Assessment of remote-sensed vegetation indices for estimating forest chlorophyll concentration

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 broadband 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.