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The vegetation canopy clumping index (CI) is an important indicator for understanding radiative transport processes, radiation interception, and the photosynthesis of vegetation canopies. However, most studies consider CI only in the nadir or specific direction. In this study, we analyze the directional characteristics of the CI based on RAMI-V (radiation transfer model intercomparison) activity, which represents most typical canopies. The directional gap fraction and CI of these scenes are accurately calculated based on the LESS (large-scale remote sensing data and image simulation framework) model. According to our results, the directional characteristics of the CI are affected by many factors, such as vegetation type, season, and canopy structure. Generally, the CI of a coniferous forest varies little with zenith angle, while the CI of a broad-leaf forest demonstrates the different trend. In winter, the CI is smaller than that in summer, and the variation in the CI at the zenith angle is less. The row structure scenes exhibit different directional characteristics along and perpendicular to the row direction, and their CIs tend to increase with zenith angle. To accurately model the directional CI, we propose a modified Gompertz function model. Compared with other directional CI models, this model has the advantages of high precision and strong applicability (R2 = 0.975). By studying the directional characteristics of CI, we can enhance the usability of radiative transfer modeling and the accuracy of canopy biophysical parameter retrieval for vegetation with different structures.

Fractional vegetation cover (FVC) is a critical biophysical parameter that characterizes the status of terrestrial ecosystems. The spatial resolutions of most existing FVC products are still at the kilometer level. However, there is growing demand for FVC products with high spatial and temporal resolutions in remote sensing applications. This study developed an operational method to generate 30-m/15-day FVC products over China. Landsat datasets were employed to generate a continuous normalized difference vegetation index (NDVI) time series based on the Google Earth Engine platform from 2010 to 2020. The NDVI was transformed to FVC using an improved vegetation index (VI)-based mixture model, which quantitatively calculated the pixelwise coefficients to transform the NDVI to FVC. A comparison between the generated FVC, the Global LAnd Surface Satellite (GLASS) FVC, and a global FVC product (GEOV3 FVC) indicated consistent spatial patterns and temporal profiles, with a root mean square deviation (RMSD) value near 0.1 and an R2 value of approximately 0.8. Direct validation was conducted using ground measurements from croplands at the Huailai site and forests at the Saihanba site. Additionally, validation was performed with the FVC time series data observed at 151 plots in 22 small watersheds. The generated FVC showed a reasonable accuracy (RMSD values of less than 0.10 for the Huailai and Saihanba sites) and temporal trajectories that were similar to the field-measured FVC (RMSD values below 0.1 and R2 values of approximately 0.9 for most small watersheds). The proposed method outperformed the traditional VI-based mixture model and had the practicability and flexibility to generate the FVC at different resolutions and at a large scale.

The bidirectional reflectance distribution function (BRDF) of the land surface contains information relating to its physical structure and composition. Accurate BRDF modeling for heterogeneous pixels is important for global ecosystem monitoring and radiation balance studies. However, the original kernel-driven models, which many operational BRDF/Albedo algorithms have adopted, do not explicitly consider the heterogeneity within heterogeneous pixels, which may result in large fitting residuals. In this paper, we attempted to improve the fitting ability of the kernel-driven models over heterogeneous pixels by changing the inversion approach and proposed a dynamic weighted least squares (DWLS) inversion approach. The performance of DWLS and the traditional ordinary least squares (OLS) inversion approach were compared using simulated data. We also evaluated its ability to reconstruct multiangle satellite observations and provide accurate BRDF using unmanned aerial vehicle observations. The results show that the developed DWLS approach improves the accuracy of modeled BRDF of heterogeneous pixels. The DWLS approach applied to satellite observations shows better performance than the OLS method in study regions and exhibits smaller mean fitting residuals both in the red and near-infrared bands. The DWLS approach also shows higher BRDF modeling accuracy than the OLS approach with unmanned aerial vehicle observations. These results indicate that the DWLS inversion approach can be a better choice when kernel-driven models are used for heterogeneous pixels.

Radiative transfer (RT) simulation based on reconstructed 3-dimensional (3D) vegetation scenarios can promote the validation and development of various retrieval algorithms to monitor the growing states of vegetation in large-scale, multi-angular, and multi-sensor ways. The radiation transfer model intercomparison (RAMI) has made great contributions to providing abstract and actual 3D vegetation scenarios, and to the benchmarking of RT models under developed evaluation systems. To date, RAMI has been updated to the fifth phase (RAMI-V). In this study, we try to implement explicit conversion from all the RAMI-V scenes to generic structural models in the Wavefront OBJ format. These reconstructed scenes are applied in the LESS RT model to probe the ability of its RT solvers to simulate all sorts of remote sensing observations and radiative budget, including the bidirectional reflectance factor (BRF), albedo, fraction of photosynthetically active radiation absorbed by vegetation, and threshold hemispherical photograph (THP). BRF simulations fully explain angle effects as well as variation and robustness of the normalized difference vegetation index. Energy conservation is well validated between simulated absorption and albedo. The gap fraction derived from THP is analyzed in directional and total situations. In addition, this paper guides us how to simplify basic geometries and tune the illumination resolution (0.02 is optimal) to balance the simulation accuracy and efficiency. The generic structural models and reliable simulation results can be referenced by other RT models and retrieval algorithms.

Both leaf inclination angle distribution (LAD) and leaf area index (LAI) dominate optical remote sensing signals. The G-function, which is a function of LAD and remote sensing geometry, is often set to 0.5 in the LAI retrieval of coniferous canopies even though this assumption is only valid for spherical LAD. Large uncertainties are thus introduced. However, because numerous tiny leaves grow on conifers, it is nearly impossible to quantitatively evaluate such uncertainties in LAI retrieval. In this study, we proposed a method to characterize the possible change of G-function of coniferous canopies as well as its effect on LAI retrieval. Specifically, a Multi-Directional Imager (MDI) was developed to capture stereo images of the branches, and the needles were reconstructed. The accuracy of the inclination angles calculated from the reconstructed needles was high. Moreover, we analyzed whether a spherical distribution is a valid assumption for coniferous canopies by calculating the possible range of the G-function from the measured LADs of branches of Larch and Spruce and the true G-functions of other species from some existing inventory data and three-dimensional (3D) tree models. Results show that the constant G assumption introduces large errors in LAI retrieval, which could be as large as 53% in the zenithal viewing direction used by spaceborne LiDAR. As a result, accurate LAD estimation is recommended. In the absence of such data, our results show that a viewing zenith angle between 45 and 65 degrees is a good choice, at which the errors of LAI retrieval caused by the spherical assumption will be less than 10% for coniferous canopies.