely sensed data have become the primary source for estimation of LAI at the scales from local to global. A summary of existing LAI data sets and a discussion of their appropriateness for the formerly Soviet Central Asia, especially Kazakhstan, which is known for its huge grassland area (about 2 million km2), are valuable for environmental modelling in this region. The paper gives a brief review of existing global LAI products, such as AVHRR LAI, MODIS LAI, and SPOT-VEGETATION LAI, and shows that validation of these products in Kazakhstan as well as in other countries of the formerly Soviet Central Asia has not been carried out yet. Apart from the global LAI products, there are just a few data sets retrieved by remote sensing methods at subregional and regional scales in Kazakhstan. More research activities are needed to focus on the validation of the available global LAI products over the formerly Soviet Central Asia and developing new LAI data sets suitable for application in environmental modelling at different scales in this region.

Calibration of MODIS-based gross primary production over an alpine meadow on the Tibetan Plateau

Moderate-resolution imaging spectroradiometer (MODIS) gross primary production (GPP) was compared with estimated GPP (GPP_EC) from eddy covariance measurements over an alpine meadow on the Tibetan Plateau in 2005–2007. The MODIS GPP (GPP_MOD17A2) with a bias of −0.38 g C m−2 d−1 (i.e., about −40.58% of the mean of the GPP_EC) strongly underestimated the GPP_EC for the alpine meadow. The MODIS GPP was recalibrated using measured surface meteorological data, including photosynthetically active radiation (PAR), daily minimum air temperature (Tamin) and daytime mean vapor pressure deficit (VPD), revised fractional photosynthetically active radiation (FPAR), and the revised maximum light use efficiency (LUEmax) of 0.81 g C MJ−1 (compared with the default value of 0.68 g C MJ−1 for grassland in the MODIS GPP algorithm) for the alpine meadow. The MODIS-based FPAR was about 14.70% larger than the surface-estimated FPAR using surface-measured leaf area index (LAI) data. Additionally, the temporal resolution of surface-measured LAI data was relatively low. Therefore, the linear relationship between surface-measured LAI and MODIS-based LAI was established (R2 > 0.80, P < 0.001). Then the revised MODIS LAI datasets were used to calculate the revised FPAR. The revised LUEmax was optimized from the MOD17A2 algorithm using daily surface measurements, including LAI, PAR, VPD, Tamin and GPP_EC. The calibrated MOD17A2 algorithm could explain 88% of GPP_EC variance for the alpine meadow. The bias between GPP_MOD17A2 and calculated GPP from the MOD17A2 algorithm using surface-measured PAR, Tamin, and VPD, MODIS-based FPAR, and the default LUEmax of 0.68 g C MJ−1 was −0.17 g C m−2 d−1 (i.e., about −17.60% of the mean of the GPP_EC). The underestimation of LUEmax caused a 13.78% underestimation of GPP. In contrast, the overestimation of FPAR resulted in a 7.17% overestimation of GPP. The net effect of meteorology data and FPAR resulted in a 13.84% underestimation of GPP. These results showed that MODIS-based meteorology data, FPAR, and LUEmax for the alpine meadow needed to be adjusted.

Impacts of including forest understory brightness and foliage clumping information from multiangular measurements on leaf area index mapping over North America

A new leaf area index (LAI) data set in 10 day intervals with consideration of the understory reflectance and foliage clumping effects over North America for 1 year is developed. The data set brings effectively together measurements from multiple sensors with complementary capabilities (SPOT‐VEGETATION, Multiangle Imaging Spectroradiometer, POLDER). First, the temporal consistency analysis indicated the new product is on par with other available LAI data sets currently used by the community. Second, with the removal of the background (understory in forests, moss, litter, and soil) effect on the forest overstory LAI retrieval, slightly different LAI reductions were found between needleleaf and broadleaf forests. This is caused by the more clumped nature of needleleaf forests, especially at higher LAI values, which allows more light to penetrate through the overstory canopy, making the understory more visible for equal LAI as compared to broadleaf forests. This is found over a representative set of 105 CEOS Benchmark Land Multisite Analysis and Intercomparison of Products sites in North America used for indirect validation. Third, the data set was directly validated and compared with Moderate Resolution Imaging Spectroradiometer Collection 5 LAI product using results from the BigFoot project for available forest test sites. This study demonstrates that the fusion of data inputs between multiple sensors can indeed lead to improved products and that multiangle remote sensing can help us to address effectively the issues (separating the signal from the understory and overstory, foliage clumping) that could not be solved via the means of the conventional mono‐angle remote sensing.