Soil models are widely used to characterize the reflectance properties of dry and wet soils. By considering detailed physical processes, the improved multilayer radiative transfer model of soil reflectance (MARMIT-2) model significantly improves the accuracy of simulating wet soil properties. However, the MARMIT-2 model relies on measured dry soil reflectance as an input, which limits its applicability in practical scenarios, especially when detailed information about specific soils is unavailable. To address this issue, this study first evaluated the ability of the general spectral vector (GSV) model of dry soil to represent the reflectance properties of dry soil. Then, we coupled these dry soil vectors with the MARMIT-2 model to propose the GSV + MARMIT-2 model. Finally, we assessed the accuracy of all three models using a wet soil database. The main conclusions of this study include: 1) the dry soil spectral vectors from the GSV model demonstrated high accuracy in describing the reflectance properties of dry soil, achieving an$R^{2}$of 0.988 and a root mean square error (RMSE) of 0.016. 2) All three soil models exhibited high fitting accuracy for the wet soil database ($R^{{2}} = \sim 0.992$and RMSE$= \sim 0.012$). Compared to the GSV and MARMIT-2 models, the GSV + MARMIT-2 model showed slightly improved accuracy under different soil moisture content (SMC) conditions. This study developed a more versatile and flexible soil model framework as it directly integrates the dry soil spectral vectors from the GSV model into the MARMIT-2 model. This coupling significantly expanded the applicability and improved the stability of the MARMIT-2 model.

Sloping terrain distorts the sun-target-sensor geometry, resulting in biases of the optical reflectance measured by remote sensors relative to flat situations. Performing topographic correction (TC) is, therefore, deemed mandatory to foster the full exploitation of satellite images worldwide to support various applications in mountainous regions. Various TC models have already been proposed and developed, while most of them were previously evaluated at local or regional scales using a few images with various evaluation criteria. Therefore, a systematic and comprehensive assessment has yet to be done on these TC models in the global mountainous regions. In the present study, 10523 Landsat 8 OLI images filtered by land cover types and seasons sampled in the global mountains are corrected by ten popular TC models (SE, b correction, VECA, CC, SCS, DS, SCS+C, PLC, Minnaert, and Minnaert+SCS) with a unified evaluation criterion on the Google Earth Engine platform. The outcomes are that: (1) global TC effects on Landsat 8 OLI images generally increase with sun zenith angles and latitudes; (2) six models (SE, b correction, CC, VECA, Minnaert, and Minnaert+SCS) show good adaptability among the ten models for the global mountainous placing a disregard to land cover types and seasons; (3) considering permanent snow and ice, needle-leaved forests in winter, and null values might appear in b correction, SE is deemed to be with the most global adaptability. This study pioneers an evaluation of fashionable TC models concerning mountainous regions worldwide and will be useful for applying TC to Landsat images for the benefit of making global TC products in the future and a fair inter-comparison of OLI surface reflectance measured in various mountainous areas of the globe.

Terrain elevation over forested areas is important for studies such as hydrological modeling and soil erosion. The spaceborne full-waveform LiDAR missions including Geoscience Laser Altimeter System (GLAS) and Global Ecosystem Dynamics Investigation (GEDI) provide freely available terrain elevation products indirectly and directly. However, the accuracies have seldom been evaluated in the same region. Here, we examined the terrain elevation accuracy and assessed the influence of terrain slope in forested areas using high-resolution airborne LiDAR data as a reference. The root mean square error (RMSE) of terrain elevation computed from all the data of GLAS and GEDI is 5.1 m and 8.4 m, respectively. Even though the footprint diameter of GEDI is much smaller than GLAS (25 m vs. 65 m), we still found a significant terrain effect with the increase of slope in GEDI. The RMSE of terrain elevation from GLAS is 3.4 m, 7.6 m, and 10.5 m when the slope ranges from 0° to 30° with an increment of 10°. The RMSE of terrain elevation from GEDI is 5.2 m, 8.8 m, 12.2 m, 14.1 m, and 16. 9 m when the slope ranges from 0° to 50° with an increment of 10°.

In this paper, we develop the Slant Leaf Area Index based Method (SLAIM) to estimate the leaf area of individual trees from single-scan Terrestrial Laser Scanner (TLS) data by introducing the concept of Slant Leaf Area Index (SLAI). SLAI quantifies the amount of leaves along the view direction and can be retrieved at given view zeniths using gap probability. Subsequently, leaf area can be accumulated by SLAI across the whole crown. The innovative SLAIM offers several advantages. Firstly, it operates with single-scan point clouds, which are more accessible than multiple-scan data. Secondly, it effectively corrects the clumping effect resulting from non-uniform leaf distribution. Both simulated and field-measured TLS point clouds of trees are used to test the method. The results show that the error of SLAIM is less than 10% in most cases.

Information about the composition and distribution of dominant tree species is crucial for sustainable forest management. A global ecosystem dynamics investigation (GEDI) offers unique advantages in detecting the vertical spatial structure of forest stands, which may improve the common issues of spectral similarity and saturation in traditional spectral-based tree species mapping. However, the effects of its application have not been explored. This study examines temperate and subtropical forests in eastern China, which are dominated by deciduous and evergreen species, respectively. We employed GEDI-derived forest vertical structure characterization (FVSC) to complement Sentinel-2 spectral features for dominant tree species mapping. The results indicate that FVSC significantly improved the mapping accuracy for 11 tree species in both temperate and subtropical forest regions across seasons, with greater benefits observed for broadleaf species than for coniferous species. During the main phenological stages of spring, summer, and autumn, the accuracy of tree species mapping in the temperate region improved by 6.99%–9.85%. The key contributing factors were the cumulative plant area index (PAI) from the ground to the canopy top and cumulative vegetation coverage (COVER) from 5 m to the canopy top. In the subtropical region, the accuracy improvement ranged from 7.75% to 9.5%, with the highest contributions from the plant gap probability (Pgap_theta) and cumulative COVER from 5 m to the canopy top. These findings demonstrate that FVSC can effectively support spectral feature data in the detailed mapping of dominant tree species at regional scales. Moreover, the method shows good stability and applicability across seasons and climatic regions.

Individual trees are fundamental to urban ecosystems as they play an important role in energy transfer, pollutant removal, and habitat formation. Leaf area (LA) is an important factor to quantify the effect of individual trees on urban ecosystems. Terrestrial laser scanners (TLSs) are widely recognized as the most accurate devices for tree structural measurements. However, they face challenges in estimating LA from LA index (LAI) for individual trees primarily due to arbitrary and confusing horizontal projection areas. Occlusion and clumping effects further hinder the objective and accurate LA measurements of individual trees. Therefore, we developed the slant leaf area index-based method (SLAIM) to estimate the LA of individual trees from single-scan TLS data by introducing the concept of slant leaf area index (SLAI). SLAI quantifies the amount of leaves along the view direction, and it can be retrieved at given view zeniths using gap probability. Subsequently, LA can be accumulated by SLAI across the whole crown. Tests with simulated and field-measured TLS point clouds demonstrate SLAIM’s accuracy, with the relative errors (REs) in LA below 10% in most cases. Stratified LA validation reveals an$R^{2}$exceeding 0.77 across all realistic crowns, along with a root-mean-square error (RMSE) under 2 m2. SLAIM’s advantages include compatibility with single-scan point clouds, effective correction of clumping effects, and consideration of variations in leaf projection coefficients at different zeniths. SLAIM proves more efficient and practical for actual LA measurements, showcasing its potential for advanced urban ecosystem research.

Clumping effect denotes the nonrandomness of foliage. It deviates from the random distribution assumption of Beer’s law which is usually applied to leaf area index (LAI) retrieval from large-footprint full-waveform light detection and ranging (LiDAR). Some studies correct for large gaps-induced between-crown clumping, yet ignore the within-crown clumping. The error of LAI caused by these clumping effects and the influence of the forest structure parameters on them have not been quantitatively studied. This study quantified the between-crown, within-crown, and total clumping indices through a theoretical derivation, clarifying the mechanism of clumping; we used airborne LiDAR point clouds data in 11 290 footprints (diameter = 25 m) to estimate these indices in real forests. We found that: 1) the underestimation of LAI caused by directly applying Beer’s law could be up to 93%, and it decreases with fractional crown coverage but increases with crown length and leaf area density; 2) the method of correcting between-crown clumping improves LAI retrieval for cylindrical canopies effectively; however, 3) considerable underestimation (up to 58%) exists if we neglect the within-crown clumping for other canopies, which has not been realized before; and 4) both the between-crown and the within-crown clumping can be the dominant contributor, and the within-crown clumping was greater than the between-crown clumping in 47% of the studied footprints. In the two physically based LAI retrieval methods, Beer’s law has been commonly used due to its simplicity. Pathways to improve future LAI retrieval would be instrument improvement to capture the between-crown gaps and method study to correct the within-crown clumping further.

Leaf inclination angle distribution plays an important role in indirect leaf area measurement methods. Path length distribution method (PATH) is an indirect leaf area measurement method based on Beer's Law, which has been applied to isolated trees with Terrestrial Laser Scanning (TLS). However, it can only set the leaf projection G to a constant value. In this paper, the PATH method considering leaf inclination models is introduced, which allows the G function to be a variable corresponding to leaf inclination. On the basis of this method, this paper explores the effect of different leaf inclination angle distribution assumptions on the inversion of the leaf area of isolated trees based on TLS. The results show that compared with the measured leaf inclination, the relative errors of the inversion results based on the six typical leaf inclination assumptions are between −14.3 % to +41.2%, which indicates that leaf inclination has a significant effect on the inversion of the leaf area. Further, experiments in this paper show that the mean value of the G function is a relatively accurate representation of it.