Leaf Area Index Datasets Research Articles

A dataset of 0.05-degree leaf area index in China during 1983–2100 based on deep learning network

Leaf Area Index (LAI) is a critical parameter in terrestrial ecosystems, with high spatial resolution data being extensively utilized in various research studies. However, LAI data under future scenarios are typically only available at 1° or coarser spatial resolutions. In this study, we generated a dataset of 0.05° LAI (F0.05D-LAI) from 1983–2100 in a high spatial resolution using the LAI Downscaling Network (LAIDN) model driven by inputs including air temperature, relative humidity, precipitation, and topography data. The dataset spans the historical period (1983–2014) and future scenarios (2015–2100, including SSP-126, SSP-245, SSP-370, and SSP-585) with a monthly interval. It achieves high accuracy (R² = 0.887, RMSE = 0.340) and captures fine spatial details across various climate zones and terrain types, indicating a slightly greening trend under future scenarios. F0.05D-LAI is the first high-resolution LAI dataset and reveals the potential vegetation variation under future scenarios in China, which benefits vegetation studies and model development in earth and environmental sciences across present and future periods.

Versatile crop yield estimator

Accurate production estimates, months before the harvest, are crucial for all parts of the food supply chain, from farmers to governments. While methods have been developed to use satellite data to monitor crop development and production, they typically rely on official crop statistics or ground-based data, limiting their application to the regions where they were calibrated. To address this issue, a new method called VeRsatile Crop Yield Estimator (VeRCYe) has been developed to estimate wheat yield at the pixel and field levels using satellite data and process-based crop models. The method uses the Leaf Area Index (LAI) as the linking variable between remotely sensed data and APSIM crop model simulations. In this process, the sowing dates of each field were detected (RMSE = 2.6 days) using PlanetScope imagery, with PlanetScope and Sentinel-2 data fused into a daily 3 m LAI dataset, enabling VeRCYe to overcome the traditional trade-off between satellite data that has either high temporal or high spatial resolution. The method was evaluated using 27 wheat fields across the Australian wheatbelt, covering a wide range of pedo-climatic conditions and farm management practices across three growing seasons. VeRCYe accurately estimated field-scale yield (R2 = 0.88, RMSE = 757 kg/ha) and produced 3 m pixel size yield maps (R2 = 0.32, RMSE = 1213 kg/ha). The method can potentially forecast the final yield (R2 = 0.78–0.88) about 2 months before the harvest. Finally, the harvest dates of each field were detected from space (RMSE = 2.7 days), indicating when and where the estimated yield would be available to be traded in the market. VeRCYe can estimate yield without ground calibration, be applied to other crop types, and used with any remotely sensed LAI information. This model provides insights into yield variability from pixel to regional scales, enriching our understanding of agricultural productivity.

Data-constrained modeling of terrestrial gross primary production over the Tibetan Plateau for 2003–2019

The gross primary productivity (GPP) of terrestrial ecosystem is the largest carbon flux between atmosphere and land surface. However, accurately simulating ecosystem GPP remains a great challenge for most land surface models (LSMs) due to the biased leaf area index (LAI) simulated by the models. In this study, we use remotely sensed LAI dataset for the period 2003–2019 to drive the Farquhar model and simulate the spatial-temporal changes of GPP for the alpine ecosystem over Tibetan Plateau. The annual GPP over the Tibetan Plateau is estimated to be 540.8 ± 27.3 Tg C yr−1, which is consistent with the benchmark datasets derived from flux tower measurements and remote sensing estimations. The GPP for the past two decades has been increasing at a rate of 2.6 Tg C yr−2. The canopy greening featured by increasing LAI contributed to 27.2 % of the increasing trend in GPP, while the direct impacts from warming and wetting climate contributed to 65.2 % of GPP changes. The contribution from the direct impact of elevated atmosphere CO2 concentration is only 6.7 %. In this study we show that constraining Farquhar model with remotely sensed LAI datasets could provide reliable simulation of GPP. Given the great challenge in modeling LAI and the considerable overestimation of LAI in current LSMs, our results highlight the importance of improving LAI modeling in LSMs and the necessity of constraining LAI when tuning the parameters for photosynthesis module.

Challenges of Including Wet Grasslands with Variable Groundwater Tables in Large-Area Crop Production Simulations

Large-scale assessments of agricultural productivity necessitate integrated simulations of cropland and grassland ecosystems within their spatiotemporal context. However, simultaneous simulations face limitations due to assumptions of uniform species distribution. Grasslands, particularly those with shallow groundwater tables, are highly sensitive to water availability, undergoing rapid species composition changes. We hypothesised that predicting above-ground biomass (AGB) remains challenging due to these dynamic responses. Ten years of data from four lysimeters at a German wet grassland site, with varying water table treatments, was utilised to test this hypothesis. Correlation analysis revealed a strong positive indirect effect of the water regime on AGB, with a one-year time lag (r = 0.97). The MONICA model initially exhibited fair agreement (d = 0.69) in simulating Leaf-Area-Index (LAI) but performed poorly in replicating AGB (d = 0.3). After removing the species composition change effect from the LAI and AGB datasets, the simulation notably improved, with the overall relative root mean square error (rRMSE) of AGB decreasing from 1.55 to 0.90 between the first and second simulations. This demonstrates MONICA’s ability to predict grass growth patterns amidst changing water supply levels for constant species composition. However, it needs a competition model to capture biomass growth changes with varying water supply.

HiQ-LAI: a high-quality reprocessed MODIS leaf area index dataset with better spatiotemporal consistency from 2000 to 2022

Abstract. Leaf area index (LAI) is a crucial parameter for characterizing vegetation canopy structure and energy absorption capacity. The Moderate Resolution Imaging Spectroradiometer (MODIS) LAI has played a significant role in landmark studies due to its clear theoretical basis, extensive historical time series, extensive validation results, and open accessibility. However, MODIS LAI retrievals are calculated independently for each pixel and a specific day, resulting in high noise levels in the time series and limiting its applications in the regions of optical remote sensing. Reprocessing MODIS LAI predominantly relies on temporal information to achieve smoother LAI profiles with little use of spatial information and may easily ignore genuine LAI anomalies. To address these problems, we designed the spatiotemporal information compositing algorithm (STICA) for the reprocessing of MODIS LAI products. This method integrates information from multiple dimensions, including pixel quality information, spatiotemporal correlation, and the original retrieval, thereby enabling both “reprocessing” and “value-added data” with respect to the existing MODIS LAI products, leading to the development of the high-quality LAI (HiQ-LAI) dataset. Compared with ground measurements, HiQ-LAI shows better performance than the original MODIS product with a root-mean-square error (RMSE) or bias decrease from 0.87 or −0.17 to 0.78 or −0.06, respectively. This is due to the improvement of HiQ-LAI with respect to capturing the seasonality in vegetation phenology and reducing abnormal time-series fluctuations. The time-series stability (TSS) index, which represents temporal stability, indicated that the area with smooth LAI time series expanded from 31.8 % (MODIS) to 78.8 % (HiQ) globally, and this improvement is more obvious in equatorial regions where optical remote sensing cannot usually achieve good performance. We found that HiQ-LAI demonstrates superior continuity and consistency compared with raw MODIS LAI from both spatial and temporal perspectives. We anticipate that the global HiQ-LAI time series, generated using the STICA procedure on the Google Earth Engine (GEE) platform, will substantially enhance support for diverse global LAI time-series applications. The 5 km 8 d HiQ-LAI datasets from 2000 to 2022 are available at https://doi.org/10.5281/zenodo.8296768 (Yan et al., 2023).

Performance assessment of the Sentinel-2 LAI products and data fusion techniques for developing new LAI datasets over the high-altitude Himalayan forests

The present study evaluates the accuracy of SNAP-Sentinel-2 Prototype Processor (SL2P) derived Leaf Area Index (LAI) and proposes a new simple method to generate new datasets of LAI through data fusion. Rigorous optimization of the data fusion approaches (Kalman filter and Linear weighted) were performed for the generation of new LAI products over the complex hilly terrain of the Himalayan region. The results showed a good correlation (r = 0.79) and low error (RMSE = 1.63) between SNAP-derived (at 20 m) and ground-observed LAI. A lower correlation was obtained between the ground observed LAI data and the corresponding global LAI products for the Moderate Resolution Imaging Spectroradiometer (MODIS) (r = 0.1, RMSE = 1.19), Copernicus Global Land Service (CGLS) (r = 0.1, RMSE = 0.61) and the Visible Infrared Imaging Radiometer Suite (VIIRS) (r = 0.04, RMSE = 1.25). Notably, after implementing the data fusion, both SNAP-derived LAI and Global LAI products exhibited much-improved performance statistics with ground observed data sets.

Impacts of land cover changes on biogenic emission and its contribution to ozone and secondary organic aerosol in China

Abstract. The greening impacts on China from 2000 to 2017 led to an increase in vegetated areas and thus enhanced biogenic volatile organic compound (BVOC) emissions. BVOCs are regarded as important precursors for ozone (O3) and secondary organic aerosol (SOA). As a result, accurate estimation of BVOC emissions is critical to understand their impacts on air quality. In this study, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) v2.1 was used to investigate the impact of different leaf area index (LAI) and land cover (LC) datasets on BVOC emissions in China in 2016, and the effects on O3 and SOA were evaluated based on the Community Multiscale Air Quality (CMAQ) modeling system. Three LAI satellite datasets of the Global LAnd Surface Satellite (GLASS), the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2H version 6 (MOD15), and the Copernicus Global Land Service (CGLS), as well as three LC satellite datasets of the MODIS MCD12Q1 LC products, the Copernicus Climate Change Service (C3S) LC products, and the CGLS LC products, were used in five parallel experiments (cases: C1–C5). Results show that changing LAI and LC datasets of the model input has an impact on BVOC estimations. BVOC emissions in China ranged from 25.42 to 37.39 Tg in 2016 and were mainly concentrated in central and southeastern China. Changing the LC inputs for the MEGAN model has a more significant difference in BVOC estimates than using different LAI datasets. The combination of C3S LC and GLASS LAI performs better in the CMAQ model, indicating that it is the better choice for BVOC estimations in China. The highest contribution of BVOCs to O3 and SOA can reach 12 ppb and 9.8 µg m−3, respectively. Changing the MEGAN inputs further impacts the concentrations of O3 and SOA, especially changing LC datasets. The relative difference between MCD12Q1 LC and C3S LC is over 52 % and 140 % in O3 and biogenic SOA (BSOA) in central and eastern China. The BSOA difference is mainly attributed to the isoprene SOA (ISOA), a major contributor to BSOA. The relative differences in ISOA between different cases are up to 160 % in eastern China. Therefore, our results suggest that the uncertainties in MEGAN inputs should be fully considered in future O3 and SOA simulations.

Reprocessed MODIS Version 6.1 Leaf Area Index Dataset and Its Evaluation for Land Surface and Climate Modeling

Satellite-based leaf area index (LAI) products, such as the MODIS LAI, play an essential role in land surface and climate modeling research, from regional to global scales. However, data gaps and high-level noise can exist, thus limiting their applications to a broader scope. Our previous work has reprocessed the MODIS LAI Collection 5 (C5) product, and the reprocessed data have been widely used these years. In this study, the MODIS C6.1 LAI data were reprocessed to broaden its application as a successor. We updated the integrated two-step method that is used for MODIS C5 LAI and implemented it into the MODIS C6.1 LAI product. Comprehensive evaluations for the original and reprocessed products were conducted. The results showed that the reprocessed LAI data had better performance in validation against reference maps. In addition, the site scale time series of reprocessed data was much smoother and more consistent with adjacent values. The global scale comparison showed that, though the MODIS C6.1 LAI does have improvements in ground validation with LAI reference maps, its spatial continuity, temporal continuity, and consistency showed little improvement when compared to C5. In contrast, the reprocessed data were more spatiotemporally continuous and consistent. Based on this evaluation, some suggestions for using various MODIS LAI products were given. This study assessed the quality of these different versions of MODIS LAI products and demonstrated the improvement of the reprocessed C6.1 data, which we recommended for use as a substitute for the reprocessed C5 data in land surface and climate modeling.

Appraisal of Climate Response to Vegetation Indices over Tropical Climate Region in India

Extreme climate events are becoming increasingly frequent and intense due to the global climate change. The present investigation aims to ascertain the nature of the climatic variables association with the vegetation variables such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI). In this study, the impact of climate change with respect to vegetation dynamics has been investigated over the Indian state of Haryana based on the monthly and yearly time-scale during the time period of 2010 to 2020. A time-series analysis of the climatic variables was carried out using the MODIS-derived NDVI and LAI datasets. The spatial mean for all the climatic variables except rainfall (taken sum for rainfall data to compute the accumulated rainfall) and vegetation parameters has been analyzed over the study area on monthly and yearly basis. The liaison of NDVI and LAI with the climatic variables were assessed at multi-temporal scale on the basis of Pearson correlation coefficients. The results obtained from the present investigation reveals that NDVI and LAI has strong significant relationship with climatic variables during the cropping months over study area. In contrast, during the non-cropping months, the relationship weakens but remains significant at the 0.05 significance level. Furthermore, the rainfall and relative humidity depict strong positive relationship with NDVI and LAI. On the other, negative trends were observed in case of other climatic variables due to the limitations of NDVI viz. saturation of values and lower sensitivity at higher LAI. The influence of aerosol optical depth was observed to be much higher on LAI as compared to NDVI. The present findings confirmed that the satellite-derived vegetation indices are significantly useful towards the advancement of knowledge about the association between climate variables and vegetation dynamics.

Uncertainty propagation in a global biogeochemical model driven by leaf area data

Satellite-observed leaf area index (LAI) is often used to depict vegetation canopy structure and photosynthesis processes in terrestrial biogeochemical models. However, it remains unclear how the uncertainty of LAI among different satellite products propagates to the modeling of carbon (C), nitrogen (N), and phosphorus (P) cycles. Here, we separately drive a global biogeochemical model by three satellite-derived LAI products (i.e., GIMMS LAI3g, GLASS, and GLOBMAP) from 1982 to 2011. Using a traceability analysis, we explored the propagation of LAI-driven uncertainty to modeled C, N, and P storage among different biomes. The results showed that the data uncertainty of LAI was more considerable in the tropics than in non-tropical regions, whereas the modeling uncertainty of C, N, and P stocks showed a contrasting biogeographic pattern. The spread of simulated C, N, and P storage derived by different LAI datasets resulted from assimilation rates of elements in shrubland and C3 grassland but from the element residence time (τ) in deciduous needle leaf forest and tundra regions. Moreover, the assimilation rates of elements are the main contributing factor, with 67.6, 93.2, and 93% of vegetated grids for the modeled uncertainty of C, N, and P storage among the three simulations. We further traced the variations in τ to baseline residence times of different elements and the environmental scalars. These findings indicate that the data uncertainty of plant leaf traits can propagate to ecosystem processes in global biogeochemical models, especially in non-tropical forests.

Improving the Quality of MODIS LAI Products by Exploiting Spatiotemporal Correlation Information

The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) product is critical for global terrestrial carbon monitoring and ecosystem modeling. However, MODIS LAI is calculated on a pixel-by-pixel and day-by-day basis without using spatial or temporal correlation information, which leads to its high sensitivity of LAI to uncertainties in observed reflectance resulting in an increased noise level in time series. While exploiting prior knowledge is a common practice to fill gaps in observations, little research has been conducted on reducing noisy fluctuations and improving the overall quality of the MODIS LAI product. To address this issue, we proposed a Spatio-Temporal Information Composition Algorithm (STICA), which directly introduces prior Spatio-temporal correlation and Multiple Quality Assessment (MQA) information into the existing MODIS LAI product. STICA reduces the noise level and improves the quality of the product while maintaining the original physically-based (Radiative Transfer Model, RTM) LAI production process. In our analysis, the R2 increased from 0.79 to 0.81, and the RMSE decreased from 0.81 to 0.68 compared to the ground-based LAI reference. The improvement was more pronounced with the degradation of the data quality. STICA reduced noisy fluctuations in the LAI time series to varying degrees among eight biome types. In the Amazon Forest, STICA significantly improved the time-series stability of LAI. Moreover, STICA can effectively eliminate abnormal declines in time series and correct for extreme outliers in LAI. We expect that the MODIS LAI Reanalyzed product generated by this method will better support the application of high-quality LAI datasets.

Spatially Distributed Overstory and Understory Leaf Area Index Estimated from Forest Inventory Data

Forest change affects the relative magnitudes of hydrologic fluxes such as evapotranspiration (ET) and streamflow. However, much is unknown about the sensitivity of streamflow response to forest disturbance and recovery. Several physically based models recognize the different influences that overstory versus understory canopies exert on hydrologic processes, yet most input datasets consist of total leaf area index (LAI) rather than individual canopy strata. Here, we developed stratum-specific LAI datasets with the intent of improving the representation of vegetation for ecohydrologic modeling. We applied three pre-existing methods for estimating overstory LAI, and one new method for estimating both overstory and understory LAI, to measurements collected from a probability-based plot network established by the US Forest Service’s Forest Inventory and Analysis (FIA) program, for a modeling domain in Montana, MT, USA. We then combined plot-level LAI estimates with spatial datasets (i.e., biophysical and remote sensing predictors) in a machine learning algorithm (random forests) to produce annual gridded LAI datasets. Methods that estimate only overstory LAI tended to underestimate LAI relative to Landsat-based LAI (mean bias error ≥ 0.83), while the method that estimated both overstory and understory layers was most strongly correlated with Landsat-based LAI (r2 = 0.80 for total LAI, with mean bias error of -0.99). During 1984-2019, interannual variability of understory LAI exceeded that for overstory LAI; this variability may affect partitioning of precipitation to ET vs. runoff at annual timescales. We anticipate that distinguishing overstory and understory components of LAI will improve the ability of LAI-based models to simulate how forest change influences hydrologic processes.

The Spatiotemporal Response of Vegetation Changes to Precipitation and Soil Moisture in Drylands in the North Temperate Mid-Latitudes

Vegetation growth in drylands is highly constrained by water availability. How dryland vegetation responds to changes in precipitation and soil moisture in the context of a warming climate is not well understood. In this study, warm drylands in the temperate zone between 30 and 50° N, including North America (NA), the Mediterranean region (MD), Central Asia (CA), and East Asia (EA), were selected as the study area. After verifying the trends and anomalies of three kinds of leaf area index (LAI) datasets (GLASS LAI, GLEAM LAI, and GLOBAMAP LAI) in the study area, we mainly used the climate (GPCC precipitation and ERA5 temperature), GLEAM soil moisture, and GLASS LAI datasets from 1981 to 2018 to analyze the response of vegetation growth to changes in precipitation and soil moisture. The results of the three mutually validated LAI datasets show an overall greening of dryland vegetation with the same increasing trend of 0.002 per year in LAI over the past 38 years. LAI and precipitation exhibited a strong correlation in the eastern part of the NA drylands and the northeastern part of the EA drylands. LAI and soil moisture exhibited a strong correlation in the eastern part of the NA drylands, the eastern part of the MD drylands, the southern part of the CA drylands, and the northeastern part of the EA drylands. The results of this study will contribute to the understanding of vegetation dynamics and their response to changing water conditions in the Northern Hemisphere midlatitude drylands.

A Continuous Change Tracker Model for Remote Sensing Time Series Reconstruction

It is hard for current time series reconstruction methods to achieve the balance of high-precision time series reconstruction and explanation of the model mechanism. The goal of this paper is to improve the reconstruction accuracy with a well-explained time series model. Thus, we developed a function-based model, the CCTM (Continuous Change Tracker Model) model, that can achieve high precision in time series reconstruction by tracking the time series variation rate. The goal of this paper is to provide a new solution for high-precision time series reconstruction and related applications. To test the reconstruction effects, the model was applied to four types of datasets: normalized difference vegetation index (NDVI), gross primary productivity (GPP), leaf area index (LAI), and MODIS surface reflectance (MSR). Several new observations are as follows. First, the CCTM model is well explained and based on the second-order derivative theorem, which divides the yearly time series into four variation types including uniform variations, decelerated variations, accelerated variations, and short-periodical variations, and each variation type is represented by a designed function. Second, the CCTM model provides much better reconstruction results than the Harmonic model on the NDVI, GPP, MSR, and LAI datasets for the seasonal segment reconstruction. The combined use of the Savitzky–Golay filter and the CCTM model is better than the combinations of the Savitzky–Golay filter with other models. Third, the Harmonic model has the best trend-fitting ability on the yearly time series dataset, with the highest R-Square and the lowest RMSE among the four function fitting models. However, with seasonal piecewise fitting, the four models all achieved high accuracy, and the CCTM performs the best. Fourth, the CCTM model should also be applied to time series image compression, two compression patterns with 24 coefficients and 6 coefficients respectively are proposed. The daily MSR dataset can achieve a compression ratio of 15 by using the 6-coefficients method. Finally, the CCTM model also has the potential to be applied to change detection, trend analysis, and phenology and seasonal characteristics extractions.

ValLAI_Crop, a validation dataset for coarse-resolution satellite LAI products over Chinese cropland

Numerous validation efforts have been conducted over the last decade to assess the accuracy of global leaf area index (LAI) products. However, such efforts continue to face obstacles due to the lack of sufficient high-quality field measurements. In this study, a fine-resolution LAI dataset consisting of 80 reference maps was generated during 2003–2017. The direct destructive method was used to measure the field LAI, and fine-resolution LAI images were derived from Landsat images using semiempirical inversion models. Eighty reference LAI maps, each with an area of 3 km × 3 km and a percentage of cropland larger than 75%, were selected as the fine-resolution validation dataset. The uncertainty associated with the spatial scale effect was also provided. Ultimately, the fine-resolution reference LAI dataset was used to validate the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product. The results indicate that the fine-resolution reference LAI dataset builds a bridge to link small sampling plots and coarse-resolution pixels, which is extremely important in validating coarse-resolution LAI products.

Estimating global aerodynamic parameters in 1982–2017 using remote-sensing data and a turbulent transfer model

The bulk surface properties, including canopy height (h), zero-plane displacement height (d), roughness length (z0), and aerodynamic resistance (rb and rd), are crucial biophysical parameters that influence momentum, energy and mass exchanges at the land-atmosphere interface. The variabilities of these parameters are important for understanding possible impacts of the ecosystem on climate change, yet they have not been systematically evaluated due to the lack of large-scale, long-term observations. Here we provide global estimates of these bulk aerodynamic parameters, including d, z0, rb, and rd, for the period 1982–2017 based on remote-sensed leaf area index (LAI), h, and plant functional type dependent canopy morphological characteristics. The global h estimate is acquired from LAI using a semi-empirical relation in which the coefficients have been optimized based on the canopy height product from the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat). Two LAI products, Global Land Surface Satellite (GLASS) and Global Inventory Modeling and Mapping Studies (GIMMS), are used to calculate canopy height and parameters separately. The products derived from the above two LAI datasets agree very well (spatial correlation coefficient, SCC = 0.99, relative root-mean-square-error, rRMSE = 8.28% for h, SCC = 0.99, rRMSE = 12.15% for d, and SCC = 0.98, rRMSE = 13.78% for z0). Verification of the products against in-situ canopy height records from the FLUXNET and eddy-covariance (EC)-based d and z0 estimates shows that the estimates can reproduce the annual means and seasonal variations of the bulk surface properties. We found significant positive trends in global h (0.04–0.05% year−1), d (0.07% year−1) and z0 (0.07% year−1) associated with the Earth greening, but they are different from the trend in LAI (0.14–0.25% year−1). The differences between the trends in LAI and the trends in derived parameters are also found in different latitudes. For instance, in the Northern Hemisphere Polar region, the GLASS LAI increases by 0.48% year−1, leading to positive trends of 0.03% year−1 in h, 0.18% in d, and 0.20% year−1 in z0. The above results highlight the importance of comprehensively considering changes in LAI, canopy height, and aerodynamic parameters when evaluating the effects of ecosystem change on climate. This long-term dataset can be used in climate models to assess the response of bulk aerodynamic parameters to climate changes and the impact of vegetation dynamics on regional and global climate.

Daily leaf area index from photosynthetically active radiation for long term records of canopy structure and leaf phenology

Leaf area index (LAI) is a critical biophysical indicator that describes foliage abundance in ecosystems. An accurate and continuous estimation of LAI is therefore desirable to quantify ecosystem status and function (e.g. carbon and water exchange between the land surface and the atmosphere). However, deriving accurate LAI measurements at regular temporal intervals remains challenging, requiring either destructive sampling or manual collection of canopy gap fraction measurements at discrete time intervals. In this study, we present four methods to obtain continuous LAI data, simply derived from above and below canopy measurements of photosynthetically active radiation (PAR) at the Borden Forest Research Station from 1999 to 2018. We compared LAI derived using the four PAR-based methods to independent measurements of LAI from optical methods and the MODIS satellite LAI product. LAI derived from all four PAR-based methods captured the seasonal changes in observed and remotely sensed LAI and showed a close linear correspondence with one another (R2 of 0.55 to 0.76 compared to MODIS LAI, and R2 of 0.78 to 0.84 compared to LAI-2000 measurements). A PAR-based method using Miller's Integral theorem showed the strongest linear relationship with LAI-2000 measurements (R2=0.84, p<0.001, SE=0.40). In many years MODIS LAI indicated an earlier start of season and earlier end of season than the daily PAR-based LAI datasets showing systematic biases in the MODIS assessment of growing season.The four PAR-based LAI methods outlined in this study provide an LAI dataset of unprecedented temporal resolution. These methods will allow precise determination of phenological events, improve leaf to canopy scaling in process-based models, and provide valuable insight into dynamic vegetation responses to global climate change.

Using the Negative Soil Adjustment Factor of Soil Adjusted Vegetation Index (SAVI) to Resist Saturation Effects and Estimate Leaf Area Index (LAI) in Dense Vegetation Areas.

Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs’ saturation in the Apiacás area (i.e., X = −0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = −0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI.

Long-Term Variation of Global GEOV2 and MODIS Leaf Area Index (LAI) and Their Uncertainties: An Insight into the Product Stabilities

Leaf area index (LAI) is an essential climate variable that is crucial to understand the global vegetation change. Long-term satellite LAI products have been applied in many global vegetation change studies. However, these LAI products contain various uncertainties that are not been fully considered in current studies. The objective of this study is to explore the uncertainties in the global LAI products and the uncertainty variations. Two global LAI datasets—the European Geoland2 Version 2 (GEOV2) and Moderate Resolution Imaging Spectroradiometer (MODIS) (2003-2019)—were investigated. The qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) embedded in the product quality layers were analyzed to identify the temporal anomalies in the quality profile. The results show that the global GEOV2 (0.042/10a) and MODIS (0.034/10a) LAI values have steadly increased from 2003 to 2019. The global LAI uncertainty (0.016/10a) and relative uncertainty (0.3%/10a) from GEOV2 have also increased gradually, especially during the growing season from April to October. The uncertainty increase is larger for woody biomes than for herbaceous types. Contrastingly, the MODIS LAI product uncertainty remained stable over the study period. The uncertainty increase indicated by GEOV2 is partly attributed to the sensor shift in the product series. Further algorithm enhancement is necessary to improve the cross-sensor performance. This study highlights the importance of studying the LAI uncertainty and the uncertainty variation. Temporal variations in the LAI products and the product quality revealed herein have significant implications on global vegetation change studies.

Polarization of Light Reflected by Grass: Modeling Using Visible-Sunlit Areas

The Bidirectional polarization distribution function (BPDF) of land surfaces is important for studies of land surfaces and aerosol. With the availability of a huge number of polarization measurements, several semi-empirical BPDF models have been proposed. However, these models do not pay much attention to canopy structure, which is fundamental for generation of polarization. In this article, we propose a new BPDF model using canopy structure information, which is parameterized by visible-sunlit areas. It is evaluated over grassland using POLDER BPDF and MODIS leaf area index data sets. Experiments suggest that compared to Nadal–Bréon and Litvinov models, the new BPDF model reduces root-mean-square error by 7% and 10%, respectively. The new BPDF model also provides better performance when it is fitted using observations clustered by sun zenith angle. The new BPDF model thus provides an effective tool for the study of land surface polarization.