Статья

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

Si GaoInnovation Research Center of Satellite Application (IRCSA), Institute of Remote Sensing Science and Engineering, State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaKai YanInnovation Research Center of Satellite Application (IRCSA), Institute of Remote Sensing Science and Engineering, State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaJinxiu LiuSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaJiabin PuDepartment of Earth and Environment, Boston University, Boston, MA 02215, USADongxiao ZouInstitute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaJianbo QiInnovation Research Center of Satellite Application (IRCSA), Institute of Remote Sensing Science and Engineering, State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaXihan MuInnovation Research Center of Satellite Application (IRCSA), Institute of Remote Sensing Science and Engineering, State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaGuangjian YanInnovation Research Center of Satellite Application (IRCSA), Institute of Remote Sensing Science and Engineering, State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

2024en

ABI

Аннотация

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.

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Идентификаторы

DOI: 10.1016/j.ecolind.2024.112001

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