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Remote-sensed vegetation indices (VIs) have emerged as essential tools for retrieving forest chlorophyll concentration. Although VIs are widely used, some concerns regarding VIs for estimating chlorophyll remain to be addressed, such as saturation effect, leaf area index (LAI) disturbance, and soil brightness influence. Currently, a systematic study on such performance evaluation of chlorophyll-related VIs considering these issues is still insufficient. This study coupled two radiative transfer models, the PROSPECT model and the LESS model, to simulate Eucalyptus monocultures with different chlorophyll content and systematically evaluated the 18 broad-band VIs’ ability in chlorophyll estimation at different scales. Our results indicate that most VIs designed for chlorophyll estimation were relatively resistant to saturation, except for SIPI and some classical VIs (e.g., NDVI and DVI), which were insensitive to chlorophyll decreases and tended to reach saturation quickly (when leaf chlorophyll content (LCC) exceeded 40 ug/cm2). The relationships between NDVI, SR, DVI, and LCC were easily influenced by soil brightness and LAI. S2REP, MTCI, TGI, TCARI, and EVI were insensitive to soil brightness when estimating LCC. Overall, S2REP was best at quantitatively retrieving chlorophyll and resisting interference from other factors. For practical applications, our study suggests that it is preferable to use S2REP for LCC estimation when the red-edge band is available; otherwise, CVI can be used instead. The judicious utilization of VI can effectively depict chlorophyll levels and improve the understanding of vegetation response to climate change. Our findings provide the necessary information for the selection of specific VIs tailored to specific vegetation parameters.

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.