Unit Leaf Area Index Research Articles

Sensor-independent LAI/FPAR CDR: reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022

Abstract. Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) are critical biophysical parameters for the characterization of terrestrial ecosystems. Long-term global LAI/FPAR products, such as the moderate resolution imaging spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS), provide the fundamental dataset for accessing vegetation dynamics and studying climate change. However, existing global LAI/FPAR products suffer from several limitations, including spatial–temporal inconsistencies and accuracy issues. Considering these limitations, this study develops a sensor-independent (SI) LAI/FPAR climate data record (CDR) based on Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products. The SI LAI/FPAR CDR covers the period from 2000 to 2022, at spatial resolutions of 500 m/5 km/0.05∘, 8 d/bimonthly temporal frequencies and available in sinusoidal and WGS1984 projections. The methodology includes (i) comprehensive analyses of sensor-specific quality assessment variables to select high-quality retrievals, (ii) application of the spatial–temporal tensor (ST-tensor) completion model to extrapolate LAI and FPAR beyond areas with high-quality retrievals, (iii) generation of SI LAI/FPAR CDR in various projections and various spatial and temporal resolutions, and (iv) evaluation of the CDR by direct comparisons with ground data and indirectly through reproducing results of LAI/FPAR trends documented in the literature. This paper provides a comprehensive analysis of each step involved in the generation of the SI LAI/FPAR CDR, as well as evaluation of the ST-tensor completion model. Comparisons of SI LAI (FPAR) CDR with ground truth data suggest an RMSE of 0.84 LAI (0.15 FPAR) units with R2 of 0.72 (0.79), which outperform the standard Terra/Aqua/VIIRS LAI (FPAR) products. The SI LAI/FPAR CDR is characterized by a low time series stability (TSS) value, suggesting a more stable and less noisy dataset than sensor-dependent counterparts. Furthermore, the mean absolute error (MAE) of the CDR is also lower, suggesting that SI LAI/FPAR CDR is comparable in accuracy to high-quality retrievals. LAI/FPAR trend analyses based on the SI LAI/FPAR CDR agree with previous studies, which indirectly provides enhanced capabilities to utilize this CDR for studying vegetation dynamics and climate change. Overall, the integration of multiple satellite data sources and the use of advanced gap filling modeling techniques improve the accuracy of the SI LAI/FPAR CDR, ensuring the reliability of long-term vegetation studies, global carbon cycle modeling, and land policy development for informed decision-making and sustainable environmental management. The SI LAI/FPAR CDR is open access and available under a Creative Commons Attribution 4.0 License at https://doi.org/10.5281/zenodo.8076540 (Pu et al., 2023a).

Increasing trend in ecosystem-scale photosynthetic efficiency in the Yellow River Basin since 2000 caused by afforestation and climate change

ABSTRACT We used the leaf area index (LAI) and solar-induced chlorophyll fluorescence (SIF) to represent vegetation greenness and photosynthetic capacity, respectively, and the ratio of SIF to LAI (SIF/LAI) as an indicator of ecosystem-scale photosynthetic efficiency. We analyzed the spatial-temporal dynamics of SIF/LAI and its driving factors in the Yellow River Basin (YRB) of China from 2000 to 2021. The annual increase rate was 1.89% for SIF and 1.21% for LAI. The greater increase in SIF relative to LAI led to a significant increase in SIF/LAI ratio from 2000 to 2021, with an annual increase of 0.63%. This suggests an enhanced photosynthetic capacity per unit LAI over time. Forest cover explained 48% of the SIF/LAI rising trend, followed by temperature (26%) and the standardized precipitation-evapotranspiration index (SPEI, 16%). Regionally, the SIF/LAI had annual increasing rates of 1.25% and 1.10% in the upper and middle reaches of the YRB, respectively. Meanwhile, the SIF/LAI of the source region and lower reaches did not show a significant trend. This study deepens the understanding of the relationship between vegetation greening and photosynthetic capacity, which has implications for ecosystem management.

Uncertainty and Emergent Constraints on Enhanced Ecosystem Carbon Stock by Land Greening

AbstractSignificant land greening since the 1980s has been detected through satellite observation, forest inventory, and Earth system modeling. However, whether and to what extent global land greening enhances ecosystem carbon stock remains uncertain. Here, using 40 global models, we first detected a positive correlation between the terrestrial ecosystem carbon stock and leaf area index (LAI) over time. Then, we diagnose the source of uncertainty of simulated the sensitivities of ecosystem carbon stock to LAI based on a traceability analysis. We found that the sensitivity of gross primary productivity (GPP) to LAI is the largest contributor to the model uncertainty in more than 60% of the vegetated grids. Using the ensemble of four long‐term global data sets of GPP and three satellite LAI products from 1982 to 2014, we provided an emergent constraint on the ecosystem carbon stock increase as 0.75 ± 0.46 kg C m−2 per unit LAI over global land areas. Furthermore, the biome‐based results reveal that the tropical forest regions have the highest inter‐model variation and model bias. Overall, this study identifies the uncertainty source and provides constrained estimates of the greening effect on ecosystem carbon stock at the global scale.

Assessing land surface phenology of the savanna ecosystem in Southeast Asia using Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index from 2002 to 2020

The sustainability of the global savanna ecosystem is currently under threat from climate and anthropological change. Despite the immense threats, the existence of the savanna ecosystem is undervalued and understudied. This study examined the dynamics of the savanna ecosystem in the southern part of Southeast Asia (SEA) using MODIS leaf area index (LAI) data (MOD15A3H) with 500-m spatial resolution and 4-day data, from 2002 to 2020. The annual phenological metrics comprising the start of season (SOS), end of season (EOS), length of season (LOS), rate of greening, rate of browning, and the maximum peak values were derived from the daily interpolated data using the spline function. Additional oscillation and trend analysis using empirical ensemble decomposition methods (EEMD) was conducted to derive the nonlinear trend dynamics of the savanna ecosystem in East Nusa Tenggara (ENT). We found that the SOS at the Savanna ecosystem in SEA is 253.76 ± 2.1 days, the EOS is 161.12 ± 4.0 days, and the average LOS is 170.68 ± 6.5 days. The rainfall variabilities can explain around 35% of the variability in the LAI of the savanna ecosystem. Our EEMD analysis captured the decreasing LAI trend, showing a net change between 2002 and 2015 from 1.08 LAI units (scale of 10−2) to − 0.17 LAI units (scale of 10−2) from 2015 onwards. The result indicated a declining trend of LAI values of savanna ecosystem in ENT, thus requiring further monitoring to ensure the sustainability of this ecosystem.

Evaluation of the version 5.0 global land surface satellite (GLASS) leaf area index product derived from MODIS data

ABSTRACT The Global Land Surface Satellite (GLASS) leaf area index (LAI) product is one of the most widely used global LAI products in the scientific community. The latest (version 5) GLASS LAI product has been generated from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data. The purpose of this paper is to evaluate the quality of the version 5 GLASS LAI product. The GLASS LAI product was compared with the latest MODIS LAI product (MCD15A2 H, Collection 6) and the second version of Geoland2 (GEOV2) LAI product to evaluate their temporal and spatial discrepancies. A direct validation was conducted to compare these LAI products to the LAI values derived from the high-resolution reference maps from the Validation of Land European Remote Sensing Instruments (VALERI) and Implementing Multi-Scale Agricultural Indicators Exploiting Sentinels (IMAGINES) sites. The results show that the GLASS and GEOV2 LAI products have great spatial integrity. However, the MODIS LAI product contains many missing pixels in tropical areas. These LAI products follow fairly consistent seasonal characteristics. The spatial discrepancies of these LAI products mainly exist in forest areas, especially evergreen broadleaf forests where the GLASS LAI values are generally lower than the GEOV2 LAI values by approximately 1.0 LAI units and lower than the MODIS LAI values by 0.5 to 1.0 LAI units. The spatial distribution of these LAI products has slight discrepancies in savannahs, broadleaf crops, grasses/cereal crops and shrubs. The GLASS and GEOV2 LAI products capture a complete and reasonable temporal profile, contrasting with the MODIS LAI product, which shows dramatic fluctuations, particularly during the growing seasons. These LAI products show similar temporal trajectories and interannual variations for all biome types except evergreen broadleaf forests. The direct validation shows that the accuracy of the GLASS LAI product is better than the accuracy of the MODIS and GEOV2 LAI products. The coefficient of determination (R2) of the GLASS, MODIS and GEOV2 LAI products versus the LAI values derived from the high-resolution reference maps are 0.68, 0.47 and 0.55, respectively, and the root mean square error (RMSE) of these products are 0.86, 1.22 and 1.21, respectively.

Comparing the transpirational and shading effects of two contrasting urban tree species

Urban trees are getting increasing attention as a tool to mitigate urban heat island effects. A more functional and quantitative view of transpirational and shading effect, particularly the magnitude of both surface and air cooling potential can further strengthen motivations for urban tree planting. We investigated the transpirational and the surface cooling potential of two contrasting tree species in Munich, Germany: ring porous Robinia pseudoacacia L. and diffuse porous Tilia cordata Mill. Throughout the summer 2016 we monitored meteorological and edaphic variables and tree sap-flow along with the air temperature within and outside tree shade at different heights. With 30% higher leaf area index (LAI), double sap-flux density and sapwood area, T. cordata trees showed three times higher transpiration compared to the R. pseudoacacia. Consequently, T. cordata trees showed higher within canopy air cooling effect. Surface cooling (∆Tshade) were higher under the denser canopies of T. cordata compared to R. pseudoacacia for asphalt surfaces but ∆Tshade for grass surfaces were not significantly different under the canopies of two species. Linear regression indicated a decrease in grass surface temperature of 3 °C with every unit of LAI but for asphalt, the reduction in surface temperature was about 6 °C. Additionally, higher water using efficiencies of R. pseudoacacia coupled with higher soil moisture and radiation probably increased the grass evapotranspiration and subsequently showed positive relationship with the near ground air cooling. Therefore, species with higher canopy density might be preferred over asphalt surfaces but low water using species with lower canopy density could be chosen over grass surfaces.

Simulating International Drought Experiment field observations using the Community Land Model

Anthropogenic climate change will alter regional hydrologic cycles around the world, in part by increasing the frequency or duration of droughts in some areas. The International Drought Experiment (IDE) is investigating the impact of severe drought on terrestrial vegetation by experimentally reducing precipitation at dozens of sites. Here we implement the IDE precipitation reduction protocol using the Community Land Model (CLM). Though many model results suggest that carbon fertilization will outpace drought-caused reduction of terrestrial carbon uptake, uncertainty is large. We therefore configure CLM to consider carbon cycling impacts of reduced moisture availability without intertwining the effects of carbon fertilization or phenological changes. California hosts a number of IDE sites and a wide range of topography, climate, and biomes. CMIP5 predictions suggest 21st century California will experience droughts in excess of the 1000-year climatological record for both frequency and magnitude. CLM suggests that some regions, including much of Northern California, may experience a steeper decline in gross primary productivity (GPP) during 21st century severe droughts than during 20th century severe droughts. Vegetation in Northern California experiences virtually all of this GPP reduction during the dry season, with little wet season GPP reduction even during severe drought. Southern California vegetation experiences soil moisture GPP limitation at virtually all times, increasing substantially with drought severity. Southern California should experience a more pronounced shift in GPP seasonality and decline in magnitude relative to Northern California during droughts. Some parts of every vegetated continent see changes to drought response and seasonality similar to Southern California. Our CLM results provide drought impacts that forthcoming IDE field observations may test, can help to spatially upscale site-based IDE observations of drought impact, and provide CLM's prediction of reduced precipitation impacts per unit leaf area index.

Evaluation of four long time-series global leaf area index products

As an important vegetation biophysical variable, leaf area index (LAI) is a critical input parameter in many climate and ecological models. There exist four long time-series global LAI products since the 1980s, namely GLASS AVHRR, NCEI AVHRR, GIMMS3g and GLOBMAP. Currently, no inter-comparison studies exist to evaluate these LAI products and understand their differences for effective applications. In this study, the four long time-series global LAI products were inter-compared to evaluate their temporal and spatial discrepancies. These LAI products were also compared with MODIS LAI product and LAI values derived from high-resolution reference maps at VAlidation of Land European Remote sensing Instruments (VALERI) sites. The results show that the GLASS AVHRR and GLOBMAP LAI products are spatially complete, but the NCEI AVHRR and GIMMS3g LAI products contain many missing pixels, especially in rainforest regions. These LAI products reasonably represent the global vegetation characteristics and their seasonal variability. A relatively large discrepancy among these LAI products was observed in tropical forest regions, where the GLASS AVHRR and NCEI AVHRR LAI values achieved good agreement with the MODIS LAI values, but were between 0.5 and 1.0 LAI units lower than the GLOBMAP LAI values and higher than the GIMMS3g LAI values (more than 0.5 LAI units). Over the last three decades, the GLASS AVHRR, NCEI AVHRR, GIMMS3g LAI products show increasing trends for all biome types except evergreen needleleaf forests and deciduous needleleaf forests, but the GLOBMAP LAI product shows positive trends only for the grasses/cereal crops and shrubs. A comparison of these LAI products against the LAI values derived from high-resolution reference maps demonstrated that the GLASS AVHRR LAI values provided the better performance (RMSE=0.9014 and Bias=−0.1885) than the NCEI AVHRR LAI values (RMSE=1.0459 and Bias=−0.5695), the GIMMS3g LAI values (RMSE=1.0971 and Bias=−0.3904) and the GLOBMAP LAI values (RMSE=1.6145 and Bias=−0.9414).

Prototyping of LAI and FPAR Retrievals from MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) Data

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation are key variables in many global models of climate, hydrology, biogeochemistry, and ecology. These parameters are being operationally produced from Terra and Aqua MODIS bidirectional reflectance factor (BRF) data. The MODIS science team has developed, and plans to release, a new version of the BRF product using the multi-angle implementation of atmospheric correction (MAIAC) algorithm from Terra and Aqua MODIS observations. This paper presents analyses of LAI and FPAR retrievals generated with the MODIS LAI/FPAR operational algorithm using Terra MAIAC BRF data. Direct application of the operational algorithm to MAIAC BRF resulted in an underestimation of the MODIS Collection 6 (C6) LAI standard product by up to 10%. The difference was attributed to the disagreement between MAIAC and MODIS BRFs over the vegetation by −2% to +8% in the red spectral band, suggesting different accuracies in the BRF products. The operational LAI/FPAR algorithm was adjusted for uncertainties in the MAIAC BRF data. Its performance evaluated on a limited set of MAIAC BRF data from North and South America suggests an increase in spatial coverage of the best quality, high-precision LAI retrievals of up to 10%. Overall MAIAC LAI and FPAR are consistent with the standard C6 MODIS LAI/FPAR. The increase in spatial coverage of the best quality LAI retrievals resulted in a better agreement of MAIAC LAI with field data compared to the C6 LAI product, with the RMSE decreasing from 0.80 LAI units (C6) down to 0.67 (MAIAC) and the R2 increasing from 0.69 to 0.80. The slope (intercept) of the satellite-derived vs. field-measured LAI regression line has changed from 0.89 (0.39) to 0.97 (0.25).

Assessing crop water stress during late kharif season using Normalized Diurnal Difference Vegetation Water Content (nddVWC) of Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E)

AMSR-E derived vegetation water content (VWC) at predawn (01:30 LST) and afternoon (13:30 LST) were found to have differences relating to rainfall patterns (wet spell/dry spell) over the rainfed region during late kharif season (August–October). A new index called Normalized Diurnal Difference Vegetation Water Content (nddVWC), i.e., diurnal difference of VWC per unit leaf area index (LAI) was conceptualized to assess the spatiotemporal variations of crop water stress. The nddVWC was found to be significantly (p = 0.05) correlated with rainfall particularly at 3 days time lag. It is also found to have significant correlation with Drought Severity Index (DSI). Irrigated rice crop was used as a benchmark to characterize nddVWC to represent different intensity of crop water stress (no stress ≤0.4, moderate stress >0.4 to ≤0.8, high stress >0.8). Inter-year comparative analysis of temporal variations of nddVWC was performed to assess its sensitivity in characterizing the normal and sub-normal years in term of the intensity and the persistence of crop water stress. Persistent high/moderate stress condition was observed over Vidarbha meteorological subdivision during 2009 (drought year), whereas no stress condition was prevailing during 2005 (normal year), and the year 2008 showed an intermediate condition. The spatiotemporal patterns of nddVWC could capture the progression of crop water stress condition at regional level. The proposed index has potential to monitor crop water stress in the areas otherwise considered to be too densely vegetated for conventional soil moisture retrieval.

Fertilization Response, Light Use, and Growth Efficiency in Eucalyptus Plantations across Soil and Climate Gradients in Brazil

Fertilization increases productivity in Eucalyptus plantations, but losses in productivity associated with soil fertility continue at operational scales. In this study, we evaluated the fertilization response (FR), light use efficiency (LUE) and growth efficiency (GE), i.e., the amount of wood biomass accumulated per unit of light absorbed (LUE) and per unit of leaf area index of Eucalyptus plantations. We used a “twin plot” approach, with 161 blocks representing 52,700 ha of planted forests that spanned a broad range of edaphoclimatic conditions in southeastern Brazil. The normal plots (NP) were part of a permanent inventory network, whereas the twin plots (TP) received extra high levels of fertilization and extra weed control after fertilization. The intensive management (twin plots) led to a large increase of 5.3 Mg·ha−1·year−1 of wood increment. The region without dry periods and with soils with high clay content was most responsive to fertilization, with a 15% increment in the LUE and 10% increase in the GE of the TPs compared with those of the NPs. Our results suggested that water availability was the primary element affecting productivity and potential response to fertilization. With this information, decisions can be made on which regions should receive priority fertilization investments. However, more research is required to determine the most limiting nutrient in each type of environment.

Spectral indices for yellow canola flowers

Reproductive growth, such as by flowers, may contribute to a canopy-level signal yet there are no current indices that measure variation in flowering. This study was conducted to determine how flowers influence the overall canopy signal and what bands of light may be useful for estimating variation in flower density and leaf area index (LAI). The effects of the number of yellow flowers per unit area and LAI on canopy spectral reflectance of spring canola (Brassica napus L.) were investigated in a field study consisting of three water regimes and three fertilizer nitrogen levels near Pendleton, Oregon, USA. A band ratio of green and blue light was strongly (r2 = 0.87) related to the number of yellow flowers per unit area, whereas a ratio of near-infrared and blue light was most suitable for estimating LAI during flowering. Spectral information during flowering may improve how remote sensing is used to describe plant development and reproductive capacity during the growing season.

A Sampling Strategy for Remotely Sensed LAI Product Validation Over Heterogeneous Land Surfaces

The development of efficient and systematic groundbased spatial sampling strategies is critical for the validation of medium-resolution satellite-derived leaf area index (LAI) products, particularly over heterogeneous land surfaces. In this paper, a new sampling strategy based on high-resolution vegetation index prior knowledge (SSVIP) is proposed to generate accurate LAI reference maps over heterogeneous areas. To capture the variability across a site, the SSVIP is designed to 1) stratify the nonhomogeneous area into zones with minimum within-class variance; 2) assign the number of samples to each zone using Neyman optimal allocation; and 3) determine the spatial distribution of samples with a nearest neighbor index. The efficiency of the proposed method was examined using different vegetation types and pixel heterogeneities. The results indicate that the SSVIP approach can properly divide a heterogeneous area into different vegetation cover zones. Whereas the LAI reference maps generated by SSVIP attain the target accuracy of 0.1 LAI units in cropland and broadleaf forest sites, the current sampling strategy based on vegetation type has a root mean square error (RMSE) of 0.14 for the same number of samples. SSVIP was compared with the current sampling strategy at 24 VALERI sites, and the results suggested that samples selected by SSVIP were more representative in the feature space and geographical space, which further indicated the reasonable validation over heterogeneous land surfaces.

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.

Optimizing the sampling scheme for LAI-2000 measurements in a boreal forest

Field measurements of leaf area index (LAI) are needed for many ecological and remote sensing applications, but they are laborious and time-consuming. The sampling designs of optical LAI measurements have been mainly driven by the field-of-view of the sensor as well as the upscaling process and spatial resolution of satellite imagery. An optimized sampling design would improve the accuracy of the LAI measurements and reduce the time needed for field work. However, a systematic comparison of sampling designs for LAI measurements in boreal forests has not been done. The aim of this study was to analyze the effect of sampling designs on the accuracy of LAI estimates obtained with the LAI-2000 Plant Canopy Analyzer in a boreal forest. Field data was collected from six study sites in Hyytiälä, central Finland. Two sites were dominated by Pinus sylvestris, two sites by Picea abies, and two sites by Betula pendula. Each site had a regular 9 × 9 -grid of LAI measurement points. All sites were studied using regular and systematic sampling techniques. Statistical analysis was used to examine the site-specific number of measurement points needed in random sampling to achieve a given precision, and as a starting point for regular and systematic sampling. Results indicated that twelve randomly located measurement points are enough to characterize site-specific LAI with standard error less than 0.15 LAI units. If the twelve sampling points are organized systematically, the average standard error is reduced to 0.06 units and the coefficient of variation to less than 0.03.

Predicting leaf area index in wheat using angular vegetation indices derived from in situ canopy measurements

Canopy structure significantly affects ecosystem function by influencing light attenuation, and thus there is a great need to characterize changes in canopy structural attributes such as leaf area index (LAI). This study presents an evaluation of a set of anisotropic vegetation indices (VIs) used in the estimation of LAI in the growth cycle of wheat. An analytical two-layer canopy reflectance model (ACRM) was used to simulate a range of bidirectional reflectance with different LAI variations and view zenith angles (VZAs). A number of indices, including the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the hot spot - dark spot index (HDS), the hot spot – dark spot NDVI (NDVIHD), and a new proposed hot spot – dark spot difference index (HDDI), were selected and compared for linearity with increasing LAI in the sensitivity study. Simulation results indicated that EVI was a better candidate than NDVI for LAI estimation in linearity. HDS560 and HDS670 showed nonmonotonical patterns with increasing LAI, and better resistance to saturation limits was observed for both HDS750 and HDS870. Indices of NDVIHD (NDVIHD870 and NDVIHD750) were found to have low sensitivity at high LAI values, with a clear saturation for LAI values above 3. For the two new angular indices, HDDI705 was found to have a relatively linear relationship in an LAI range of 1–5. HDDI750 showed the best linear relationship with LAI, and no saturation was observed for LAI values in the range 1–8. A validation study was conducted for the growth cycle of two types of wheat. NDVI and EVI showed reasonable potential in the estimation of LAI, with determination coefficients R2 of 0.68 and 0.78, respectively, and corresponding root mean square errors (RMSE in LAI units) of 0.79 and 0.66. Band selection was demonstrated to have significant effects on HDS indices. HDS560 showed a very low correlation with LAI (R2 = 0.40, RMSE = 0.85), and no correlation was found between HDS670 and LAI. However, both HDS750 and HDS870 can provide moderate estimates of LAI, with R2 values of 0.76 and 0.73, respectively, and corresponding RMSE values of 0.61 and 0.65. For the NDVIHD indices, low determination coefficients R2 of 0.51 and 0.47 were obtained (RMSE = 0.81 and 0.88). For the two new angular indices, significant correlations (R2 = 0.84 and 0.85, respectively, and RMSE = 0.48 and 0.45) were observed for HDDI705 and HDDI750. These preliminary results provide certain insights for the development of future multi-angle remote sensing models for LAI estimation with satellite observations in other ecosystems.

Prototyping of Land-SAF leaf area index algorithm with VEGETATION and MODIS data over Europe

The Satellite Application Facility on Land Surface Analysis (Land-SAF) aims to provide land surface variables for the meteorological and environmental science communities from EUMETSAT satellites. This study assesses the performance of a simplified (i.e. random distribution of vegetation is assumed) version of the Land-SAF algorithm for the estimation of Leaf Area Index (LAI) when prototyped with VEGETATION (processed in CYCLOPES program) and MODIS reflectances. The prototype estimates of LAI are evaluated both by comparison with validated CYCLOPES and MODIS LAI products derived from the same sensors and directly through comparison with ground-based estimates. Emphasis is given on evaluating the impact of the algorithm and input data on LAI retrieval discrepancies. Analysis is achieved over Europe for the 2000–2003 period. The results demonstrate the capacity of the Land-SAF algorithm to retrieve consistent LAI estimates from multiple optical sensors even when their reflectances present systematic differences. High spatial and temporal consistencies between Land-SAF prototype estimates and existing LAI products are found. The differences between Land-SAF and CYCLOPES LAI are lower than their uncertainties (RMSE (relative RMSE) within 0.4 (30%)). Land-SAF prototype estimates and MODIS LAI show larger discrepancies mainly due to differences in the vegetation structure representation and algorithm assumptions (RMSE ranging from 0.2 (30%) up to 0.8 (40%)). Land-SAF prototype provides higher LAI values than MODIS for herbaceous canopies (i.e. shrubs, grasses and crops) and lower values for woody biomes (i.e. savannas and forests). Direct validation indicates that LAI estimates from prototyping of the Land-SAF algorithm with CYCLOPES and MODIS reflectances achieve similar performances (differences with ground measurements are lower than 0.5 LAI units in 60% and 50% of the cases, respectively) as CYCLOPES and MODIS LAI products. Results from this prototyping exercise appear useful for improved retrieval of LAI and constitute a step forward for refinement, validation and consolidation of the Land-SAF algorithm.

Biophysical estimation in tropical forests using JERS‐1 VNIR imagery. I: Leaf area index

Leaf area index (LAI) is often used as an indicator of green biomass. In tropical forests it can provide important inputs to ecological and physical models that simulate woody biomass accumulation, water-gas exchanges and carbon sequestration. Commonly accepted ground measurements with the LI-COR LAI-2000 instrument were found very difficult to deploy in mountainous, seasonal tropical forests in northern Thailand. As a result, spatial distribution of LAI cannot be derived from remotely sensed data via empirical regression models. Using hemispherical photography, this study developed a new approach to estimate LAI from remote sensing images. A linear unmixing model in the Modified Soil-Adjusted Vegetation Index (MSAVI) domain was first applied to estimate canopy fractional cover (fc) from the JERS-1 optical images acquired with the Very Near Infrared Radiometer (VNIR) onboard the satellite. Based on hemispherical photographs taken at the study sites, a modified Gaussian regression model was developed to relate LAI to estimated fc, with a squared correlation coefficient (R 2 ) of 0.87. Assuming the 1-m pan-sharpened IKONOS image as ground truth, the VNIR-estimated fc values agreed well with the IKONOS fc in 400 randomly selected locations (R 2 =0.79). The rms errors (RMSE) of the VNIR-estimated fc in different forest types ranged from 13% to 21%. With ground hemispherical photographs taken in the wet season, the RMSE of the LAI-fc model developed in this study was only 0.1 in LAI units, indicating that the LAI-fc regression model could be used in all seasons in the study area.

Validation of an Integrated Estimation of Loblolly Pine (Pinus taeda L.) Leaf Area Index (LAI) Using Two Indirect Optical Methods in the Southeastern United States

Abstract Quality assessment of satellite-derived leaf area index (LAI) products requires appropriate ground measurements for validation. Since the National Aeronautics and Space Administration launch of Terra (1999) and Aqua (2001), 1-km, 8-day composited retrievals of LAI have been produced for six biome classes worldwide. The evergreen needle leaf biome has been examined at numerous validation sites, but the dominant commercial species in the southeastern United States, loblolly pine (Pinus taeda), has not been investigated. The objective of this research was to evaluate an in situ optical LAI estimation technique combining measurements from the Tracing Radiation and Architecture of Canopies (TRAC) optical sensor and digital hemispherical photography (DHP) in the southeastern US P.taeda forests. Stand-level LAI estimated from allometric regression equations developed from whole-tree harvest data were compared to TRAC–DHP optical LAI estimates at a study site located in the North Carolina Sandhills Region. Within-shoot clumping, (i.e., the needle-to-shoot area ratio [γE]) was estimated at 1.21 and fell within the range of previously reported values for coniferous species (1.2–2.1). The woody-to-total area ratio (α = 0.31) was within the range of other published results (0.11–0.34). Overall, the indirect optical TRAC–DHP method of determining LAI was similar to LAI estimates that had been derived from allometric equations from whole-tree harvests. The TRAC–DHP yielded a value 0.14 LAI units below that retrieved from stand-level whole-tree harvest allometric equations. DHP alone yielded the best LAI estimate, a 0.04 LAI unit differential compared with the same allometrically derived LAI.

Carbon exchange and water use efficiency of a growing, irrigated olive orchard

We measured eddy covariance fluxes of CO 2 and H 2O over a flat irrigated olive orchard during growth, in different periods from Leaf Area Index (LAI) of 0.3–1.9; measurements of soil respiration were also collected. The daily net ecosystem exchange flux ( F NEE) was practically zero at LAI around 0.4 or when the orchard intercepted 11% of the incoming daily radiation; at the end of the experiment, with LAI of 1.9 (and the fraction of intercepted daily radiation close to 0.5), F NEE was around 10 g CO 2 m −2 day −1. The night-time ecosystem respiration ( R eco), calculated from eddy fluxes in well-mixed night conditions, show a clear but non-linear dependence with LAI; it ranged from 0.05 to 0.15 mg CO 2 m −2 s −1 (in average), being the lower limit ideally close to the heterotrophic soil respiration at the site. The gross primary production flux ( F GPP) was linearly related to LAI within the LAI range of this experiment (with 11 g CO 2 m −2 day −1 increments per unit of LAI) and to the fraction of intercepted radiation. The maximum rates of F GPP (0.75 mg CO 2 m −2 s −1) were obtained in the summer mornings of 2002, at LAI close to 1.9. F GPP was strongly modulated by vapour pressure deficit (VPD) through the canopy conductance, even in absence of water stress. Hence, especially in the summer, the maximum rates of carbon assimilation are reached always before noon. The daily course of F GPP shows a two-phase pattern, first related to irradiance and then to canopy conductance. The water use efficiency (WUE) was, in average, 3.8, 6.3 and 7 g CO 2 L −1 in 1999, 2001 and 2002, respectively, with maxima always in the early morning. Hourly WUE was strongly related to VPD (WUE = −10.25 + 22.52 × VPD −0.34). Our results suggest that drip irrigated orchards in general, and olive in particular, deserve specific carbon exchange and carbon budget studies and cannot be easily included in other biomes.