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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 GEDI total 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 as the estimation height increases from ground to 35 m. The GEDI total 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 CC accuracy increases with beam sensitivity and decreases with increasing canopy cover. The GEDI CC was improved at moderate CC values using a canopy-to-ground backscattering coefficient ratio (ρv/ρg) determined with the regression method. The partial difference between GEDI CC and ALS CC is attributed to definitional discrepancies. Further improvement of the CC algorithm can be made by using vegetation-specific waveform processing algorithms and realistic ρv/ρg values.

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 (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 = 0.76; RMSE=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.

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.

Lytic polysaccharide monooxygenases (LPMOs) are copper-dependent enzymes that catalyze the oxidative cleavage of recalcitrant polysaccharides. There are limited reports on LPMOs capable of concurrently catalyzing the oxidative cleavage of both cellulose and chitin. In this study, we identified and cloned a novel LPMO from the newly isolated bacterium Chitinilyticum aquatile CSC-1, designated as CaLPMO10. When using 2, 6-dimethylphenol (2, 6-DMP) as the substrate, CaLPMO10 exhibited optimal activity at 50 °C and pH 8, demonstrating good temperature stability at 30 °C. Even after a 6 h incubation at pH 8 and 30 °C, CaLPMO10 retained approximately 83.03 ± 1.25% residual enzyme activity. Most metal ions were found to enhance the enzyme activity of CaLPMO10, with ascorbic acid identified as the optimal reducing agent. Mass spectrometry analysis indicated that CaLPMO10 displayed oxidative activity towards both chitin and cellulose, identifying it as a C1/C4-oxidized LPMO. CaLPMO10 shows promise as a key enzyme for the efficient utilization of biomass resources in future applications.

Leaf area index (LAI) is defined as one half of the total green leaf area per unit ground surface area. Its vertical profile is critical for understanding the remote sensing radiative transfer processes. LAI profile has been derived from airborne and spaceborne LiDAR data, such as the Global Ecosystem Dynamics Investigation (GEDI) installed on the International Space Station. However, the capability of various algorithms for the LAI profile estimation with airborne LiDAR is not clearly evaluated, and the estimated LAI profiles, including the GEDI LAI products, are not been fully validated. This study conducted a quantitative retrieval and validation of the LAI profiles using terrestrial and airborne laser scanning (TLS and ALS) and spaceborne GEDI data over a deciduous needleleaf forest site in northern China. The vertical LAI profile was estimated in the field using an upward digital hemispherical photography (DHP) attached to a portable measurement system in 2020 and 2021. A suite of new LiDAR indices combining both LiDAR return number and return intensity was explored for the LAI profile estimation. All LAI profiles obtained from the DHP, TLS, ALS, and GEDI during the leaf-on season and leaf-off season were compared. The DHP shows a good agreement with the TLS LAI profiles (R2 = 0.97). The LAI profile derived from the ALS data using the combined light penetration index (LPIRI) agrees well (R2 ≥0.86) with the DHP, TLS, and GEDI estimates. In general, the LPIRI is advantageous for regional LAI profile mapping from ALS. The GEDI cumulative LAI corresponds well with the DHP during the leaf-on season (R2 = 0.90, RMSE = 0.23), but underestimates during the leaf-off season (R2 = 0.70, RMSE = 0.14, bias=−0.13). The underestimation is attributed to the higher canopy and ground reflectance ratio (ρv/ρg) assigned in the algorithm and the height discrepancy between the GEDI and field measurements. For the GEDI LAI profile product, further validation and improvement are necessary for other biome types and landscape conditions, especially during the leaf-off season.

Leaf optical spectra determine vegetation canopy optical spectra. Physically based leaf models are relatively complicated as they require many biophysical and biochemical parameters that are usually difficult to obtain. The aim of this study is to develop a new empirical model, the general spectral vector-leaf (GSV-L) model using globally collected leaf spectra data. A global field-measured leaf spectra database, including 66,478 leaf reflectance and 3575 transmittance spectra in the wavelength range of 400–2400 nm, was first constructed. The singular value decomposition (SVD) method was applied to the field data matrix to derive the spectral vectors for leaf spectral modeling. The SVD method was also compared with the successive approximation (SA) and principal component analysis (PCA) methods using the same training and validation datasets, and the GSV-L model was compared with two other physical models, PROSPECT and LEAF-SIP. The GSV-L model can be used to simulate full leaf reflectance and transmittance spectra using a few wavelengths in different bands with very high R2 (0.99, 0.99), small RMSE (0.014, 0.014) and bias (0, 0.002) values. The model also performs better than the PROSPECT and LEAF-SIP models for leaf spectral simulation. The coefficients of the reflectance spectral vectors can be used to estimate leaf structure parameters, equivalent water thickness, and leaf biochemical contents. As an efficient and accurate approach for leaf hyperspectral modeling, GSV-L can also be integrated with various canopy reflectance and land surface models.

In recent decades, several global clumping index (CI), fractional vegetation cover (FVC), and leaf area index (LAI) products have been generated using a range of optical satellite sensors. It is essential for the application community to understand the accuracy and uncertainty of these products. However, current validation studies have limited spatial coverage and temporal continuity, and the relationships between different products are not clearly understood. This study aims to validate the most recent global CAS-CI, GEOV2 FVC, and MODIS LAI products using the Ground-Based Observations for Validation (GBOV) and DIRECT 2.1 datasets. The relationships between these products, the leaf projection function in the nadir direction (G(0)), and the vertical structural characteristics of the forest were also analyzed. The GEOV2 FVC and MODIS LAI show good performance, whereas CAS-CI shows a slight underestimation (bias = −0.10) and strong seasonal variations. FVC and LAI are strongly correlated at different spatial scales [field, high-resolution (30 m and upscaled 3 km), and moderate-resolution (3 km)], particularly for forests [mean Pearson's correlation coefficients (r) = 0.90]. The CI-FVC/LAI relationship is more consistent across spatial scales for grass and shrubs than for forests. The derived G(0) overestimates the reference value (bias = 0.28) mainly due to inconsistencies between the CI, FVC, and LAI products. The overstory CI is very close to the overall forest CI, indicating that the understory may be neglected when estimating the overall CI. The overstory G(0) is similar to the overall G(0) for most forest types, except for the evergreen needleleaf forest. The overall CI-FVC/LAI relationship is mainly influenced by the overstory, while the FVC-LAI relationship is affected by both the overstory and understory. Overall, the CI-FVC/LAI relationship should be further evaluated and applied cautiously across different scales. The forest vertical structural characteristics revealed in this study are important for field measurement, remote sensing estimation, and modeling studies.

Accurate fractional vegetation coverage (FVC) detection is beneficial for evaluating the dynamics and development potential of desert ecosystem. However, at present the performance of the related published products in the Qinghai-Tibet Plateau (QTP)’s desert ecosystem has not been verified, and the method for extracting FVC for this region from unmanned aerial vehicle (UAV) RGB images has also not been tested. UAV RGB images were processed via a boosted regression tree model (BRT). 57 ground measurements were collected, and were further paired with vegetation indexes (VIs) from satellite sensors to develop an empirical model that was used to evaluate FVC of QTP’s desert ecosystem. The results showed that: (1) BRT effectively enhanced the vegetation information in UAV RGB images and improved the accuracy of FVC ground measurements (the area under the receiver operating characteristic curve (AUC) = 0.95, kappa > 0.95); (2) The relationship between FVC and modified soil-adjusted vegetation index (MSAVI) was most robust in QTP’s desert ecosystem, and a power model (FVC = 13.85 × MSAVI2.07, R2 = 0.89, RMSE

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Monitoring gross primary production (GPP) is necessary for quantifying the terrestrial carbon balance. The near-infrared reflectance of vegetation (NIRv) has been proven to be a good predictor of GPP. Given that radiation powers photosynthesis, we hypothesized that (i) the addition of photosynthetic photon flux density (PPFD) information to NIRv would improve estimates of GPP and that (ii) a further improvement would be obtained by incorporating the estimates of radiation distribution in the canopy provided by the foliar clumping index (CI). Thus, we used GPP data from FLUXNET sites to test these possible improvements by comparing the performance of a model based solely on NIRv with two other models, one combining NIRv and PPFD and the other combining NIRv, PPFD and the CI of each vegetation cover type. We tested the performance of these models for different types of vegetation cover, at various latitudes and over the different seasons. Our results demonstrate that the addition of daily radiation information and the clumping index for each vegetation cover type to the NIRv improves its ability to estimate GPP. The improvement was related to foliage organization, given that the foliar distribution in the canopy (CI) affects radiation distribution and use and that radiation drives productivity. Evergreen needleleaf forests are the vegetation cover type with the greatest improvement in GPP estimation after the addition of CI information, likely as a result of their greater radiation constraints. Vegetation type was more determinant of the sensitivity to PPFD changes than latitude or seasonality. We advocate for the incorporation of PPFD and CI into NIRv algorithms and GPP models to improve GPP estimates.

Canopy clumping index (CI), fractional vegetation cover (FVC), and leaf area index (LAI) are important vegetation structural variables. Characterization of the spatial and temporal variations of these variables is important for understanding the global vegetation properties, canopy radiative transfer processes, and land surface modeling studies. This study explores the global CI, FVC, and LAI and their relationships using currently available global products from 2003−2017: CAS-CI (V1.1), GEOV2 FVC, and MODIS LAI (C6.0). The results show that global FVC and LAI have increased, whereas CI has slightly decreased since 2003. CI shows a negative relationship with both FVC and LAI. The specific vegetation cover (SVC), defined as the ratio of FVC to LAI, shows distinct spatial and temporal patterns for different land cover types and has great potential for vegetation characterization and phenology studies. The base and full CIs, calculated as the CI values during leaf emergence and full ground coverage or maximum LAI, respectively, were proposed to determine the seasonal variations in CI for different vegetation types. A global monthly leaf projection function (G(0)) map was generated for the first time to characterize the leaf angular distribution. Overall, this study enhances our understanding of the global CI, FVC, and LAI and provides new insights on characterizing the biophysical properties of global vegetation. These findings are important for canopy reflectance modeling and parameter retrieval studies.

The leaf inclination angle (LIA), defined as the leaf or needle inclination angle to the horizontal plane, is vital in radiative transfer, precipitation interception, evapotranspiration, photosynthesis, and hydrological processes. This paper reviews the field and remote sensing methods to determine LIA. In the field, LIA is determined using direct and indirect methods. The direct methods include direct contact, photographic, and light detection and ranging (LiDAR) methods, while the indirect methods are composed of the gap fraction, four-component, and polarization measurement methods. The direct methods can obtain LIA accurately at individual leaves, crown, and plot scales, whereas the indirect methods work well for crops at the plot level. The remote sensing methods to estimate LIA are mainly based on the empirical, radiative transfer model, and gap fraction methods. More advanced inversion strategies and validation studies are necessary to improve the robustness of LIA remote sensing estimation. In future studies, automated observation systems can be developed and the LIA measurement can be incorporated into existing ground observation networks to enhance spatial coverage.

As a major crop type in the global agroecosystem, paddy rice fields contribute to global greenhouse gas emissions. Surface albedo plays a vital role in estimating carbon emissions. However, it is difficult to find a broadband albedo estimation over paddy rice fields. The objective of this study was to derive an applicable method to improve albedo estimation over a paddy rice field. Field multiangle reflectance and surface albedo were collected throughout the growing season. A physically based model (AMBRALS) was utilized to reconstruct the directional reflectance into the spectral albedo. Multiple spectral albedos (at the wavelengths of 470, 550, 660, 850, 1243, 1640 and 2151 nm) were calculated, and new narrowband to broadband conversion coefficients were derived between the observed spectral albedo and broadband albedo. The conversion schemes showed high consistency with the field albedo observations in the shortwave (285–3000 nm), infrared (700–3000 nm), and visible (400–700 nm) bands. This method can help improve albedo estimation in partially submerged environments.

A high-quality leaf-area index (LAI) is important for land surface process modeling and vegetation growth monitoring. Although multiple satellite LAI products have been generated, they usually show spatio-temporal discontinuities and are sometimes inconsistent with vegetation growth patterns. A deep-learning model was proposed to retrieve time-series LAIs from multiple satellite data in this paper. The fusion of three global LAI products (i.e., VIIRS, GLASS, and MODIS LAI) was first carried out through a double logistic function (DLF). Then, the DLF LAI, together with MODIS reflectance (MOD09A1) data, served as the training samples of the deep-learning long short-term memory (LSTM) model for the sequential LAI estimations. In addition, the LSTM models trained by a single LAI product were considered as indirect references for the further evaluation of our proposed approach. The validation results showed that our proposed LSTMfusion LAI provided the best performance (R2 = 0.83, RMSE = 0.82) when compared to LSTMGLASS (R2 = 0.79, RMSE = 0.93), LSTMMODIS (R2 = 0.78, RMSE = 1.25), LSTMVIIRS (R2 = 0.70, RMSE = 0.94), GLASS (R2 = 0.68, RMSE = 1.05), MODIS (R2 = 0.26, RMSE = 1.75), VIIRS (R2 = 0.44, RMSE = 1.37) and DLF LAI (R2 = 0.67, RMSE = 0.98). A temporal comparison among LSTMfusion and three LAI products demonstrated that the LSTMfusion model efficiently generated a time-series LAI that was smoother and more continuous than the VIIRS and MODIS LAIs. At the crop peak growth stage, the LSTMfusion LAI values were closer to the reference maps than the GLASS LAI. Furthermore, our proposed method was proved to be effective and robust in maintaining the spatio-temporal continuity of the LAI when noisy reflectance data were used as the LSTM input. These findings highlighted that the DLF method helped to enhance the quality of the original satellite products, and the LSTM model trained by the coupled satellite products can provide reliable and robust estimations of the time-series LAI.

Canopy clumping index (CI) is a key structural parameter related to vegetation phenology and the absorption of radiation, and it is usually retrieved from remote sensing data based on an empirical relationship with the Normalized Difference between Hotspot and Darkspot (NDHD) index. A rapid production software was developed to implement the CI algorithm based on the Google Earth Engine (GEE) to update current CI products and promote the application of CI in different fields. Daily, monthly, and yearly global CI products are continuously generated and updated in real-time by the software. Users can directly download the product or work with CI without paying attention to data generation. For the application case study, a change detection algorithm, LandTrendr, was implemented on the GEE to examine the global CI trend from 2000 to 2020. The results indicate that the area of increase trend (28.7%, ΔCI > 0.02) is greater than that of the decrease trend (17.1%, ΔCI < −0.02). Our work contributes toward the retrieval, application, and validation of CI.

The MODIS LAI/FPAR products have been widely used in various fields since their first public release in 2000. This review intends to summarize the history, development trends, scientific collaborations, disciplines involved, and research hotspots of these products. Its aim is to intrigue researchers and stimulate new research direction. Based on literature data from the Web of Science (WOS) and associated funding information, we conducted a bibliometric visualization review of the MODIS LAI/FPAR products from 1995 to 2020 using bibliometric and social network analysis (SNA) methods. We drew the following conclusions: (1) research based on the MODIS LAI/FPAR shows an upward trend with a multiyear average growth rate of 24.9% in the number of publications. (2) Researchers from China and the USA are the backbone of this research area, among which the Chinese Academy of Sciences (CAS) is the core research institution. (3) Research based on the MODIS LAI/FPAR covers a wide range of disciplines but mainly focus on environmental science and ecology. (4) Ecology, crop production estimation, algorithm improvement, and validation are the hotspots of these studies. (5) Broadening the research field, improving the algorithms, and overcoming existing difficulties in heterogeneous surface, scale effects, and complex terrains will be the trend of future research. Our work provides a clear view of the development of the MODIS LAI/FPAR products and valuable information for scholars to broaden their research fields.

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

In terrestrial ecosystems, leaves are aggregated into different spatial structures and their spatial distribution is non-random. Clumping index (CI) is a key canopy structural parameter, characterizing the extent to which leaf deviates from the random distribution. To assess leaf clumping effects on global terrestrial ET, we used a global leaf area index (LAI) map and the latest version of global CI product derived from MODIS BRDF data as well as the Boreal Ecosystem Productivity Simulator (BEPS) to estimate global terrestrial ET. The results show that global terrestrial ET in 2015 was 511.9 ± 70.1 mm yr−1 for Case I, where the true LAI and CI are used. Compared to this baseline case, (1) global terrestrial ET is overestimated by 4.7% for Case II where true LAI is used ignoring clumping; (2) global terrestrial ET is underestimated by 13.0% for Case III where effective LAI is used ignoring clumping. Among all plant functional types (PFTs), evergreen needleleaf forests were most affected by foliage clumping for ET estimation in Case II, because they are most clumped with the lowest CI. Deciduous broadleaf forests are affected by leaf clumping most in Case III because they have both high LAI and low CI compared to other PFTs. The leaf clumping effects on ET estimation in both Case II and Case III is robust to the errors in major input parameters. Thus, it is necessary to consider clumping effects in the simulation of global terrestrial ET, which has considerable implications for global water cycle research.

The Sanjiang Plain is the largest agricultural reclamation area and the biggest marsh area in China. The regional vegetation coverage in this area is vital to local ecological systems, and vegetation growth is affected by natural and anthropogenic factors. The clumping index (CI) is of great significance for land surface models and obtaining information on other vegetation structures. However, most existing ecological models and the retrieval of other vegetation structures do not consider the spatial and temporal variations of CI, and few studies have focused on detecting factors that influence the spatial differentiation of CI. To address these issues, this study investigated the spatial and temporal characteristics of foliage CI in the Sanjiang Plain, analysing the correlation between CI and leaf area index (LAI) through multiple methods (such as Theil−Sen trend analysis, the Mann−Kendall test, and the correlation coefficient) based on the 2001−2015 Chinese Academy of Sciences Clumping Index (CAS CI) and Global LAnd Surface Satellite Leaf Area Index (GLASS LAI). The driving factors of the spatial differentiation of CI were also investigated based on the geographical detector model (GDM) with natural data (including the average annual temperature, annual precipitation, elevation, slope, aspect, vegetation type, soil type, and geomorphic type) and anthropogenic data (the land use type). The results showed that (1) the interannual variation of foliage CI was not obvious, but the seasonal variation was obvious in the Sanjiang Plain from 2001 to 2015; (2) the spatial distribution of the multiyear mean CI of each season in the Sanjiang Plain was similar to the spatial distribution of the land use type, and the CI decreased slightly with increases in elevation; (3) the correlation between the growing season mean CI (CIGS) and the growing season mean LAI (LAIGS) time series was not significant, but their spatial distributions were negatively correlated; (4) topographic factors (elevation and slope) and geomorphic type dominated the spatial differentiation of foliage CI in the Sanjiang Plain, and the interactions between driving factors enhanced their explanatory power in terms of the spatial distribution of foliage CI. This study can help improve the accuracy of the retrieval of other vegetation structures and the simulation of land surface models in the Sanjiang Plain, providing invaluable insight for the analysis of the spatial and temporal variations of vegetation based on CI. Moreover, the results of this study support a theoretical basis for understanding the explanatory power of natural and anthropogenic factors in the spatial distribution of CI, along with its driving mechanism.

Leaf area index (LAI) is an important vegetation parameter. Active light detection and ranging (LiDAR) technology has been widely used to estimate vegetation LAI. In this study, LiDAR technology, LAI retrieval and validation methods, and impact factors are reviewed. First, the paper introduces types of LiDAR systems and LiDAR data preprocessing methods. After introducing the application of different LiDAR systems, LAI retrieval methods are described. Subsequently, the review discusses various LiDAR LAI validation schemes and limitations in LiDAR LAI validation. Finally, factors affecting LAI estimation are analyzed. The review presents that LAI is mainly estimated from LiDAR data by means of the correlation with the gap fraction and contact frequency, and also from the regression of forest biophysical parameters derived from LiDAR. Terrestrial laser scanning (TLS) can be used to effectively estimate the LAI and vertical foliage profile (VFP) within plots, but this method is affected by clumping, occlusion, voxel size, and woody material. Airborne laser scanning (ALS) covers relatively large areas in a spatially contiguous manner. However, the capability of describing the within-canopy structure is limited, and the accuracy of LAI estimation with ALS is affected by the height threshold and sampling size, and types of return. Spaceborne laser scanning (SLS) provides the global LAI and VFP, and the accuracy of estimation is affected by the footprint size and topography. The use of LiDAR instruments for the retrieval of the LAI and VFP has increased; however, current LiDAR LAI validation studies are mostly performed at local scales. Future research should explore new methods to invert LAI and VFP from LiDAR and enhance the quantitative analysis and large-scale validation of the parameters.

Understanding the impact of vegetation mixture and misclassification on leaf area index (LAI) estimation is crucial for algorithm development and the application community. Using the MODIS standard land cover and LAI products, global LAI climatologies and statistics were obtained for both pure and mixed pixels to evaluate the effects of biome mixture on LAI estimation. Misclassification between crops and shrubs does not generally translate into large LAI errors (