Variation In Leaf Area Index Research Articles

A simple CO2 exchange model simulates the seasonal leaf area development of peatland sedges

Sedges (Cyperaceae) are dominant plants in many northern wetlands and contribute to the carbon cycling in several ways. In order to more accurately estimate the carbon balance of a wetland it is essential to be able to simulate the temporal changes of the leaf area of the sedges. Our aim was to test the ability of a simple carbon dioxide (CO2) exchange model to predict the seasonal development of sedge leaf area index (LAI). The model simulates the development of sedge LAI that depends on carbon gain/loss in net photosynthesis, utilization of stored carbon in spring, and LAI decline due to senescence in late summer. The net photosynthesis model is driven by LAI, air temperature, water table depth and photosynthetically active radiation. We parameterized and validated the model with two years of observational LAI data from two boreal pristine peatland sites and included data points from both pristine and manipulated (drainage and warming) treatment plots. Model parameter values other than the timing of the start of senescence, LAI decline rate in senescence and size of carbon storage were independent of observational data. The model successfully simulated the seasonal course of LAI development and is thus an easy and adjustable tool for producing estimates of sedge LAI for other peatland sites. It can be adjusted to different sites although it cannot predict the exact LAI level of a site that is controlled by site characteristics. Its ability to simulate the interannual variation in this two-year dataset was inadequate so there is a need to test the model against longer time series of measured LAIs in order to validate models’ capability to predict the interannual variability of LAI.

The response of ecosystem carbon fluxes to LAI and environmental drivers in a maize crop grown in two contrasting seasons.

The eddy correlation technique was used to investigate the influence of biophysical variables and crop phenological phases on the behaviour of ecosystem carbon fluxes of a maize crop, in two contrasting growing seasons. In 2009, the reduced water supply during the early growing stage limited leaf area expansion, thus negatively affecting canopy photosynthesis. The variability of gross primary production (GPP) and ecosystem respiration (R eco) was mainly explained by seasonal variation of leaf area index (LAI). The seasonal variation of R eco was positively influenced by soil temperatures (T soil) in 2008 but not in 2009. In 2008, a contribution of both autotrophic and heterotrophic components to total R eco could be hypothesized, while during 2009, autotrophic respiration is supposed to be the most important component. Crop phenological phases affected the response of ecosystem fluxes to biophysical drivers.

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.

Implementation of a dynamic rooting depth and phenology into a land surface model: Evaluation of carbon, water, and energy fluxes in the high latitude ecosystems

Recent studies and observations have shown that northern high latitude ecosystems (NHLE) are strongly responsive to environmental changes, particularly warming temperature. Ecosystem models are important tools that help us to understand and assess the impact of environmental changes in the NHLE. However, models lack processes that are essential for modeling ecosystem dynamics for the NHLEs. In this study, NHLE-specific dynamic phenology and dynamic rooting distribution and depth parameterizations was implemented in a land surface model, the Integrated Science Assessment Model (ISAM), to improve the estimated carbon, water, and energy fluxes in the NHLs. These parameterizations account for light, water, and nutrient stresses while allocating the assimilated carbon to leaf, stem, and root pools. The model parameters related to these processes were calibrated and evaluated using measured data from 16 sites (12 fluxnet sites and 4 non-flux net sites) representative of the dominant NHLEs. By including these dynamic processes, ISAM was able to capture the measured seasonal variability in leaf area index (LAI) and root distribution in the soil layers. The evaluation of the model results suggested that without including the dynamic processes, the modeled growing season length (GSL) in the NHLE was almost two times higher, as compared to measurements. To quantify the implication of these processes on the C, water, and energy fluxes, we compared the results of two different versions of ISAM, a dynamic version that includes dynamic processes (ISAMDYN) and a static version that does not include dynamic processes (ISAMBC), with measurements from 12 eddy covariance flux sites. The results showed that ISAMDYN, unlike ISAMBC, was more capable to capture the flux site-based seasonal variability in GPP, water, and energy fluxes. Regional analysis revealed that the growing season length increased on average by about 5 days in the NHLs in the 2000s compared to 1990s.

Space-time LAI variability in Northern Puglia (Italy) from SPOT VGT data.

The vegetation space–time variability during 1999–2010 in the North of the Apulian region (Southern Italy) was analysed using SPOT VEGETATION (VGT) sensor data. Three bands of VEGETATION (RED, NIR and SWIR) were used to implement the vegetation index named reduced simple ratio (RSR) to derive leaf area index (LAI). The monthly average LAI is an indicator of biomass and canopy cover, while the difference between the annual maximum and minimum LAI is an indicator of annual leaf turnover. The space–time distribution of LAI at the catchment scale was analysed over the examined period to detect the consistency of vegetation dynamics in the study area. A diffuse increase of LAI was observed in the examined years that cannot be directly explained only in terms of increasing water availability. Thus, in order to explain such a general behaviour in terms of climatic factors, the analysis was performed upon stratification of land cover classes, focusing on the most widespread species: forest and wheat. An interesting ascending–descending behaviour was observed in the relationship between inter-annual increments of maximum LAI and rainfall, and in particular, a strong negative correlation was found when the rainfall amount in January and February exceeded a critical threshold of about 100 mm.

Exploring scale‐dependent ecohydrological responses in a large endorheic river basin through integrated surface water‐groundwater modeling

AbstractEcohydrological processes in a water‐limited environment are sensitive to both climate conditions and human activities, but the response mechanisms have rarely been explored for large endorheic river basins via an integrated modeling approach. This study established an integrated surface water‐groundwater model for the Heihe River Basin (HRB), China's second largest endorheic river basin, using GSFLOW as the modeling platform. Evapotranspiration (ET) and Leaf Area Index (LAI) data independently derived from remote sensing products were compared and correlated, respectively, with the modeling results. Scale‐dependent interrelationships among ecological, hydrological, and human‐impact (i.e., diversion and pumping) variables were revealed through multiple regression analyses. Major study findings include: (1) the independent ET and LAI data enabled the modeler to crosscheck the modeling results from a unique angle not possible with conventional groundwater and streamflow observations; (2) controlling factors for the temporal variability of ET and LAI exhibit notable scale‐dependence, reflecting distinctive climate, and human impacts on different land covers; and (3) there exists an intricate linkage between the hydrological regimes in the lower HRB and the middle HRB, essentially equivalent to a tradeoff between the ecosystem health of the lower HRB and the sustainable development of the middle HRB. Overall, the integrated modeling assisted by the independent ET and LAI data has provided a coherent understanding on the regional water cycle, and led to new insights on tackling the existing water conflicts in HRB.

On improving the accuracy of digital hemispherical photography measurements of seasonal leaf area index variation in deciduous broadleaf forests

Optical methods have been widely used to estimate seasonal changes of the leaf area index (LAI) in forest stands because they are convenient and effective; however, their accuracy in deciduous broadleaf forests has rarely been evaluated. We estimate the seasonal changes in the LAI by combining periodic observations of leaf area variation with litter collection (LAIdir) in deciduous broadleaf forests and use these estimates to evaluate the accuracy of optical LAI measurements made using digital hemispherical photography (DHP). We also propose a method to correct DHP-derived LAI (LAIDHP) values for seasonal changes in major factors that influence the determination of LAI, including the woody to total area ratio (α), the element clumping index (ΩE, using three different methods), and the photographic exposure setting (E). Before these corrections were made, LAIDHP underestimates LAIdir by 14%–55% from 21 May to 1 October but overestimates it by 78% on 12 May and by 226% on 11 October. Although pronounced differences are observed between LAIdir and LAIDHP, they are significantly correlated (R2 = 0.85, RMSE = 0.32, P < 0.001). After considering seasonal changes in α, ΩE, and E, the accuracy of LAIDHP improves markedly, with a mean difference between the corrected LAIDHP and LAIdir of <17% in all periods. The results suggest that the proposed scheme for correcting LAIDHP is useful and effective for estimating seasonal LAI variation in deciduous broadleaf forests.

Structural, physiognomic and above-ground biomass variation in savanna–forest transition zones on three continents – how different are co-occurring savanna and forest formations?

Abstract. Through interpretations of remote-sensing data and/or theoretical propositions, the idea that forest and savanna represent "alternative stable states" is gaining increasing acceptance. Filling an observational gap, we present detailed stratified floristic and structural analyses for forest and savanna stands located mostly within zones of transition (where both vegetation types occur in close proximity) in Africa, South America and Australia. Woody plant leaf area index variation was related to tree canopy cover in a similar way for both savanna and forest with substantial overlap between the two vegetation types. As total woody plant canopy cover increased, so did the relative contribution of middle and lower strata of woody vegetation. Herbaceous layer cover declined as woody cover increased. This pattern of understorey grasses and herbs progressively replaced by shrubs as the canopy closes over was found for both savanna and forests and on all continents. Thus, once subordinate woody canopy layers are taken into account, a less marked transition in woody plant cover across the savanna–forest-species discontinuum is observed compared to that inferred when trees of a basal diameter > 0.1 m are considered in isolation. This is especially the case for shrub-dominated savannas and in taller savannas approaching canopy closure. An increased contribution of forest species to the total subordinate cover is also observed as savanna stand canopy closure occurs. Despite similarities in canopy-cover characteristics, woody vegetation in Africa and Australia attained greater heights and stored a greater amount of above-ground biomass than in South America. Up to three times as much above-ground biomass is stored in forests compared to savannas under equivalent climatic conditions. Savanna–forest transition zones were also found to typically occur at higher precipitation regimes for South America than for Africa. Nevertheless, consistent across all three continents coexistence was found to be confined to a well-defined edaphic–climate envelope with soil and climate the key determinants of the relative location of forest and savanna stands. Moreover, when considered in conjunction with the appropriate water availability metrics, it emerges that soil exchangeable cations exert considerable control on woody canopy-cover extent as measured in our pan-continental (forest + savanna) data set. Taken together these observations do not lend support to the notion of alternate stable states mediated through fire feedbacks as the prime force shaping the distribution of the two dominant vegetation types of the tropical lands.

Empirical models for tracing seasonal changes in leaf area index in deciduous broadleaf forests by digital hemispherical photography

Accurate estimation of seasonal leaf area index (LAI) variations is essential for predicting forest growth, but rapid and reliable methods for obtaining such estimates have rarely been reported. In this study, direct measurements of LAI seasonal variations in deciduous broadleaf forests in China were made through leaf seasonality observations in the leaf-out season and litter collection in the leaf-fall season. Meanwhile, indirect LAI measurements were made using a digital hemispherical photography (DHP) method. Our objectives were to explore the relationship between direct and indirect LAI measurements and to recommend a rapid and reliable method to determine the seasonal variation of LAI in forests. To achieve these objectives, we first evaluated seasonal variations of the biases due to key factors (woody materials, clumping effects and incorrect automatic exposure) known to influence the estimation of LAI by DHP. The results showed that the biases due to these factors exhibited different seasonal variation patterns, and the total contribution of these factors could explain 72% of the difference between direct LAI and DHP LAI throughout the entire growing season. Second, linear regression models between direct and DHP LAI were first constructed for each 10-day period as well as the entire growing season. Significance tests were made to the differences among the models for different dates, and models for estimating LAI based on DHP in each date were aggregated to 4 periods with R2 and RMSE values of 0.91 and 0.22, 0.79 and 0.29, 0.81 and 0.14, 0.97 and 0.14, respectively. There was no significant difference between direct LAI and estimated LAI using the four models in each aggregated period (p <0.01). Thus, we confirm that these models can fully simulate the seasonal variations in LAI from the initial leaf emergence to leaf fall in deciduous broadleaf forests.

Estimating seasonal variations of leaf area index using litterfall collection and optical methods in four mixed evergreen–deciduous forests

Leaf area index (LAI), a critical parameter used in process models for estimating vegetation growth, can be measured through litterfall collection, which is usually referred to as a direct method. This method has been demonstrated to be applicable to deciduous forests, but few studies have used this method for estimating seasonal variations of LAI in mixed evergreen–deciduous forests. In this study, we proposed a practical method to estimate the seasonal variation of LAI directly by combining leaf emergent seasonality and litterfall collection (defined as LAIdir) in a mixed broadleaved-Korean pine (Pinus koraiensis) forest (BK), a Korean pine plantation (KP), a spruce–fir valley forest (SV), and a secondary birch (Betula platyphylla) forest (SB). In this direct method, the seasonal variation of LAI in a mixed forest can be quantified by tracking leaf growth and fall patterns throughout the growing season for each major evergreen and deciduous species. Using the LAIdir as a reference, we validated optical LAI (effective LAI, Le) measurements through a digital hemispherical photography (DHP) and the LAI-2000 instrument. We also explored the contribution of major sources of errors to optical LAI, including woody-to-total area ratio (α), clumping index (ΩE), needle-to-shoot area ratio (γE) and automatic exposure (E). We determined that DHP Le significantly (P<0.05) underestimated LAIdir from May to November by 48–64% in BK, KP and SV but overestimated LAIdir by 7% on average in SB. Similarly, LAI-2000 Le also significantly (P<0.05) underestimated LAIdir by an average of 27–35% in BK, KP and SV but overestimated LAIdir by 22% on average in SB. The relative contribution of E to the error in DHP Le is larger than other factors, and the γE was the largest relative contributor to the underestimation of LAI by LAI-2000. The results from our study demonstrate that seasonal variations of LAI in mixed evergreen–deciduous forests can be optically estimated with high accuracy (85% for DHP and 91% for LAI-2000), as long as accurate corrections are made to the various factors mentioned above. These close agreements between direct and optical LAI results also suggest that the direct method developed in this study is useful for tracking the seasonal variation of LAI in mixed forests.

Variabilidade do índice de área foliar em campos naturais e floresta de transição na região Sul do Amazonas

O Indice de Area Foliar (IAF) e determinante nos processos de trocas de massa e energia nos ecossistemas, sendo fundamental para o entendimento das mudancas no clima regional e na dinâmica do ecossistema. O objetivo deste trabalho foi analisar, por meio da geoestatistica, a variabilidade e dependencia espacial dos valores de indice de area foliar (IAF) em areas naturais de campo 'baixo', campo 'alto' e floresta de transicao no Sul do Amazonas. Os valores de IAF foram determinados numa malha regular com 33 pontos para cada area estudada, e a aquisicao dos dados foi feita pelo analisador de dossel LAI-2000, que permite se calcular a estrutura da vegetacao com base na radiacao que penetra na copa das arvores. Os valores medios do IAF variaram de acordo com a area estudada, com maiores valores na floresta (4,42 m²m-2), seguida pelo campo alto (2,03 m²m-2) e campo baixo (1,72 m²m-2). Em todas as areas os valores de IAF mostraram dependencia espacial com o uso da krigagem entre as amostras e apresentam distribuicao espacial diferenciada em cada area estudada: o grau de dependencia espacial foi moderado na floresta de transicao e fraco para os campos naturais, sendo que no campo alto a distância limite em que os pontos estao correlacionados e de 35,3 m. No mapa de krigagem foi possivel observar manchas com valores altos e baixos distribuidos de forma diferenciada entre as areas estudadas.

LAI Variability as a Habitat Feature Determining Reptile Occurrence: A Case Study in Large Forest Complexes in Eastern Poland

Reptile habitats are described using various indices. The definitions of such indices are crucial, as they are applied to habitat modelling for numerous species on local to continental scales. We examined the Leaf Area Index (LAI) for its value as a tool for determining reptile habitat. During measurements carried out in spring and summer months between 2011 and 2013, LAI values were assessed and surveys were conducted on reptile fauna at 11 survey sites in the Solska Forest and Roztocze National Parks areas in Eastern Poland. In total, six Squamata reptiles occurring in Poland were found. We determined that LAI can be utilized as a reptile habitat index, with reptile species associated with LAI seasonal variability as well as LAI range. Moreover, we found that the higher the LAI median value, the greater the variety of reptile species. These findings are useful for development of spatial models of habitats based on LAI as they point to the importance of its seasonal variation.

Crop growth and irrigation interact to influence surface fluxes in a regional climate-cropland model (WRF3.3-CLM4crop)

In this study, we coupled Version 4.0 of the Community Land Model that includes crop growth and management (CLM4crop) into the Weather Research and Forecasting (WRF) model Version 3.3 to better represent interactions between climate and agriculture. We evaluated the performance of the coupled model (WRF3.3-CLM4crop) by comparing simulated crop growth and surface climate to multiple observational datasets across the continental United States. The results showed that although the model with dynamic crop growth overestimated leaf area index (LAI) and growing season length, interannual variability in peak LAI was improved relative to a model with prescribed crop LAI and growth period, which has no environmental sensitivity. Adding irrigation largely improved daily minimum temperature but the RMSE is still higher over irrigated land than non-irrigated land. Improvements in climate variables were limited by an overall model dry bias. However, with addition of an irrigation scheme, soil moisture and surface energy flux partitioning were largely improved at irrigated sites. Irrigation effects were sensitive to crop growth: the case with prescribed crop growth underestimated irrigation water use and effects on temperature and overestimated soil evaporation relative to the case with dynamic crop growth in moderately irrigated regions. We conclude that studies examining irrigation effects on weather and climate using coupled climate–land surface models should include dynamic crop growth and realistic irrigation schemes to better capture land surface effects in agricultural regions.

Assessment and Validation of MODIS and GEOV1 LAI With Ground-Measured Data and an Analysis of the Effect of Residential Area in Mixed Pixel

Leaf area index (LAI) is a critical variable for simulating the carbon or nitrogen cycles and water and heat energy balance of ecosystem. MODIS and Geoland2 version 1 (GEOV1) LAI products were validated based on the ground-measured maize, winter wheat, and grass LAI data in several years. This study also investigated the residential area effect in mixed pixels on global LAI product accuracies in North Plain and Northeastern Plain in China. The MODIS and GEOV1 LAI products showed marked difference in variations of maize and winter wheat LAI at different key growth stages, and the GEOV1 LAI can present much clear differences and variations during crop growth periods. The MODIS and GEOV1 LAI products often underestimate the maize and winter wheat LAI, with the exception that GEOV1 LAI overestimate when maize LAI is large. For grass, the MODIS and GEOV1 LAI both overestimate a little. Overall, the GEOV1 LAI is often larger than the MODIS LAI. The GEOV1 LAI showed better regressions (with ${\rm{R}}^{2} $ of 0.868, 0.496, and 0.216) with the ground-measured LAI than MODIS LAI (with ${\rm{R}}^{2}$ of 0.258, 0.350, and 0.129) for maize, winter wheat, and grass, respectively. The residential area in mixed pixel make marked impact on MODIS and GEOV1 LAI data at different maize and winter wheat growth stages, and it maybe a main error source of the MODIS and GEOV1 LAI underestimations. The quadratic polynomial fitting relationships (most of the regressions ${\rm{R}}^{2} $ exceeded 0.90) can describe well the effect of residential area percent in mixed pixel on global LAI product.

Influence of Land Use Patterns on Evapotranspiration and Its Components in a Temperate Grassland Ecosystem

To better understand variation in response of components of ecosystem evapotranspiration (ET) to grassland use differences, we selected three typical land use patterns in a temperate steppe area: grazed steppe (G), steppe with grazers excluded (GE), and steppe cultivated to cropland (C). ET was divided into its components evaporation (E) and canopy transpiration (T) using herbicide and a chamber attached to a portable infrared gas analyzer (Li-6400). The results indicated that daily water consumption by ET in G was 3.30 kg m−2d−1; compared with G, ET increased significantly in GE at 13.4% and showed a trend of 6.73% increase in C. Daily water consumption by E increased 24.3% in GE relative to G, and C showed 20.2% more than GE. At 0.46, E/ET in C was significantly higher than G at 0.35. Air temperature and the vapor pressure deficit were closely correlated with variation in diurnal ET, E, and T. The leaf area index (LAI) was also positively correlated with daily ET and E varied among grassland use patterns and explained variation in E/ET (81%). Thus, variation in LAI strongly influences the overall magnitude of ecosystem ET and the composition of its components under different grassland use patterns.

Study of Subtropical Forestry Index Retrieval Using Terrestrial Laser Scanning and Hemispherical Photography

In order to retrieve gap fraction, leaf inclination angle, and leaf area index (LAI) of subtropical forestry canopy, here we acquired forestry detailed information by means of hemispherical photography, terrestrial laser scanning, and LAI-2200 plant canopy analyzer. Meanwhile, we presented a series of image processing and computer graphics algorithms that include image and point cloud data (PCD) segmentation methods for branch and leaf classification and PCD features, such as normal vector, tangent plane extraction, and hemispherical projection method for PCD coordinate transformation. In addition, various forestry mathematical models were proposed to deduce forestry canopy indexes based on the radiation transfer model of Beer-Lambert law. Through the comparison of the experimental results on many plot samples, the terrestrial laser scanner- (TLS-) based index estimation method obtains results similar to digital hemispherical photograph (HP) and LAI-2200 plant canopy analyzer taken of the same stands and used for validation. It indicates that the TLS-based algorithm is able to capture the variability in LAI of forest stands with a range of densities, and there is a high chance to enhance TLS as a calibration tool for other devices.

Estimation of leaf area index and the fraction of absorbed photosynthetically active radiation in a boreal forest

The aim of this dissertation is to assess the accuracy of different ground reference methods used to validate satellite based leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (fPAR) products. LAI and fPAR are strongly linked, although they principally and practically measure different properties: LAI quantifies the areal interphase between soil and atmosphere, whereas fPAR quantifies the energy available for photosynthesis. Until now, the development of remote sensing based methods to estimate LAI and fPAR in a boreal forest has been hindered by the scarcity of ground data, which is required to validate and develop existing algorithms. The aim of the first part of this dissertation was to assess the impacts of different methodological approaches to estimate LAI in boreal forests, and to validate satellite based LAI products. Results showed that the accuracy of ground based LAI estimates is sensitive to both the retrieval methods and sampling scheme used to collect the optical LAI data. The satellite based measurements of LAI demonstrated a large temporal variability in LAI. The second part of the dissertation focused on measuring and modeling fPAR in a boreal forest. A new scheme for measuring and modeling ground reference fPAR based on photon recollision probability was presented in this dissertation. Ground reference fPAR is usually estimated only for the forest canopy layer. This study is among the first ones to validate the new global satellite based fPAR product called GEOV1 using data of both the forest canopy and understory layers from boreal forests. Results showed that satellite based fPAR products may correspond better with the total fPAR, instead of only the forest canopy fPAR as has often been presumed.

Seasonal variations in photosynthetic parameters and leaf area index in an urban park

Parks account for a large proportion of green spaces in urban regions, but most previous studies have focused on the values of recreational services in urban parks-carbon uptake by plants in urban parks has been studied less extensively. Urban parks typically form complex landscapes in space and time by integrating multiple species with open canopies. Thus, to better understand canopy photosynthesis in urban park, measuring spatial and temporal variations in photosynthetic parameters and canopy structural variables is essential. Here, we report seasonal and spatial variations in two key photosynthetic parameters (Vcmax and Jmax which are the maximum rates of carboxylation and electron transport, respectively) and leaf area index (LAI) in Seoul Forest Park. During the peak growing season, we found an eightfold difference (20 to 149μmolm−2s−1) and fourfold difference (38 to 141μmolm−2s−1) in Vcmax and Jmax, respectively, across 10 species. Over the seasons, two woody species (Zelkova serrata (Thunb.) Makino and Prunus yedoensis Matsum), respectively showed three- to fivefold differences in Vcmax and two- to fivefold differences in Jmax. We evaluated whether leaf nitrogen contents could predict Vcmax and Jmax, and found significant correlations among the three variables during the peak growing season across 10 species, but no significant correlations among them over the seasons in the two woody species. LAI computed using in- situ observations and satellite remote-sensing imagery showed a high spatial heterogeneity during the growing season. These results highlight the considerable spatial and temporal variability in photosynthetic parameters and LAI, which implies that individual tree-based three-dimensional canopy modeling will be essential for accurate estimation of canopy photosynthesis in urban parks.

Investigating the impact of leaf area index temporal variability on soil moisture predictions using remote sensing vegetation data

The impact of leaf area index (LAI) seasonality on three-year (2005–2007) daily soil moisture predictions was investigated for two different land surface models (IBIS and HYDRUS) at the Stanley semi-arid grassland field site. Three daily LAI time series derived from different empirical NDVI–LAI relationships using the MODIS NDVI data were used in the analysis. Calibration results from both models consistently suggested that an average LAI over time, rather than a time varying daily LAI, was sufficient to reproduce daily soil moisture at our site. We did, however, find that the sensitivity of the impact of LAI time variability on soil moisture estimation was a function of soil parameters. The influence of LAI time variation on the soil moisture simulations is controlled by the sensitivity of modelled soil moisture to the average LAI values over that period, and soil parameters affected the sensitivity of the model to LAI. Those parameter sets that were most sensitive to the long-term mean LAI were also those that were the most sensitive to the time variability. In our case, model calibrations using a constant LAI adjusted the soil parameters to reduce the impact of LAI variability. Results also suggested that the LAI variability could be significant if the varying LAI approached a very low level (i.e. LAI < 1) for a significant proportion of the simulation period. This is most likely to be the case for short grasses in grasslands.

Seasonal variation of leaf area index (LAI) over paddy rice fields in NE China: Intercomparison of destructive sampling, LAI-2200, digital hemispherical photography (DHP), and AccuPAR methods

Continuous field leaf area index (LAI) measurement has become increasingly important for the validation of remote sensing LAI products. A seasonal field campaign was carried out to take continuous LAI measurements over paddy rice fields in NE China in 2012. Three indirect optical methods, LAI-2200, digital hemispherical photography (DHP), and AccuPAR, were compared with a destructive sampling method conducted concurrently. Corrections for the clumping effect were applied to the effective plant area indices (PAIeff) estimated from the indirect optical measurements.Both LAI-2200 and DHP produce consistent PAIeff estimates over the season (R2=0.76, RMSE=0.97). The clumping index (CI) values obtained from DHP generally decrease with plant growth and range between 0.63 and 0.74 during the peak growing period from day of year (DOY) 191−230. The CI values retrieved from DHP photos generally decrease with increasing view angles. The optical PAI and LAI values estimated from LAI-2200 and DHP correspond very well with the destructive values before DOY 230 (R2=0.75, RMSE=1.15 for PAI and R2=0.78, RMSE=0.74 for LAI), and the relative errors are less than 10% and 5%, respectively, for the two instruments. Omitting ring 5 for LAI-2200 generates very accurate PAI and LAI estimations during the peak season. Nevertheless, AccuPAR underestimates the PAIeff, PAI, and LAI values obtained from other methods (up to 30%). After DOY 231, the capability to detect PAI decreases significantly for both destructive and optical methods due to the leaf senescence and the DHP classification difficulty. In general, rice PAI could be accurately estimated with LAI-2200 and DHP before senescence if the clumping effect could be properly taken into account. The seasonal continuous LAI measurements obtained from this study are valuable for the validation of remote sensing LAI products.