Canopy reflectance models characterize the reflection of solar radiation from vegetation surfaces and are fundamental to vegetation parameter retrieval. Developing an efficient, accurate and highly generalizable canopy reflectance model is critical in vegetation remote sensing studies. This study develops an empirical canopy reflectance model, called the General Spectral Vector-Canopy (GSV-C) model, to simulate canopy hyperspectral reflectance (400-2400nm) of various vegetation types. Global hyperspectral satellite reflectance spectra covering different vegetation types and months were first collected. A novel matrix factorization method-nonnegative matrix factorization (NMF)-was then applied to extract spectral vectors (SVs) from the global spectra to construct the model. Finally, the model was systematically evaluated using independent reference data from satellite, field measurements, and those simulated by PROSAIL and 5-SCALE models.Results show that the NMF method successfully extracted physically interpretable and globally representative SVs from the training data. Using reflectance at only a few wavelengths as input, GSV-C achieves excellent performance (R² = 0.9882 and RMSE = 0.0151) compared to satellite reference data. The model exhibits strong performance for all vegetation types (R² = 0.9285-0.9911 and RMSE = 0.0085-0.0183). It also performs very well for some non-vegetation types, such as barren areas (R² = 0.9843 and RMSE = 0.0162) and permanent snow and ice (R² = 0.9871 and RMSE = 0.0234). GSV-C shows very good performance when evaluated with field reference data (R² = 0.8975 and RMSE = 0.0517) and simulations from PROSAIL and 5-SCALE (R² = 0.9696 and 0.8593 and RMSE = 0.023 and 0.0645, respectively). Overall, GSV-C provides an efficient and accurate method to simulate canopy hyperspectral reflectance for different vegetation types. The model can be used for hyperspectral reconstruction and vegetation parameter retrieval and can be effectively integrated into land surface models in future studies.

Canopy radiative transfer models (RTMs) are essential tools for characterizing the complex interactions between solar radiation and vegetation canopies. Vegetation canopies exhibit pronounced vertical heterogeneity in terms of their biophysical and optical properties at different growth stages, significantly influencing canopy reflectance. Existing multilayer canopy RTMs predominantly use the 4-stream theory and the adding method to calculate multiple scattering. In these models, the eigenvector decomposition method is used to solve the differential equations and derive the layer scattering matrices, which are then used to calculate multiple scattering. However, for a vertically heterogeneous canopy, deriving analytical solutions to the layer scattering matrices is difficult since each canopy layer exhibits distinct scattering characteristics. The aim of this study is to develop a new multilayer canopy RTM, CANOP, based on the spectral invariant theory and the adding method. The new model employed spectral invariants, instead of intractable layer scattering matrices, to link the scattering properties at the leaf and canopy levels and derive the adding operators for multilayer canopy. The spectral invariants enable a realistic and anisotropic representation of the top and bottom reflectances, as well as the upward and downward transmittances of multilayer canopy. The CANOP model was compared with the multilayer 4-stream and discrete anisotropic radiative transfer (DART) models, and the field-measured paddy rice vertical profile data. The CANOP model performs better than the 4-stream model in simulating multilayer canopy reflectance. It also shows good agreement with the measured layered paddy rice data, achieving high R2 (0.99), low RMSE (0.038) and bias (0.019) values. CANOP provides an accurate and efficient approach for multilayer canopy spectral modeling and can be used for multilayer canopy reflectance modeling and inversion of vegetation biophysical and biochemical parameters.

Canopy clumping index (CI) and nadir CI (NCI) describe foliage spatial distribution and are important for canopy radiative transfer and land-surface process simulations. Classical methods, such as the logarithmic gap fraction averaging (LX), the gap size distribution (CC), and the combined gap size and logarithmic averaging (CLX) have been explored to estimate CI in field studies. However, estimating forest NCI from airborne laser scanning (ALS) using classical methods remains challenging, because these methods are not directly applicable to ALS data and the estimation accuracy is greatly hampered by pulse density and footprint size. Here, we adapted the classical methods to estimate NCI from ALS data over 45 global forest sites by accounting for pulse density, footprint size, and other constraints. Results showed that CC yielded the highest mean NCI (0.82 ± 0.20), followed by LX (0.65 ± 0.19) and CLX (0.54 ± 0.18). Validation against field measurements showed that plant area index (PAI) derived by LX and CLX agreed well with field PAI (|bias| ≤ 0.31) obtained from digital hemispherical photography (DHP) and terrestrial laser scanning (TLS), whereas the CC-derived PAI showed systematic underestimation (bias = −1.35). CLX performed best overall, achieving R² = 0.76, RMSE = 1.31, bias = −0.24 for PAI, and R² = 0.22, RMSE = 0.14, bias = −0.07 for NCI. The LX NCI decreased with increasing FVC, CHM, PAIe, and PAI, whereas the CC NCI showed the opposite trend. CLX NCI was overall closer to LX NCI and decreased with increasing structural parameters. The proposed method can be expanded to estimate NCI at a much larger scale. The retrieved NCI values can be used in canopy reflectance and land surface models and directional CI estimation.

Canopy cover (CC) is the proportion of ground area covered by vertical projection of canopy elements and is a key variable in ecological and hydrological models. A CC product has been derived from Global Ecosystem Dynamics Investigation (GEDI). However, recent validation studies show that this product contains biases, mainly because of uncertainties in ground return determination. This study developed a novel model to predict ground elevation based on a Transformer Encoder using 49 features derived from the raw GEDI waveform. Reference elevation was obtained from airborne laser scanning (ALS) point clouds and used to train the model. Subsequently, CC was estimated by fitting ground return based on the predicted elevations. A leave-one-out method was used to cross-validate the estimated elevation and CC across 23 National Ecological Observatory Network (NEON) based on reference ALS data. The enhanced elevation and CC product were further validated at three independent sites in Brazil and Australia. Results show that ground elevation predicted from the model improved over the original GEDI product, with RMSE reduced from 4.86 m to 3.69 m. Cross-validation and independent testing show that RMSE values were reduced by 14.0% and 15.4%, respectively, compared to the original GEDI products. The estimated CC (r² = 0.65, RMSE = 0.20) exhibits higher accuracy than the GEDI product (r² = 0.59, RMSE = 0.23) compared to the reference ALS data. Both elevation and CC were significantly improved in broadleaf forests (elevation: ΔRMSE = −2.07 m; CC: Δr² = 0.21, ΔRMSE = −0.07) and mixed forests (elevation: ΔRMSE = −1.70 m; CC: Δr² = 0.27, ΔRMSE = −0.07), especially for areas with high vegetation cover (CC > 0.8). In general, the proposed transformer encoder model can effectively improve the GEDI ground elevation and CC estimation and can be used for future product generation.

Forest vertical structure is a fundamental characteristic of plant communities. Quantifying vertical stratification is essential for understanding forest structural complexity, assessing forest stability, and supporting forest management and conservation. The effective plant area index (PAIe), which includes all aboveground components, is a comprehensive indicator representing the complete vertical structure of vegetation. Although multiple remote-sensing techniques have been developed to estimate vertical structural parameters, their practical application has been hampered by limitations in data quality and spatial coverage. Even when using datasets with extensive spatial coverage, mapping fine-scale vertical structure still requires concessions in vertical stratification accuracy and wall-to-wall spatial continuity. Consequently, a fully comprehensive and spatially explicit characterization of forest vertical structure remains unavailable. To address these gaps, we compiled over 130,000 unoccupied aerial vehicle laser scanning (ULS) plots (30 & times; 30 m) from 608 flights across China and conducted a comprehensive sensitivity analysis of voxel size and LiDAR penetration indices, based on using stratified simulation data and field measurements, to identify the optimal retrieval configuration for ULS-derived stratified PAIe profiles. We then implemented a multi-task deep learning framework (PAIe-MoE) using a mixtureof-experts mechanism to generate wall-to-wall stratified PAIe maps of China at 30 m spatial resolution and 5 m vertical resolution. The results showed that the vertical distribution of PAIe across China concentrated in the middle layers and reductions toward both the upper and lower layers; the 5-10 m layer represented the center of mass of the overall vertical structure. The variations of stratified PAIe along latitudinal gradients reflect the influence of vegetation composition and climatic differences on PAIe. Tropical and subtropical regions displayed continuous, multilayered vertical structures, whereas temperate and cold-temperate forests tended to have compressed structures dominated by the mid-canopy (5-15 m). On an independent test set of 6575 ULS plots, total PAIe prediction achieved R2 = 0.69 and RMSE = 1.48. This study presents the first wall-to-wall forest stratified PAIe maps of China, offering new insights into vertical structural patterns and supplying key data for ecosystem assessment, biodiversity monitoring, and sustainable management in support of carbon-neutrality goals.

Remote sensing is a technique to acquire information from a distance. Remote sensing effects refer to any factors that need to be considered in remote sensing processes, while remote sensing invariants represent features that remain stable throughout these processes. Both remote sensing effects and invariants are fundamental to the study of remote sensing systems, methods, algorithms, products, and applications. Many studies have explored different effects and invariants independently, yet these studies are scattered across the literature and a comprehensive synthesis is lacking within the community. This paper intends to synthesize various remote sensing effects and invariants under a unified framework. The characterization, underlying principles, and potential applications of a selected group of remote sensing effects were first examined. Subsequently, a suite of nine key effects, atmospheric effects, background effects, clumping effects, directional effects, heterogeneity effects, saturation effects, scaling effects, temporal effects, and topographic effects, were addressed. Furthermore, a list of remote sensing invariants, including spectral, spatial, temporal, directional, and thematical invariants, were analyzed. Potential directions for future studies were further discussed. This synthesis represents a concerted effort to advance the theoretical understanding of fundamental principles in remote sensing science.

Knowledge of the vertical plant area index (PAI) profile is critical for understanding the forest structural and functional characteristics. Vertical PAI profile has been retrieved by the Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR. However, large-scale validation of the GEDI PAI profile products is limited, and their performance has yet to be clearly established. This study aims to systematically assess the performance of GEDI PAI profile product and investigate the impact factors on PAI profile estimates. The digital hemispherical photography (DHP) of vertical measurement and airborne laser scanning (ALS) data were collected to derive the reference PAI profiles. The results indicate that adjusting footprint geolocation before GEDI validation is essential for enhancing product assessment. The GEDI PAI profile moderately agrees with the DHP and ALS (R2 = 0.84 and 0.58, respectively) but underestimates the reference (bias =-0.14 and-0.28, respectively). The needleleaf forest exhibits the highest agreement with ALS (R2 = 0.60 and bias =-0.16), while shrubland shows the lowest agreement (R2 = 0.38 and bias = 0.21). The agreement between GEDI and ALS increases with the canopy height but decreases with the canopy cover. Low vegetation height and steep slopes affect the GEDI PAI accuracy owing to the difficulty in decomposing the mixed ground and canopy returns. Additionally, the limited penetration of GEDI in dense vegetation with high canopy cover contributes to the underestimation. The performance of GEDI PAI profile can be improved by applying a specific canopy and ground reflectance ratio (rho v/rho g) value derived from the linear regression of return energy. The discrepancies between GEDI and ALS PAI profiles were partially attributed to the sub-optimal waveform processing algorithm settings and differences in LiDAR specifications. Further improvement to the GEDI PAI product may be achieved by implementing a customized waveform processing algorithm and using realistic rho v/rho g values.

Leaf chlorophyll content (LCC) is a crucial biochemical parameter for monitoring the plant's nutritional status and photosynthetic capacity. However, retrieving LCC from canopy reflectance is challenging due to the coupling influence of LCC and canopy structure, particularly leaf area index (LAI). The isolation of leaf-scale information from canopy signals is therefore essential to improve the LCC estimation. This study proposed an approach for deriving the leaf-scale chlorophyll index (CIleaf) from the canopy bidirectional reflectance factor (BRF) based on the spectral invariant theory (p-theory). Six widely used canopy-scale chlorophyll indices (CIcanopy) were selected to derive the corresponding CIleaf. The CIleaf is expressed as the product of its original CIcanopy and a scale conversion factor (SCF) (CIleaf = CIcanopy x SCF). The SCF is determined by two spectral invariants of p-theory (recollision probability p and directional area scattering factor DASF), as well as canopy BRFs at specific wavelengths, and it corrects for the contribution of canopy multiple scattering to CIcanopy. The analysis through radiative transfer model simulations showed that CIleaf exhibited more unified relationships with LCC across LAI conditions than the original CIcanopy and substantially eliminated the influence of LAI on the CI-based model. Validation results demonstrated that CIleaf improved the accuracy of LCC estimation compared to CIcanopy. The leaf-scale MERIS terrestrial chlorophyll index (MTCIleaf) exhibited the most prominent improvements, reducing the root-mean-square error (RMSE) by 6.68 mu g/cm2 for ground spectra and 2.33-4.21 mu g/cm2 for Sentinel-2 images with multi-ecosystem datasets. Additionally, the influence of vegetation types on the CI-based model was mitigated by CIleaf. MTCIleaf reduced the RMSE values by 3.8 %-34.0 % for different plant functional types, giving more consistent accuracies across species than MTCIcanopy. Our results show that the proposed CIleaf combines the robustness of the physically-based method with the simplicity of the CI-based method, thus providing a practical approach for large-scale high-resolution LCC mapping. Moreover, the method holds promise for designing leaf-scale vegetation indices sensitive to various leaf biochemical parameters beyond LCC, extending its utility to broader leaf-scale remote sensing retrieval (e.g., leaf carotenoid content and leaf dry mass).

The fraction of absorbed photosynthetically active radiation (FAPAR) stands as a pivotal parameter within the Earth system, quantifying the energy exchange between vegetation and solar radiation. Accordingly, there is an urgent need for comprehensive validation studies to accurately quantify uncertainties and improve the reliability of FAPAR-based applications. This study validated five global FAPAR products, MOD15A2H, MYD15A2H, VNP15A2H, GEOV2, and GEOV3, over four boreal forest sites in North America. Qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) of each product were analyzed. Time series high-resolution reference FAPAR maps were developed using the Harmonized Landsat and Sentinel-2 dataset. The reference FAPAR maps revealed a strong agreement with the in situ FAPAR from AmeriFlux (correlation coefficient (R) = 0.91; root mean square error (RMSE) = 0.06). The results revealed that global FAPAR products show similar uncertainties (RMSE: 0.16 ± 0.04) and moderate agreement with the reference FAPAR (R = 0.75 ± 0.10). On average, 34.47 ± 6.91% of the FAPAR data met the goal requirements of the Global Climate Observing System (GCOS), while 54.41 ± 6.89% met the threshold requirements of the GCOS. Deciduous forests perform better than evergreen forests, and the products tend to underestimate the reference data, especially for the beginning and end of growing seasons in evergreen forests. There are no obvious quality differences at different QQFs, and the relative QQI can be used to filter high-quality values. To enhance the regional applicability of global FAPAR products, further algorithm improvements and expanded validation efforts are essential.

The leaf area index (LAI) is a critical parameter for monitoring vegetation health and studying climate change. The spatial resolutions of most LAI products range from 500 to 1000 m. Only a few LAI products exhibit spatial resolutions ranging from 16 to 30 m, but notable missing data occur because of the revisit cycle of satellites and the effect of weather conditions. These methods cannot satisfy the requirements of LAI application communities. To address this issue, a workflow was proposed for high-resolution seamless mapping of the LAI on the basis of multisource data and the Transformer deep learning model. Jiangsu Province in China was chosen as the study area. In this area, numerous cloudy and rainy days occur annually. Harmonized Landsat and Sentinel-2 (HLS) and moderate resolution imaging spectroradiometer (MODIS) reflectance images were obtained. The MODIS and HLS images were first composited and spatially aligned. Then, on the basis of the MODIS images and the spatiotemporal fusion incorporating spectral autocorrection (FIRST) method, missing HLS image data were reconstructed, thus producing a reflectance product with a spatial resolution of 30 m and a temporal resolution of 12 days. On the basis of the reflectance product, a Transformer model was designed for LAI prediction and compared with models designed through backpropagation neural network (BPNN), convolutional neural network (CNN), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM) methods. These models were compared with and without transfer learning on the basis of an independent dataset. The best model was selected and employed to produce a LAI product for the study area, which was subsequently compared with an existing MODIS product from spatial and temporal perspectives. The results showed that the procedure for HLS reconstruction is effective, with errors varying between 4.00% and 15.93% for different bands. Among the LAI prediction models, the Transformer model consistently performed the best across all scenarios. Notably, the Transformer model trained via transfer learning yielded the best results, with a test R-2 value of 0.62, a root mean square error (RMSE) of 0.79 and a mean relative error (MRE) of 14.80%. The R-2 values of the other models ranged from 0.31 to 0.59, the RMSE values ranged from 0.82 to 1.06, and the MRE values ranged from 15.41% to 22.28%. In addition, the HLS LAI established via the above best model provided greater spatiotemporal accuracy than did the MODIS LAI product. This study provides reference data for establishing seamless LAI products with high spatial and temporal resolutions, contributing to applications such as vegetation health monitoring and global change research.

Leaf inclination angle (LIA), the angle between the leaf surface normal and zenith directions, is a vital trait in radiative transfer, rainfall interception, evapotranspiration, photosynthesis, and hydrological processes. Due to the difficulty of obtaining large-scale field measurement data, LIA is typically assumed to follow the spherical leaf distribution or simply considered to be constant for different plant types. However, the appropriateness of these simplifications and the global LIA distribution are still unknown. This study compiled global LIA measurements and generated the first global 500 m mean LIA (MLA) product by gap-filling the LIA measurement data using a random forest regressor. Different generation strategies were employed for noncrops and crops. The MLA product was evaluated by validating the nadir leaf projection function (G(0)) derived from the MLA product with high-resolution reference data. The global MLA is 41.47°±9.55°, and the value increases with latitude. The MLAs for different vegetation types follow the order of cereal crops (54.65°) > broadleaf crops (52.35°) > deciduous needleleaf forest (50.05°) > shrubland (49.23°) > evergreen needleleaf forest (47.13°) ≈ grassland (47.12°) > deciduous broadleaf forest (41.23°) > evergreen broadleaf forest (34.40°). Cross-validation shows that the predicted MLA presents a medium consistency (r=0.75, RMSE = 7.15°) with the validation samples for noncrops, whereas crops show relatively lower correspondence (r=0.48 and 0.60 for broadleaf crops and cereal crops, respectively) because of the limited LIA measurements and strong seasonality. The global mean G(0) is 0.68±0.11. The global G(0) distribution is out of phase with that of the MLA and agrees moderately with the reference data (r=0.62, RMSE = 0.15). This study shows that the common spherical and constant LIA assumptions may underestimate the interception of most vegetation types. The MLA and G(0) products derived in this study could enhance our knowledge of global LIA and should greatly facilitate remote sensing retrieval and land surface modeling studies. The global MLA and G(0) products can be accessed at https://doi.org/10.5281/zenodo.12739662 (Li and Fang, 2025).

Objective: We intend to provide a meta-review of various remote sensing effects and invariants.Methods: The characterization, underlying principles, and potential applications of a selected group of remote sensing effects were examined.Results: A suite of ten key effects was addressed. A list of remote sensing invariants were analyzed.Limitations: Potential directions for future studies were discussed.Conclusions: This synthesis represents a concerted effort to advance the theoretical understanding of fundamental principles in remote sensing science.

• Leaf area index (LAI) and green area index (GAI) are fundamental plant traits. However, there is a lack of ground observation network for LAI/GAI due to technical limitations. Here we present a new method to achieve continuous LAI/GAI monitoring using ordinary cameras. • By tilting ordinary cameras by 30°, images can cover a large view zenith angle range to measure multi-angular gap fractions and thus to quantify LAI using radiative transfer theory. In addition, using cameras can separate green tissues from non-green tissues. We conducted intensive experiments to evaluate the performance of 30°-tilted cameras and built a countywide LAI/GAI ground observation network. • LAI/GAI derived from 30°-tilted cameras are consistent with LAI-2200 and destructive samplings. The countywide LAI/GAI ground observation network can capture distinct seasonality of corn, soybean, miscanthus, switchgrass, restored prairie and deciduous forest. • 30°-tilted cameras provide an accurate, robust, automatic, standardized and scalable method to acquire spatially-distributed and temporally-continuous LAI/GAI at low cost. It is promising for building a LAI ground observation network at regional and global scales.

Leaf area index (LAI) is one of key variables for depicting vegetation structures in land ecosystems. Land surface models necessitate uniform LAI inputs at varying spatial scales to ensure accurate outputs at multiscale levels, however, operational satellite LAI products are acquired only at low spatial resolutions, inhibiting their application at finer spatial scales. Spatial downscaling methods are beneficial for the spatial enhancement of LAI products, and the emergence of deep learning methods has provided promising options for land surface parameter downscaling. However, the potential of deep learning has not been well explored in LAI downscaling. To address this research gap, this study designed an original hierarchical downscaling approach facilitated by generative adversarial network (GAN), transfer learning (TL), and data augmentation techniques to retrieve LAI at fine spatial resolutions, leveraging multiscale satellite images, and cascading from 500-m to 250-m and then to 30-m scales. First, an improved super-resolution GAN (ISRGAN) model was pre-trained using the GLASS LAI and MOD09Q1 products to bridge the general non-linear relationships of LAI between the 500-m and 250-m resolutions. Subsequently, limited reference LAI images were applied to fine-tune this pre-trained ISRGAN model to address the domain shift in the 250-m resolution LAI estimations. Then, the fine-tuned LAI values and the 30-m resolution LAI reference images were utilized as the ISRGAN inputs to produce fine-resolution LAI maps. Finally, the downscaled LAI values derived from the proposed approach were separately validated against reference LAI maps and field measurements across the 250-m and 30-m resolutions. Results show that the fine-tuned transfer learning technique outperforms the pre-trained ISRGAN model and GLASS LAI, with a lower RMSE (0.78) and higher R2 2 (0.83) at the 250-m resolution. Moreover, the proposed hierarchical downscaling framework achieves better performances for 30-m resolution LAI estimations, regardless of the validation accuracy (R2 2 = 0.76; RMSE=0.95) =0.95) and spatiotemporal distributions, than the ISRGAN model which was directly trained by the 500-m and 30-m resolution images. This study highlights that a hierarchical downscaling is valuable for fine-resolution LAI estimations, which leverages multiscale and multisource satellite observations via deep learning.

Canopy cover (CC) quantifies the proportion of canopy materials projected vertically onto the ground surface. CC is a crucial canopy structural variable and is commonly used in many ecological and climatic models. The vertical CC profile product is currently available from the Global Ecosystem Dynamics Investigation (GEDI). However, detailed information about the accuracy and uncertainty of the GEDI vertical CC profile product remains limited. The objective of this study is to validate the GEDI CC product over selected forest sites using reference values derived from digital hemispherical photography (DHP), airborne laser scanning (ALS) point clouds, and simulated waveforms. The accuracy of CC was quantified and analyzed regarding GEDI observation conditions, waveform processing, and estimation methods. The results show that the total GEDI CC correlates well with those estimated from DHP, ALS, and simulated waveform data (r2 = 0.65, 0.71, and 0.71, respectively) but is systematically underestimated (bias = −0.05, −0.11, and −0.07, respectively) based on reference data. Compared with the ALS-estimated CC, needleleaf forest shows the highest correlation for vertical CC (r2 ≥ 0.65) and shrubland shows the lowest bias for total CC (bias = −0.13). The mean absolute error (MAE) of the GEDI CC decreases from 0.15 to 0.09 from the ground to 35 m. The total GEDI CCs derived from the waveform interpretation algorithms A2 and A6 display the highest r2 (≥ 0.6) and smallest RMSE (≤ 0.23) compared to those of the other algorithms. The GEDI CC was improved at moderate CC values using a canopy-to-background backscattering coefficient ratio () determined with the regression method. The CC accuracy increases with beam sensitivity and decreases with increasing canopy cover. The partial difference between GEDI CC and ALS CC is attributed to definition differences. Further improvement of the CC algorithm can be made by using vegetation-specific waveform processing algorithms and realistic values.

The leaf area index (LAI) is a critical variable for forest ecosystem processes. Passive optical and active LiDAR remote sensing have been used to retrieve LAI. LiDAR data have good penetration to provide vertical structure distribution and deliver the ability to estimate forest LAI, such as the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). Segment size and beam type are important for ICESat-2 LAI estimation, as they affect the amount of signal photons returned. However, the current ICESat-2 LAI estimation only covered a limited number of sites, and the performance of LAI estimation with different segment sizes has not been clearly compared. Moreover, ICESat-2 LAIs derived from strong and weak beams lack a comparative analysis. This study derived and evaluated LAI from ICESat-2 data over the National Ecological Observatory Network (NEON) sites in North America. The LAI estimated from ICESat-2 for different segment sizes (20, 100, and 200 m) and beam types (strong beam and weak beam) were compared with those from the airborne laser scanning (ALS) and the Copernicus Global Land Service (CGLS). The results show that the LAI derived from strong beams performs better than that of weak beams because more photon signals are received. The LAI estimated from the strong beam at the 200 m segment size shows the highest consistency with those from the ALS data (R = 0.67). Weak beams also present the potential to estimate LAI and have moderate agreement with ALS (R = 0.52). The ICESat-2 LAI shows moderate consistency with ALS for most forest types, except for the evergreen forest. The ICESat-2 LAI shows satisfactory agreement with the CGLS 300 m LAI product (R = 0.67, RMSE = 1.94) and presents a higher upper boundary. Overall, the ICESat-2 can characterize canopy structural parameters and provides the ability to estimate LAI, which may promote the LAI product generated from the photon-counting LiDAR.

Physical model simulations have been widely utilized to simulate the reflectance of vegetation canopies. Such simulations can be used to estimate key biochemical and physical vegetation parameters, such as leaf chlorophyll content (LCC), leaf area index (LAI), and leaf inclination angle (LIA) from remotely sensed data via model inversion. In simulations, field crops are typically regarded as one-dimensional (1D) vegetation canopies with constant leaf properties in the vertical direction and across the growing season. We investigated the seasonal effects of these two simplifications, 1D canopy structure, and vertically constant leaf properties, on canopy reflectance simulations in a rice field using in situ measurements and the 3D discrete anisotropic radiative transfer model (DART). We also developed a new methodology for reconstructing 3D crop canopy architecture, which was validated using measurements of gap fraction and canopy reflectance. Our results revealed that the 1D canopy assumption only holds during the early stage of the growing season, then leaf clumping affects canopy reflectance from the jointing stage onwards. Consideration of the 3D canopy structure and its seasonal variation significantly reduced the deviation between simulated and measured canopy reflectance in the green and near-infrared wavelengths when compared to the typical 1D canopy assumption and produced the closest multi-angular distribution pattern to the measurements. The vertical heterogeneity of leaf spectra affected canopy reflectance weakly during the maturation stage when senescence started from the bottom of the canopy. Consideration of seasonal and vertical variation in LIAs significantly improved the results of 1D canopy reflectance simulations, including the multi-angular distribution patterns. In contrast, the directionally-averaged clumping index (CI) only slightly improved the 1D canopy reflectance simulation. To summarize, these findings can be used to reduce the simulation bias of canopy reflectance and improve the retrieval accuracy of key vegetation parameters in crop canopies at the seasonal scale.

The U.S. National Park Service uses W126 to assess critical levels of ozone (O-3) exposure to sensitive plant species, whereas the scientific community considers the stomatal flux-based metric, phytotoxic O-3 dose (POD), more relevant in foliar risk evaluation. This study found a decreasing trend of 0.15 ppm-hrs/yr (p = 0.049) in W126 at a Yellowstone National Park monitoring site during 1997-2021 and no trend at a Grand Teton National Park site over 2012-2021 but no trends at both sites in POD calculated following the United Nations Economic Commission for Europe Convention on Long-range Transboundary Air Pollution (CLRTAP) (2017). To evaluate the CLRTAP (2017) method, 2019 summer POD was calculated for comparison using the Big-leaf model and was found 20%-100% less than the CLRTAP (2017)-calculated leaf-level one already, let alone compared to the latter scaled up to canopy-level, owing to the much smaller stomatal O-3 flux (SOF) estimates under most conditions. Further analysis revealed exceptions where larger SOF values (>0.02 mmol/m(2)/hr), obtained from using the Big-leaf model with measured sensible heat and water vapor flux included in input, were not captured by the CLRTAP (2017) method and the Big-leaf model with input of regular meteorological variables only. Those large SOF values resulted under cooler, less windy, more humid, and cloudier conditions, when the approximate water vapor concentration difference between the ambient and leaf saturation level was found <1 mol/m(3). A multivariate function was developed, through machine learning, by regressing SOF on air temperature, wind speed, relative humidity, phytosynthetically active radiation, and O-3 concentration. Its testing and evaluation demonstrated improved estimates of the large variability in SOF. This new parameterization showed promising capability of assessing critical levels of O-3 exposure in park management, and it could be further improved with long-term flux measurement data becoming available and evapotranspiration better represented.

Evaluated tower, mast, crane, and UAV methods for forest vertical gap fraction, LAI, and CI measurements in different seasons. UAV is promising for forest vertical structural profiling. The vertical distribution of canopy structural parameters, such as canopy gap fraction, leaf area index (LAI) and clumping index (CI), is important for understanding the forest structural and functional properties. However, vertically distributed canopy structural data are rare, and current methods are either inefficient or costly for obtaining sufficient amounts of such data. This study conducted a series of field campaigns to obtain forest vertical structural measurements at two temperate forest sites in northern China from 2020 to 2023. Four different measurement systems were compared: (1) flux towers with accessible platforms at different heights, (2) a portable and extensible sampling mast with a digital hemispherical photography (DHP) camera attached on top, (3) a tower crane with a DHP camera fixed on the crane hook, and (4) an uncrewed aerial vehicle (UAV) with a DHP camera attached on top. The measured effective plant area index (PAIeff) shows clearly seasonal variations at different heights. The CI remains relatively consistent at different heights, and the leaf-off value is approximately 0.1−0.2 higher than the leaf-on one. The flux tower method can be used for vertical profile measurement at a fixed location, whereas the portable mast is suitable for lower-level (< 15 m) measurement. Crane measurement requires an established facility and is useful for local measurement around the crane. UAV with an attached DHP provides a promising method for monitoring vertical structural parameters. The vertical structural profiles obtained in this study can be used in various modeling and validation studies.