Surface Leaf Area Index Research Articles

A High Spatiotemporal Enhancement Method of Forest Vegetation Leaf Area Index Based on Landsat8 OLI and GF-1 WFV Data

The leaf area index (LAI) is a crucial parameter for analyzing terrestrial ecosystem carbon cycles and global climate change. Obtaining high spatiotemporal resolution forest stand vegetation LAI products over large areas is essential for an accurate understanding of forest ecosystems. This study takes the northwestern part of the Inner Mongolia Autonomous Region (the northern section of the Greater Khingan Mountains) in northern China as the research area. It also generates the LAI time series product of the 8-day and 30 m forest stand vegetation growth period from 2013 to 2017 (from the 121st to the 305th day of each year). The Simulated Annealing-Back Propagation Neural Network (SA-BPNN) model was used to estimate LAI from Landsat8 OLI, and the multi-period GaoFen-1 WideField-View satellite images (GF-1 WFV) and the spatiotemporal adaptive reflectance fusion mode (STARFM) was used to predict high spatiotemporal resolution LAI by combining inversion LAI and Global LAnd Surface Satellite-derived vegetation LAI (GLASS LAI) products. The results showed the following: (1) The SA-BPNN estimation model has relatively high accuracy, with R2 = 0.75 and RMSE = 0.38 for the 2013 LAI estimation model, and R2 = 0.74 and RMSE = 0.17 for the 2016 LAI estimation model. (2) The fused 30 m LAI product has a good correlation with the LAI verification of the measured sample site (R2 = 0.8775) and a high similarity with the GLASS LAI product. (3) The fused 30 m LAI product has a high similarity with the GLASS LAI product, and compared with the GLASS LAI interannual trend line, it accords with the growth trend of plants in the seasons. This study provides a theoretical and technical reference for forest stand vegetation growth period LAI spatiotemporal fusion research based on high-score data, and has an important role in exploring vegetation primary productivity and carbon cycle changes in the future.

Seasonal and interannual variations of ecosystem photosynthetic characteristics in a semi-arid grassland of Northern China

Abstract The ecosystem apparent quantum yield (α), maximum rate of gross CO2 assimilation (Pmax) and daytime ecosystem respiration rate (Rd), reflecting the physiological functioning of ecosystem, are vital photosynthetic parameters for the estimation of ecosystem carbon budget. Climatic drivers may affect photosynthetic parameters both directly and indirectly by altering the response of vegetation. However, the relative contribution and regulation pathway of environmental and physiological controls remain unclear, especially in semi-arid grasslands. We analyzed seasonal and interannual variations of photosynthetic parameters derived from eddy-covariance observation in a typical semi-arid grassland in Inner Mongolia, Northern China, over 12 years from 2006 to 2017. Regression analyses and a structural equation model (SEM) were adopted to separate the contributions of environmental and physiological effects. The photosynthetic parameters showed unimodal seasonal patterns and significantly interannual variations. Variations of air temperature (Ta) and soil water content (SWC) drove the seasonal patterns of photosynthetic parameters, while SWC predominated their interannual variations. Moreover, contrasting with the predominant roles of Ta on α and Rd, SWC explained more variance of Pmax than Ta. Results of SEM revealed that environmental factors impacted photosynthetic parameters both directly and indirectly through regulating physiological responses reflected by stomatal conductance at the canopy level. Moreover, leaf area index (LAI) directly affected α, Pmax and Rd and dominated the variation of Pmax. On the other hand, SWC influenced photosynthetic parameters indirectly through LAI and canopy surface conductance (gc). Our findings highlight the importance of physiological regulation on the photosynthetic parameters and carbon assimilation capacity, especially in water-limited grassland ecosystems.

A data-driven approach to estimate leaf area index for Landsat images over the contiguous US

Leaf Area Index (LAI) is a fundamental vegetation biophysical variable serving as an essential input to many land surface and atmospheric models. Long-term LAI maps are typically generated with satellite images at moderate spatial resolution (0.25 to 1 km), such as those from the Moderate Resolution Imaging Spectroradiometer (MODIS). While useful for regional-scale land surface modeling, these moderate resolution products often cannot resolve spatial heterogeneity important for many agricultural and hydrological applications. This paper proposes an approach to map LAI at 30-m resolution based on Landsat images for the Contiguous US (CONUS) consistent with the MODIS product, aimed at multi-scale modeling applications. The algorithm was driven by 1.6 million spatially homogeneous samples derived from MODIS LAI and Landsat surface reflectance products from 2006 to 2018. Based on these samples, we trained separate random forest models to estimate LAI from Landsat surface reflectance for eight biomes of the National Land Cover Database (NLCD). A balanced sample design regarding the saturation status of MODIS LAI and a machine-learning-based noise detection technique were introduced to mitigate the trade-off in estimation accuracy between medium LAI (e.g., 3 to 4, unsaturated) and high LAI (e.g., 4–6, saturated).This approach was evaluated using ground measurements from 19 National Ecological Observatory Network (NEON) sites and eight independent sites from other sources. These sites comprise a representative sample of forests, grasslands, shrublands, and croplands across the US. For NEON sites, the LAI estimates show an overall Root Mean Squared Error (RMSE) of 0.8 with r2 of 0.88. For the eight independent sites, the Landsat LAI algorithm achieves RMSE between 0.52 and 0.91. The uncertainty in Landsat estimated LAI varies across biomes and locations. The proposed algorithm was implemented on the Google Earth Engine platform, allowing for the rapid generation of long-term high-resolution LAI records from the 1980s using Landsat images (code is available at https://github.com/yanghuikang/Landsat-LAI). Our findings also highlight the importance of sample balance on regression-based modeling in remote sensing applications.

Evaluation of vegetation and evapotranspiration changes in Iran using satellite data and ground measurements

Evapotranspiration (ET) is a critical component of the water cycle on a global scale. Changes in leaf area index (LAI) influence ET changes. Satellite products are among the effective methods of estimating ET and LAI at local, regional, and global scales. Therefore, the purpose of our study was to evaluate three global remote sensing products of ET and two products of LAI and investigate their simultaneous changes in Iran over the 2000 to 2015 period. Selected ET products include Global Land Evaporation Amsterdam Model (GLEAM), Global Land Data Assimilation System (GLDAS2.0), and Moderate Resolution Imaging Spectroradiometer ET (MODIS ET) products. LAI products include MODIS LAI and Global Land Surface Satellite (GLASS) LAI products. Validation using the 23 selected evaporation stations showed that among the three ET products MODIS has the best performance. Three ET products consistently show an increasing trend in annual ET over a large part of Iran. The mean annual trend values obtained from GLEAM3.0a, GLDAS, and MODIS ET products are 2.022, 1.359, and 0.429 mm/year, respectively. Among LAI products, GLASS had the highest correlation with the selected ET product (0.78). On average, the trend of intra-annual changes of LAI across Iran did not show a significant increasing or decreasing trend. Moreover, in areas where no significant change was observed in LAI, ET changes were unpredictable and depended on other factors. Overall, the results showed a relatively strong correlation between the selected ET and vegetation satellite products in the study area.

A Data Assimilation Method for Simultaneously Estimating the Multiscale Leaf Area Index From Time-Series Multi-Resolution Satellite Observations

Current global leaf area index (LAI) products are generally produced from single-temporal satellite observations acquired by a single sensor. These LAI products are usually spatiotemporally discontinuous and inaccurate for some vegetation types in many areas, which limit the applications of these LAI products to the understanding of land dynamics. In this paper, a new data assimilation method was proposed to estimate multiscale and temporally continuous LAI values from multi-sensor time-series satellite observations with different spatial resolutions. An ensemble multiscale tree (EnMsT) was used to establish the conversion relationships between different spatial resolution LAI values, and dynamic models of the LAI at different spatial scales were constructed to evolve LAI at the corresponding spatial scales over time. At each time step, a multiscale Kalman filter (MKF) was introduced to fuse the predicted LAI values from the dynamic models at different spatial scales and to construct a forecasted EnMsT. When satellite observations were available, an ensemble multiscale filter (EnMsF) technique was applied to update the LAI values at each node of the EnMsT. The method was applied to estimate temporally continuous multiscale LAI values from the time series of Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+) surface reflectance data and Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data at several sites with different vegetation types. The estimated multiscale LAI values were compared with the MODIS and GEOV2 LAI products, and the reference LAI values at the corresponding scales aggregated from the high-resolution LAI surface images. The estimated LAI values with the finest spatial resolution were also validated by ground measurements from the selected sites. The results show that the new method is able to simultaneously estimate temporally continuous multiscale LAI values by assimilating satellite observations with different spatial resolutions, and the estimated multiscale LAI values are well consistent with the reference LAI values at the corresponding scales over the selected sites. The root-mean-square error (RMSE) and coefficient of determination of the retrieved LAI values at the finest spatial scale against the ground measurements over the selected sites are 0.539 and 0.788, respectively.

Influences of leaf area index and albedo on estimating energy fluxes with HOLAPS framework

The High resOlution Land Atmosphere surface Parameters from Space (HOLAPS) programme provides a modeling system to maximize the use of satellite-based products and ensure internally consistent estimation of surface water and energy fluxes. Leaf area index (LAI) and land surface albedo are two key parameters for estimation of latent and sensible heat fluxes with HOLAPS. Thus, to facilitate the generation of long-term high accuracy latent and sensible heat fluxes, high quality global long-term LAI and land surface albedo datasets are essential. The Quality Assurance for Essential Climate Variables (QA4ECV) project released quality-assured long-term LAI and albedo datasets with traceable and reliable uncertainty information provided in the dataset. Taking MODIS-BNU-LAI and MODIS albedo as reference, different global long-term LAI and albedo datasets including GlobAlbedo, QA4ECV and GLOBMAP were investigated for estimation of latent/sensible heat fluxes with HOLAPS in this study. The results show that all albedo datasets show similar accuracy for estimation of latent and sensible heat fluxes when validated against FLUXNET observations. The QA4ECV LAI leads to worse latent heat flux estimation due to its use of effective LAI rather than green leaf LAI. Sensitivity analysis also shows that the HOLAPS estimated latent heat flux (LE) is more sensitive to uncertainty in LAI than land surface albedo. Overall, the combined use of QA4ECV albedo and GLOBMAP LAI is suggested for estimation of latent/sensible heat fluxes with HOLAPS. The root mean square differences (RMSD) between estimations and FLUXNET measurements are 54 (30) W/m2 for hourly (monthly) latent heat flux, and 80.5 (24.5) W/m2 for sensible heat flux, which are comparable to estimates with MODIS and other reported studies.

Integration of Landsat-8 Thermal and Visible-Short Wave Infrared Data for Improving Prediction Accuracy of Forest Leaf Area Index

Leaf area index (LAI) has been investigated in multiple studies, either by means of visible/near-infrared and shortwave-infrared or thermal infrared remotely sensed data, with various degrees of accuracy. However, it is not yet known how the integration of visible/near and shortwave-infrared and thermal infrared data affect estimates of LAI. In this study, we examined the utility of Landsat-8 thermal infrared data together with its spectral data from the visible/near and shortwave-infrared region to quantify the LAI of a mixed temperate forest in Germany. A field campaign was carried out in August 2015, in the Bavarian Forest National Park, concurrent with the time of the Landsat-8 overpass, and a number of forest structural parameters, including LAI and proportion of vegetation cover, were measured for 37 plots. A normalised difference vegetation index threshold method was applied to calculate land surface emissivity and land surface temperature and their relations to LAI were investigated. Next, the relation between LAI and eight commonly used vegetation indices were examined using the visible/near-infrared and shortwave-infrared remote sensing data. Finally, the artificial neural network was used to predict the LAI using: (i) reflectance data from the Landsat-8 operational land imager (OLI) sensor; (ii) reflectance data from the OLI sensor and the land surface emissivity; and (iii) reflectance data from the OLI sensor and land surface temperature. A stronger relationship was observed between LAI and land surface emissivity compared to that between LAI and land surface temperature. In general, LAI was predicted with relatively low accuracy by means of the vegetation indices. Among the studied vegetation indices, the modified vegetation index had the highest accuracy for LAI prediction (R2CV = 0.33, RMSECV = 1.21 m2m−2). Nevertheless, using the visible/near-infrared and shortwave-infrared spectral data in the artificial neural network, the prediction accuracy of LAI increased (R2CV = 0.58, RMSECV = 0.83 m2m−2). The integration of reflectance and land surface emissivity significantly improved the prediction accuracy of the LAI (R2CV = 0.81, RMSECV = 0.63 m2m−2). For the first time, our results demonstrate that the combination of Landsat-8 reflectance spectral data from the visible/near-infrared and shortwave-infrared domain and thermal infrared data can boost the estimation accuracy of the LAI in a forest ecosystem. This finding has implication for the prediction of other vegetation biophysical, or possibly biochemical variables using thermal infrared satellite remote sensing data, as well as regional mapping of LAI when coupled with a canopy radiative transfer model.

Bayesian maximum entropy data fusion of field-observed leaf area index (LAI) and Landsat Enhanced Thematic Mapper Plus-derived LAI

Accurate high-resolution leaf area index (LAI) reference maps are necessary for the validation of coarser-resolution satellite-derived LAI products. In this article, we propose an efficient method based on the Bayesian Maximum Entropy (BME) paradigm to combine field observations and Landsat Enhanced Thematic Mapper Plus (ETM+)-derived LAI surfaces in order to produce more accurate LAI reference maps. This method takes into account the uncertainties associated with field observations and with the regression relationship between ETM+-derived LAI and field measurements to perform a non-linear prediction of LAI, the variable of interest. In order to demonstrate the difference by soft data and hard data, we estimate the LAI reference maps by three BME interpolation methods, BME1, BME2, and BME3. BME1 and BME2 perform maximum estimation and mean estimation, respectively, by taking the ETM+-derived LAI as interval soft data and the field LAI measurements as hard data. BME3 is utilized when ETM+-derived LAI surfaces are processed as uniform probability soft data and field measurements are processed as Gaussian probability soft data. Three study sites are selected from the BigFoot project (NASA's Earth Observing System validation programme) (http://www.fsl.orst.edu/larse/bigfoot/index.html). In regard to the mean and standard deviation of LAI surfaces, standard deviation predicted by BME methods has lower values than that derived by ETM+. The mean value of the BME-predicted LAI, which takes into account the uncertainties of field measurements, is lower than that of ETM+-derived LAI at each study site. A comparison with field measurements shows that BME1, BME2, and BME3 have root mean square errors (RMSE) of 0.455, 0.485, and 0.517 and average biases of −0.017, −0.010, and −0.304, respectively. The RMSEs and biases of the predicted LAI surfaces are less when compared to the ETM+-derived LAI, which has the average RMSE and bias of 0.642 and −0.080. When the field measurements are processed as soft data, the predicted LAI by BME3 has more bias than those of the predictions by BME1 and BME2, but has less RMSE than that of the ETM+-derived LAI by 0.125. In summary, BME is capable of incorporating the spatial autocorrelation and the uncertainties in the field LAI measurements into the LAI surface estimation to produce a more accurate LAI surface with less RMSE in validation. The maximum estimation has relatively better accuracy than the mean estimation. The results indicate that the BME is a promising method for fusing point-scale and area-scale data.

Hourly and Daytime Evapotranspiration from Grassland Using Radiometric Surface Temperatures

Determination of evapotranspiration (E) is needed for many applications in agriculture, hydrology, and meteorology. The spatial variability of leaf area index (LAI) and soil water availability makes it impractical to modelEover heterogeneous lands using ground‐based techniques. Remote sensing can be a good source for both LAI and radiometric surface temperature (Ts) estimates. However, remotely sensed soil moisture content is not suitable forEprediction. In this study, we propose a procedure to estimateEusingTs. The method uses a dimensionless temperature ΔT, defined as (Ts–Ta)/(Tmax–Ta), whereTais the air temperature andTmaxis the surface temperature that would occur if all the net radiation (Rn) was converted to sensible heat flux and no evaporation occurred. This approach has been tested on data from two grassland sites in Oklahoma and Kansas. Root mean square differences between hourly predicted and measuredEranged from 30 to 50 W m−2. The slope andr2for the zero‐intercept linear regression between hourly estimated and measuredEranged from 1.01 to 1.37 and 78 to 0.94, respectively. Daytime conservation of evaporative fraction (EF =E/Rn) was used to extrapolate from hourly to daytimeE. The slope andr2of the linear regression between daytime estimated and measuredEranged from 0.89 to 1.07 and 0.69 to 0.9, respectively. These results demonstrate that, for grassland, the model may give good estimates ofEwhenTaandTsare available.

Hourly and Daytime Evapotranspiration from Grassland Using Radiometric Surface Temperatures

Determination of evapotranspiration (E) is needed for many applications in agriculture, hydrology, and meteorology. The spatial variability of leaf area index (LAI) and soil water availability makes it impractical to model E over heterogeneous lands using ground-based techniques. Remote sensing can be a good source for both LAI and radiometric surface temperature (Ts) estimates. However, remotely sensed soil moisture content is not suitable for E prediction. In this study, we propose a procedure to estimate E using Ts. The method uses a dimensionless temperature ΔT, defined as (Ts – Ta)/(Tmax – Ta), where Ta is the air temperature and Tmax is the surface temperature that would occur if all the net radiation (Rn) was converted to sensible heat flux and no evaporation occurred. This approach has been tested on data from two grassland sites in Oklahoma and Kansas. Root mean square differences between hourly predicted and measured E ranged from 30 to 50 W m−2. The slope and r2 for the zero-intercept linear regression between hourly estimated and measured E ranged from 1.01 to 1.37 and 78 to 0.94, respectively. Daytime conservation of evaporative fraction (EF = E/Rn) was used to extrapolate from hourly to daytime E. The slope and r2 of the linear regression between daytime estimated and measured E ranged from 0.89 to 1.07 and 0.69 to 0.9, respectively. These results demonstrate that, for grassland, the model may give good estimates of E when Ta and Ts are available.