Leaf Area Index Distribution Research Articles

Estimation of the Leaf Area Index and Light Intensity in a Brazilian Tropical Forest Fragment

In order to apply techniques aimed at the restoration and recovery of forest fragments, it is necessary to understand the ecological succession of the respective biome and the factors influencing its dynamics. Luminosity is one of the key factors for comprehending the cycle of native forests, influenced not only by climatic seasonality but also by the canopy structure and forest strata. The Leaf Area Index (LAI) is directly linked to plant growth conditions. The objective of this study is to map the distribution of LAI in the canopy of a Mixed Ombrophilous Forest fragment using non-destructive methods and to quantify luminosity. The research was conducted around the University Campus of the State University of the Midwest - UNICENTRO, in the municipality of Irati, Paraná, Brazil. These variables underwent numerical terrain modeling using the free software SPRING, resulting in two spatial representations, each containing four classes for each variable. The data underwent statistical analysis using Spearman's and Pearson's correlations, resulting in coefficients of 0.89 and 0.9, respectively. Thus, both variables are strongly linked, with LAI as the independent variable and luminosity as the dependent variable. The use of numerical terrain modeling for the variables proved appropriate, as it allowed the expression of visual features that can be observed within the experimental area.

Estimation of the Leaf Area Index and Light Intensity in a Brazilian Tropical Forest Fragment

In order to apply techniques aimed at the restoration and recovery of forest fragments, it is necessary to understand the ecological succession of the respective biome and the factors influencing its dynamics. Luminosity is one of the key factors for comprehending the cycle of native forests, influenced not only by climatic seasonality but also by the canopy structure and forest strata. The Leaf Area Index (LAI) is directly linked to plant growth conditions. The objective of this study is to map the distribution of LAI in the canopy of a Mixed Ombrophilous Forest fragment using non-destructive methods and to quantify luminosity. The research was conducted around the University Campus of the State University of the Midwest - UNICENTRO, in the municipality of Irati, Paraná, Brazil. These variables underwent numerical terrain modeling using the free software SPRING, resulting in two spatial representations, each containing four classes for each variable. The data underwent statistical analysis using Spearman's and Pearson's correlations, resulting in coefficients of 0.89 and 0.9, respectively. Thus, both variables are strongly linked, with LAI as the independent variable and luminosity as the dependent variable. The use of numerical terrain modeling for the variables proved appropriate, as it allowed the expression of visual features that can be observed within the experimental area.

Inversion of maize leaf area index from UAV hyperspectral and multispectral imagery

The accurate estimationof Leaf area index (LAI)is of great importance for evaluating crop growth in precision agriculture. Although previous studies have confirmed great advantages ofunmanned aerial vehicle(UAV) remote sensing for LAI estimation in the field, accurate, reliable and efficient LAI estimation with practical applications still faces challenges due to model limitations and variations in the spectral and spatial scales of UAV remote sensing. In this study, we constructed the hybrid inversion models (HIMs) for estimating maize LAI using UAV hyperspectral and multispectral data, respectively. The HIMs combines the advantages of radiative transfer models and machine learning regression algorithms, and are optimized by active learning (AL) algorithm. The results reveal that the inclusion of AL in the HIMs an effectively improve the accuracy of the model. The Gaussian process regression-based HIM with AL (GPR-AL-HIM) obtained the best performance in LAI estimation (R2 = 0.86, RMSE = 0.30 and NRMSE = 10.16 %). Meanwhile, GPR-AL-HIM was also determined to outperform the physical model based on the look-up-table (LUT) and the empirical statistical model based on vegetation indices. The model was validated with another independent dataset and also obtained a high accuracy (R2 = 0.84, RMSE = 0.23 and NRMSE = 11.78 %). In addition, we also explore the effects of the UAV spectral (multispectral and hyperspectral) and image spatial resolution on LAI inversion. The results reveal that the hyperspectral data exhibit an advantage over the multispectral data for LAI inversion using the GPR-AL-HIM. The accuracy of the GPR-AL-HIM decreased with increasing spatial resolution, but the accuracy varied less within a certain spatial resolution range (e.g., R2of 0.86–0.84 and RMSE of 0.30–0.33 for hyperspectral images at 1.5–15 cm spatial resolution). Furthermore, the LAI distribution in the study area was accurately mapped using the GPR-AL-HIM with the hyperspectral and multispectral images, with the latter exhibiting lower uncertainties. The GPR-AL-HIM is mainly aimed at maize, and in the future, we will explore the applicability of this model in other crops. This work provides a reference for the design of a monitoring scheme with crop parameters based on UAV remote sensing in precision agriculture.

The development of a global LAI and FAPAR product using GCOM-C/SGLI data

The Japan Aerospace Exploration Agency (JAXA) launched the Global Change Observation Mission - Climate (GCOM-C) satellite on December 23rd, 2017. As a part of the standard products of JAXA’s GCOM-C satellite, we developed the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product. In comparison to other global LAI and FAPAR products by the National Aeronautics and Space Administration (NASA) and the Copernicus Global Land Service (CGLS), our products have the following advantageous features: high spatial and temporal resolution (250 m and daily); the retrieval algorithm (look-up-table) is based on “the Forest Light Environmental Simulator” (FLiES) which is a full 3-D canopy radiative transfer model as well as a vegetation landscape model and a global forest landscape map created for this study. The algorithm and ancillary data sets are completely independent of the existing products that are more or less influenced by the MODIS LAI/FAPAR, providing an alternate and independent source of LAI and FAPAR. The dataset has been created since April 2018 and is freely available on JAXA’s G-Portal site (https://gportal.jaxa.jp/gpr). As a result of accuracy assessment with in-situ data, the Root Mean Square Errors (RMSEs) for total LAI and FAPAR were 0.776 and 0.112, respectively. We also compared our LAI and FAPAR estimates with three existing global datasets (PROBA-V/OLCI, MODIS and VIIRS product). The spatial distribution of LAI and FAPAR showed different characteristics among the datasets although their temporal trends were similar. The differences in estimation results among datasets due to differences in the retrieval algorithms, base map (or land cover data) and input data need to be evaluated in the future.

Mapping Crop Leaf Area Index and Canopy Chlorophyll Content Using UAV Multispectral Imagery: Impacts of Illuminations and Distribution of Input Variables

Leaf area index (LAI) and canopy chlorophyll content (CCC) are important indicators that describe the growth status and nitrogen deficiencies of crops. Several studies have been performed to estimate LAI and CCC using multispectral cameras onboard an unmanned airborne vehicle (UAV) system. However, the impacts of illuminations during UAV flight and problems of how to invert still need more investigation. UAV flights with a multispectral camera were performed under clear (diffuse ratio 0) and cloudy illumination conditions (diffuse ratio 1) over rapeseed, wheat and sunflower (only clear) fields. One-dimension radiative transfer model PROSAIL was run twice to generate a clear-sky model and a cloudy-sky model, respectively. The LAI and CCC of flights under a clear sky were inverted from the clear-sky model, and the flights under cloudy conditions were inverted from both clear-sky and cloudy-sky models to compare the results. Moreover, three Look-Up-Tables (LUT) were built with same input variables but different distributions of LAI. Results showed that LAI from uniform dense LUT had better correspondence with ground measurements for all crops (R2 = 0.51~0.69). The illumination condition had little impact on small to medium LAI (LAI < 5) and CCC. However, the inversion of imageries during cloudy sky conditions from the clear-sky model led to an overestimation of high LAI values.

Summer Maize Growth Estimation Based on Near-Surface Multi-Source Data

Rapid and accurate crop chlorophyll content estimation and the leaf area index (LAI) are both crucial for guiding field management and improving crop yields. This paper proposes an accurate monitoring method for LAI and soil plant analytical development (SPAD) values (which are closely related to leaf chlorophyll content; we use the SPAD instead of chlorophyll relative content) based on the fusion of ground–air multi-source data. Firstly, in 2020 and 2021, we collected unmanned aerial vehicle (UAV) multispectral data, ground hyperspectral data, UAV visible-light data, and environmental cumulative temperature data for multiple growth stages of summer maize, respectively. Secondly, the effective plant height (canopy height model (CHM)), effective accumulation temperature (growing degree days (GDD)), canopy vegetation index (mainly spectral vegetation index) and canopy hyperspectral features of maize were extracted, and sensitive features were screened by correlation analysis. Then, based on single-source and multi-source data, multiple linear regression (MLR), partial least-squares regression (PLSR) and random forest (RF) regression were used to construct LAI and SPAD inversion models. Finally, the distribution of LAI and SPAD prescription plots was generated and the trend for the two was analyzed. The results were as follows: (1) The correlations between the position of the hyperspectral red edge and the first-order differential value in the red edge with LAI and SPAD were all greater than 0.5. The correlation between the vegetation index, including a red and near-infrared band, with LAI and SPAD was above 0.75. The correlation between crop height and effective accumulated temperature with LAI and SPAD was above 0.7. (2) The inversion models based on multi-source data were more effective than the models made with single-source data. The RF model with multi-source data fusion achieved the highest accuracy of all models. In the testing set, the LAI and SPAD models’ R2 was 0.9315 and 0.7767; the RMSE was 0.4895 and 2.8387. (3) The absolute error between the extraction result of each model prescription map and the measured value was small. The error between the predicted value and the measured value of the LAI prescription map generated by the RF model was less than 0.4895. The difference between the predicted value and the measured value of the SPAD prescription map was less than 2.8387. The LAI and SPAD of summer maize first increased and then decreased with the advancement of the growth period, which was in line with the actual growth conditions. The research results indicate that the proposed method could effectively monitor maize growth parameters and provide a scientific basis for summer maize field management.

Modeling the Leaf Area Index of Inner Mongolia Grassland Based on Machine Learning Regression Algorithms Incorporating Empirical Knowledge

Leaf area index (LAI) is one of the key biophysical indicators for characterizing the growth and status of vegetation and is also used in modeling earth system processes. Machine learning algorithms (MLAs) such as random forest regression (RFR), artificial neural network regression (ANNR) and support vector regression (SVR) based on satellite data have been widely used for the estimation of LAI. However, the selection of input variables has a great impact on the estimation performance of MLAs. In this study, we aimed to improve the LAI inversion model of Inner Mongolia grassland based on MLAs incorporating empirical knowledge. Firstly, we used the ANNR, SVR and RFR approaches, respectively, to rank the input variables including vegetation indices, climate factors, soil factors and topography factors and found that Normalized Difference Phenology Index (NDPI) contributed the most to LAI estimation. Secondly, we selected four sets of input variables, namely, all variables—A, model selected variables—B, overlapping variables—C and self-defined variables—D, respectively. Subsequently, we built twelve LAI estimation models (RFR-A, RFR-B, RFR-C, etc.) based on three MLAs and four sets of input variables. The evaluation of them showed the RFR produced higher prediction accuracy, followed by ANNR and SVR. Furthermore, the RFR-D presented the highest accuracy in predicting LAI (R2 = 0.55, RMSE = 0.37 m2/m2, MAE = 0.29 m2/m2). Finally, we compared our results with MODIS LAI and GEOV2 LAI products and found that all of them showed a similar spatial distribution of grassland LAI in the four sub-regions covering all grassland types, but our model exhibited larger LAI values in the desert steppe and smaller LAI values in the others. These findings demonstrated that MLAs incorporating empirical knowledge could improve the accuracy of modelling LAI and further study is necessary to reduce the uncertainty in LAI mapping in grassland.

Spatial Distribution of Optimal Plant Cover and Its Influencing Factors for Populus simonii Carr. on the Bashang Plateau, China

The Bashang Plateau is the core zone of the agro-pastoral ecotone in northern China and represents an ecological barrier for preventing the invasion of wind-blown sand in the Beijing–Tianjin–Hebei region. Increasing plant cover to control soil erosion is an effective measure to address land degradation; however, plant cover is different from climatic conditions. In this study, we determined the optimal spatial distribution of Populus simonii Carr., which is a widely planted species used for revegetation on the Bashang Plateau. A modified Biome-BGC model was used to simulate the dynamics of the net primary productivity (NPP), actual evapotranspiration (AET), and leaf-area index (LAI). The model was validated using field-observed tree-ring and MODIS AET and NPP data. The dynamics of AET, NPP and LAI for P. simonii at 122 representative sites in the study area for the period 1980–2019 were simulated by the validated model. The results showed that the spatial distributions of mean AET, NPP, and LAI generally decreased from southeast to northwest. The ranges of optimal plant cover in terms of maximum LAI for P. simonii were 3.3 in the Fengning–Weichang area, 1.9 in the Shangyi–Zhangbei–Guyuan area and 1.3 in the Kangbao area. Mean annual precipitation (MAP), elevation, soil texture and mean annual temperature were the main factors influencing the distribution of AET, NPP and LAI. As the MAP decreased, the correlations between AET, NPP, LAI and precipitation gradually decreased. In different subregions, the factors influencing optimal-plant-cover distribution varied significantly. These quantitative findings provide the optimal plant cover for the dominant tree in different subregions and provide useful information for land degradation management on the Bashang Plateau.

Seagrass Depth Distribution Mirrors Coastal Development in the Mexican Caribbean – An Automated Analysis of 800 Satellite Images

The seagrass Thalassia testudinum is the dominant habitat-builder in coastal reef lagoons of the Caribbean, and provides vital ecosystem services including coastal protection and carbon storage. We used a remote sensing methodology to map T. testudinum canopies over 400 km of coastline of the eastern Yucatán Peninsula, comparing the depth distribution of canopy density, in terms of leaf area index (LAI), to a previously established ecological model of depth and LAI for this species in oligotrophic conditions. The full archive of Sentinel-2 imagery from 2016 to 2020 was applied in an automated model inversion method to simultaneously estimate depth and LAI, covering ∼900 km2 of lagoon with approximately 800 images. Data redundancy allowed for statistical tests of change detection. Achieved accuracy was sufficient for the objectives: LAI estimates compared to field data had mean absolute error of 0.59, systematic error of 0.04 and r2 > 0.67 over a range of 0–5. Bathymetry compared to 46,000 ICESat-2 data points had a mean absolute error of 1 m, systematic error less than 0.5 m, and r2 > 0.88 over a range of 0–15 m. The estimated total area of seagrass canopy was consistent with previously published estimates of ∼580 km2, but dense canopies (LAI > 3), which are the primary contributors to below-ground carbon storage, comprise only ∼40 km2. Within the year-to-year variation there was no change in overall seagrass abundance 2017–2020, but localised statistically significant (p < 0.01) patches of canopy extension and retraction occurred. 2018 and 2019 were affected by beaching of pelagic Sargassum and dispersion as organic matter into the lagoon. The multi-year analysis enabled excluding this influence and provided an estimate of its extent along the coast. Finally, the distribution of LAI with depth was consistent with the ecological model and showed a gradient from north to south which mirrored a well-established gradient in anthropogenic pressure due to touristic development. Denser canopies were more abundant in developed areas, the expected growth response to nutrient enrichment. This increase in canopy density may be a useful early bio-indicator of environmental eutrophication, detectable by remote sensing before habitat deterioration is observed.

Mapping Leaf Area Index of restored mangroves using WorldView-2 imagery in Perancak Estuary, Bali, Indonesia

There have been efforts to restore and rehabilitate mangrove forests to compensate for their loss or degradation. Approaches for monitoring these efforts are increasingly developed, including assessment of the dynamic changes in restored mangroves using the biophysical tree parameter called Leaf Area Index (LAI). This study aimed to (1) apply a semi-empirical approach to estimate LAI using WorldView-2 imagery, (2) compare the LAI distributions of three different mangrove forest types (natural, mixed, and planted) at the mangrove restoration site in Perancak Estuary, Bali, Indonesia, and (3) evaluate the applicability of LAI as an indicator for monitoring mangrove growth in restoration areas. To achieve these objectives, a high-spatial-resolution WorldView-2 image data (2 m pixel size) and Normalized Difference Vegetation Index (NDVI) were used. A semi-empirical approach was used to correlate field LAI and NDVI values at the corresponding location. The results showed that the best regression function between the two variables was the third-order polynomial (R2 = 0.98). Overall, the estimated LAI values ranged from 0 to 9.56 (mean = 1.87, σ = 0.95, max. Accuracy = 97.6%) and differed between the three mangrove forest types. The comparison revealed that high LAI was associated with planted mangrove forest type where Rhizophora sp. dominated. It indicates that LAI is a useful indicator for assessing mangrove biophysical conditions and, as such, can be used for monitoring mangrove restoration activities and outcomes.

Using Microwave Profile Radar to Estimate Forest Canopy Leaf Area Index: Linking 3D Radiative Transfer Model and Forest Gap Model

Profile radar allows direct characterization of the vertical forest structure. Short-wavelength, such as Ku or X band, microwave data provide opportunities to detect the foliage. In order to exploit the potential of radar technology in forestry applications, a helicopter-borne Ku-band profile radar system, named Tomoradar, has been developed by the Finnish Geospatial Research Institute. However, how to use the profile radar waveforms to assess forest canopy parameters remains a challenge. In this study, we proposed a method by matching Tomoradar waveforms with simulated ones to estimate forest canopy leaf area index (LAI). Simulations were conducted by linking an individual tree-based forest gap model ZELIG and a three-dimension (3D) profile radar simulation model RAPID2. The ZELIG model simulated the parameters of potential local forest succession scene, and the RAPID2 model utilized the parameters to generate 3D virtual scenes and simulate waveforms based on Tomoradar configuration. The direct comparison of simulated and collected waveforms from Tomoradar could be carried out, which enabled the derivation of possible canopy LAI distribution corresponding to the Tomoradar waveform. A 600-m stripe of Tomoradar data (HH polarization) collected in the boreal forest at Evo in Finland was used as a test, which was divided into 60 plots with an interval of 10 m along the trajectory. The average waveform of each plot was employed to estimate the canopy LAI. Good results have been found in the waveform matching and the uncertainty of canopy LAI estimation. There were 95% of the plots with the mean relative overlapping rate (RO) above 0.7. The coefficients of variation of canopy LAI estimates were less than 0.20 in 80% of the plots. Compared to lidar-derived canopy effective LAI estimation, the coefficient of determination was 0.46, and the root mean square error (RMSE) was 1.81. This study established a bridge between the Ku band profile radar waveform and the forest canopy LAI by linking the RAPID2 and ZELIG model, presenting the uncertainty of forest canopy LAI estimation using Tomoradar. It is worth noting that since the difference of backscattering contribution is caused by both canopy structure and tree species, similar waveforms may correspond to different canopy LAI, inducing the uncertainty of canopy LAI estimation, which should be noticed in forest parameters estimation with empirical methods.

Retrieval of forest vertical leaf area index and clumping index through field measurement and remote sensing techniques: A review

Forest vertical stratification directly affects the transfer of solar radiation and water evapotranspiration in the canopy. Consequently, leaf photosynthesis varies in different parts of the canopy, affecting the growth of trees and the succession process of the plant community. The vertical distribution of forest structural parameters, such as the leaf area index (LAI) and clumping index (CI), is critical not only for understanding of the forest carbon, nitrogen, and water cycles and remote sensing radiative transfer processes, but also for forest monitoring and management practices. Forest vertical LAI and CI can be obtained through field measurement and remote sensing inversion methods. For field measurements, LAI-2200, digital hemispherical photography, and photosynthetically active radiation sensors are frequently used instruments. Vertical structural measurements are usually carried out through destructive sampling, tower-base measurement, and mobile elevators. In general, field measurements are more accurate; however, they are time-consuming and laborious, making them unsuitable for large-scale applications. Remote sensing technology uses the passive optical method, the light detection and ranging (LiDAR) technology, and synthetic aperture radar (SAR) methods to estimate forest vertical LAI and CI. The classical passive optical method is based on either the empirical vegetation index estimation method or physical model inversion method. The vegetation index method establishes an empirical relationship between vegetation structural parameters and vegetation indices. Subsequently, the relationship is used to estimate the structural parameters for different layers. The physical model inversion method is based on a physical radiative transfer model. In general, the application of both passive optical and SAR methods in forest vertical parameter retrieval is limited. As an active remote sensing technology, the LiDAR technology has shown its unique advantages in the inversion of vertical structural parameters. Based on the platform used, LiDAR can be classified into three categories: Terrestrial laser scanning (TLS), airborne laser scanning (ALS), and spaceborne laser scanning. The basic rationale of the LiDAR technology is based on the Beer-Lambert Law, which estimates canopy LAI and CI from the canopy gap fraction; both empirical statistical and physical inversion methods are used during the process. The use of LiDAR to estimate LAI and CI is gaining popularity worldwide, but most of the research is based on TLS and ALS. The development of new space-based LiDAR technologies, such as the Global Ecosystem Dynamics Investigation on the International Space Station, will greatly advance the acquisition of forest vertical structural information on a global scale. However, the relatively complex operation and data processing requirements have limited its widespread application. The vertical distribution of forest LAI can be represented by the normal, Weibull, beta, chi-square, and Johnson’s S-B distributions. The CI values usually decrease with the increase in canopy height because the upper part of the canopy usually has larger gaps. However, mature global vertical LAI and CI products that can meet the application requirements are currently unavailable. Due to the limits of ground measurements, forest vertical LAI and CI retrieved from remote sensing lack sufficient ground validation. In field measurements, the LAI values of trees, shrubs, and herbs should be acquired simultaneously in order to validate the remote sensing LAI. The development of wireless sensor networks and unmanned aerial vehicles provides convenient and effective methods to obtain a large amount of vertical structural information useful for validation purposes. Forest vertical LAI and CI have been applied in land surface and radiative transfer models and forest management studies. In future research, new field measurement and remote sensing inversion methods should be explored. Various LiDAR data sources can be used to generate high-quality vertical structural products, and these vertical products should be fully validated to better meet the requirements proposed by the research and management communities.

Does economic optimisation explain LAI and leaf trait distributions across an Amazon soil moisture gradient?

Leaf area index (LAI) underpins terrestrial ecosystem functioning, yet our ability to predict LAI remains limited. Across Amazon forests, mean LAI, LAI seasonal dynamics and leaf traits vary with soil moisture stress. We hypothesise that LAI variation can be predicted via an optimality-based approach, using net canopy C export (NCE, photosynthesis minus the C cost of leaf growth and maintenance) as a fitness proxy. We applied a process-based terrestrial ecosystem model to seven plots across a moisture stress gradient with detailed in situ measurements, to determine nominal plant C budgets. For each plot, we then compared observations and simulations of the nominal (i.e. observed) C budget to simulations of alternative, experimental budgets. Experimental budgets were generated by forcing the model with synthetic LAI timeseries (across a range of mean LAI and LAI seasonality) and different leaf trait combinations (leaf mass per unit area, lifespan, photosynthetic capacity and respiration rate) operating along the leaf economic spectrum. Observed mean LAI and LAI seasonality across the soil moisture stress gradient maximised NCE, and were therefore consistent with optimality-based predictions. Yet, the predictive power of an optimality-based approach was limited due to the asymptotic response of simulated NCE to mean LAI and LAI seasonality. Leaf traits fundamentally shaped the C budget, determining simulated optimal LAI and total NCE. Long-lived leaves with lower maximum photosynthetic capacity maximised simulated NCE under aseasonal high mean LAI, with the reverse found for short-lived leaves and higher maximum photosynthetic capacity. The simulated leaf trait LAI trade-offs were consistent with observed distributions. We suggest that a range of LAI strategies could be equally economically viable at local level, though we note several ecological limitations to this interpretation (e.g. between-plant competition). In addition, we show how leaf trait trade-offs enable divergence in canopy strategies. Our results also allow an assessment of the usefulness of optimality-based approaches in simulating primary tropical forest functioning, evaluated against in situ data.

A Spatial Relationship between Canopy and Understory Leaf Area Index in an Old-Growth Cool-Temperate Deciduous Forest

Quantification of leaf area index (LAI) is essential for understanding forest productivity and the atmosphere–vegetation interface, where the majority of gas and energy exchange occurs. LAI is one of the most difficult plant variables to adequately quantify, owing to large spatial and temporal variability, and few studies have examined the horizontal and vertical distribution of LAI in forest ecosystems. In this study, we demonstrated the LAI distribution in each layer from the understory to canopy using multiple-point measurements (121 points) and examined the relationships among layers in a cool-temperate deciduous forest. LAI at each point, and the spatial distribution of LAI in each layer, varied within the forest. The spatial distribution of LAI in the upper layer was more heterogeneous than that of LAI at the scale of the entire forest. Significant negative correlations were observed between the upper- and lower-layer LAI. Our results indicate that the understory compensates for gaps in LAI in the upper layer; thus, the LAI of the entire forest tends to remain spatially homogeneous even in a mature forest ecosystem.

Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales.

Modeled leaf area index (LAI) in conjunction with satellite-derived LAI data streams may be used to support various regional and local scale air quality models for retrospective and future meteorological assessments. The Environmental Policy Integrated Climate (EPIC) model holds promise for providing LAI within a dynamic range for input into climate and air quality models, improving on current LAI distribution assumptions typical within atmospheric modeling. To assess the potential use of EPIC LAI, we first evaluated the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product collections 5 and 6 (i.e., Mc5, Mc6) with in situ LAI estimates upscaled at four 1.0 km resolution research sites distributed over the Albemarle-Pamlico Basin in North Carolina and Virginia, USA. We then compared the EPIC modeled 12.0 km resolution LAI to aggregated MODIS LAI (Mc5, Mc6) over a 3 × 3 grid (or 36 km × 36 km) centered over the same four research sites. Upscaled in situ LAI comparison with MODIS LAI showed improvement with the newer collection where the Mc5 overestimate of +2.22 LAI was reduced to +0.97 LAI with the Mc6. On three of the four sites, the EPIC/MODIS LAI comparison at 12.0 km resolution grid showed similar weighted mean LAI differences (LAI 1.29-1.34), with both Mc5 and Mc6 exceeding EPIC LAI across most dates. For all four research sites, both MODIS collections showed a positive bias when compared to EPIC LAI, with Mc6 (LAI = 0.40) aligning closer to EPIC than the Mc5 (LAI = 0.61) counterpart. Despite modest differences between both MODIS collections and EPIC LAI, the overestimation trend suggests the potential for EPIC to be used for future meteorological alternative management applications on a regional or national scale.

Estimating Grassland Parameters from Sentinel-2: A Model Comparison Study

Grassland plays an important role in German agriculture. The interplay of ecological processes in grasslands secures important ecosystem functions and, thus, ultimately contributes to essential ecosystem services. To sustain, e.g., the provision of fodder or the filter function of soils, agricultural management needs to adapt to site-specific grassland characteristics. Spatially explicit information derived from remote sensing data has been proven instrumental for achieving this. In this study, we analyze the potential of Sentinel-2 data for deriving grassland-relevant parameters. We compare two well-established methods to calculate the aboveground biomass and leaf area index (LAI), first using a random forest regression and second using the soil–leaf-canopy (SLC) radiative transfer model. Field data were recorded on a grassland area in Brandenburg in August 2019, and were used to train the empirical model and to validate both models. Results confirm that both methods are suitable for mapping the spatial distribution of LAI and for quantifying aboveground biomass. Uncertainties generally increased with higher biomass and LAI values in the empirical model and varied on average by a relative RMSE of 11% for modeling of dry biomass and a relative RMSE of 23% for LAI. Similar estimates were achieved using SLC with a relative RMSE of 30% for LAI retrieval, and a relative RMSE of 47% for the estimation of dry biomass. Resulting maps from both approaches showed comprehensible spatial patterns of LAI and dry biomass distributions. Despite variations in the value ranges of both maps, the average estimates and spatial patterns of LAI and dry biomass were very similar. Based on the results of the two compared modeling approaches and the comparison to the validation data, we conclude that the relationship between Sentinel-2 spectra and grassland-relevant variables can be quantified to map their spatial distributions from space. Future research needs to investigate how similar approaches perform across different grassland types, seasons and grassland management regimes.

Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data.

Above-ground biomass (AGB) and the leaf area index (LAI) are important indicators for the assessment of crop growth, and are therefore important for agricultural management. Although improvements have been made in the monitoring of crop growth parameters using ground- and satellite-based sensors, the application of these technologies is limited by imaging difficulties, complex data processing, and low spatial resolution. Therefore, this study evaluated the use of hyperspectral indices, red-edge parameters, and their combination to estimate and map the distributions of AGB and LAI for various growth stages of winter wheat. A hyperspectral sensor mounted on an unmanned aerial vehicle was used to obtain vegetation indices and red-edge parameters, and stepwise regression (SWR) and partial least squares regression (PLSR) methods were used to accurately estimate the AGB and LAI based on these vegetation indices, red-edge parameters, and their combination. The results show that: (i) most of the studied vegetation indices and red-edge parameters are significantly highly correlated with AGB and LAI; (ii) overall, the correlations between vegetation indices and AGB and LAI, respectively, are stronger than those between red-edge parameters and AGB and LAI, respectively; (iii) Compared with the estimations using only vegetation indices or red-edge parameters, the estimation of AGB and LAI using a combination of vegetation indices and red-edge parameters is more accurate; and (iv) The estimations of AGB and LAI obtained using the PLSR method are superior to those obtained using the SWR method. Therefore, combining vegetation indices with red-edge parameters and using the PLSR method can improve the estimation of AGB and LAI.

Inversion of leaf area index of winter wheat based on GF-1/2 image

Leaf area index (LAI) is an important parameter for monitoring crop growth, and an important input parameter for crop yield prediction model, hydrological and climatic models. LAI can be used in field crop growth monitoring and verification of remote sensing products. Therefore, accurate, rapid and large-scale estimation of LAI is not only conducive to better monitoring crops, but also conducive to its application in modeling, crop management and precision agriculture. Remote sensing technique has become a promising method to detect and monitor the crop LAI due to its many advantages. In this paper, Banjiequan Village, Wumachang Township, Qitai County, Xinjiang, China was selected as the study area. In order to rapidly and extensively retrieve LAI of winter wheat using domestic remote sensing images, 17 common vegetation indices were extracted from the GF-1/2 images, which was synchronized with field sampling, and observation at intervals of 20 m in the east-west direction and 30 m in the north-south direction in a 130 m × 420 m block. A total of 78 sampling points were taken from a small area of 4 rows × 50 cm. Sampling width was measured by tape ruler and coordinates of sampling points were given by GPS. Based on the vegetation indices extracted from GF-1/2 image and LAI data measured at erecting stage, jointing stage and flowering stage, we established univariate (linear, exponential, power, quadratic polynomials) and multivariate (partial least squares regression, PLSR) empirical models for inversion of winter wheat LAI, and validated them. The correlation coefficients of LAI with MSR (modified simple ratio), GNDVI (green normalized difference vegetation index), EVI (enhanced vegetation index) extracted from the erecting, jointing and flowering stages of GF-1 were the maximum, which were 0.708, 0.671, and 0.743, respectively, indicating that the correlation between these vegetation indices and LAI of winter wheat was significant. The univariate model R 2 based on MSRGF-1, NDVIGF-2 (normalized difference vegetation index), GNDVIGF-1 at jointing stage and EVIGF-1 at flowering stage were all greater than 0.7. Compared with different image data at the same growth stage, the quadratic polynomial model based on NDVIGF-2 and PLSR model based on NDVIGF-2, MSRGF-2, and SAVIGF-2 (soil-adjusted vegetation index) were more precise than those based on GF-1, with R 2 of 0.768 and 0.809 respectively. Compared with the models with the same data (GF-1) at different growth stages, the quadratic polynomial model based on GNDVIGF-1 at jointing stage and the PLSR model based on EVIGF-1, GSRGF-1 (green simple ratio) and NDVIGF-1 at flowering stage had the maximum value of R 2, which was 0.783. The RMSE of the PLSR model was smaller than that of the quadratic polynomial model, indicating the stability of the multivariate model was better than univariate model. Analyzing the LAI distribution maps inverted from different growth stages, it was found that the LAI inversion values basically coincided with the measured LAI values. The above results show that the domestic high-resolution remote sensing image has certain application value in crop physiological parameter inversion, and provides some references for related researches in the future.

Impacts of meteorological factors and land use pattern on hydrological elements in a semi-arid basin.

The allocation of water resources to meet both human wellbeing and environmental requirements is a critical challenge in a semi-arid landscape. Hydrological models are widely used to understand the influence mechanism of hydrological elements, which is helpful for optimizing water resources management. However, the spatial heterogeneity of hydrological dynamics has been largely omitted in prior studies, partly because it's difficult to correctly simulate the spatial distributions of hydrological elements due to roughly representing the surface biophysical parameters in the hydrological model. In this study, the Distributed Hydrological Soil Vegetation Model (DHSVM) was incorporated with the high-resolution remotely sensed leaf area index (LAI) data in a semiarid basin, located in the upstream of the Xar Moron River Basin, to explore the impacts of meteorological factors (i.e., air temperature, wind speed, relative humidity, shortwave radiation, and precipitation) and LAI on hydrological processes of forests, grasslands, and farmlands. Our results show that the spatial distribution of LAI slightly improves the accuracy of streamflow simulations and significantly promotes the model performance of spatial hydrological element simulations. For the area in study, precipitation, LAI, and relative humidity are the three major influential factors in the forest hydrological dynamics. The hydrological elements of grasslands and farmlands are mainly affected by shortwave radiation, relative humidity, air temperature, and LAI. Compared with grasslands and farmlands, LAI has greater negative influence on forest water availability. To mitigate the negative effects of drying and warming climate and promote ecosystems sustainability, the forest area should be converted into grassland while the native grassland should be maintained for soil water conservation.

Estimating leaf area index and light extinction coefficient using Random Forest regression algorithm in a tropical moist deciduous forest, India

Leaf area index (LAI) and light extinction coefficient (k) are the key structural parameters controlling many canopy functions like radiation and water interception, radiation extinction, water and gas exchange. The present study aims at developing predictive models for generating spatial distribution of LAI and k by integrating remote sensing imagery and field data. The study was carried out in a tropical moist deciduous forest of Uttarakhand, India. Various spectral variables were derived from Landsat 8 Operational Land Imager (OLI) data of 8 May 2017 to predict LAI and k. In-situ measurements of LAI, incident Photosynthetically Active Radiation (PAR) above canopy (Io) and below canopy (I) were taken using CI-110 Plant Canopy Imager. Canopy gap fraction and k (using Beer-Lambert's equation) were calculated. Random Forest (RF) algorithm was used to predict the spatial distribution of LAI and k using the best predictor variables. The best predictor variables for LAI included band 6 (Short wave infra-red (SWIR) -1) and band 7 (SWIR-2), tasseled cap wetness, Moisture Stress Index (MSI), and Normalized Difference Moisture Index (NDMI). For prediction of k, the best predictor variables were band 6 (SWIR-1) and band 7 (SWIR-2), NDMI, tasseled cap wetness, MSI and Normalized Difference Vegetation Index (NDVI). These variables were selected to generate RF-based models to predict LAI and k. On validation, the models were able to predict LAI with R2 = 0.79 and % RMSE = 14.25% and k with R2 = 0.77 and % RMSE = 11.98%. The predicted LAI and k followed an inverse relation in accordance with the Beer Lambert's Law. The results showed that RF can be effectively applied to predict the spatial distribution of LAI and k.