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Challenges and opportunities in detecting leaf water and carotenoid content across biomes from satellite multispectral indices

Created on 05 Nov 2025

Authors

Montes-Bojorquez, M. G., Robles-Zazueta, C. A., Tinoco-Ojanguren, C., Enquist, B. J., Frazier, A. E., Maitner, B. S., Massaine Moulatlet, G., Romo-Leon, J. R., Song, L., Hinojo-Hinojo, C.

Abstract

Climate change is causing vegetation stress across the globe, increasing the need for reliable indicators to monitor plant health. Leaf water and carotenoid content, and the chlorophyll/carotenoid ratio, are established proxies for environmental stress that can be detected by remote sensing. Here, we evaluated the sensitivity of 11 multispectral vegetation indices (VIs) designed to monitor these three stress-related leaf traits across a broad range of environmental and vegetation conditions. For this, we combined radiative transfer modeling with cross-biome field and satellite observations from Sentinel-2, Landsat 8, and MODIS from the National Ecological Observatory Network (NEON), spanning in most major terrestrial ecosystems. Our model-based analysis showed that VIs have a low to moderate sensitivity to their target traits, ranging from water indices with 66% of their variability explained by leaf water content, to carotenoid indices with 27% variability explained by leaf carotenoid content. Surprisingly, our field-based analyses revealed minimal to no sensitivity to leaf water and carotenoid content and chlorophyll/carotenoid ratio across all VIs. In contrast, we showed that leaf area index was the dominant driver of all studied VIs, accounting for 54-74 % of their variability in the field-based analysis. Lastly, we detected that VIs' sensitivity to atmospheric conditions and field sampling issues contribute to their low performance in validating ground truth observations. These findings show that improvements in the VIs formulation and field sampling strategies are needed to increase the reliability of vegetation stress monitoring from multispectral satellites and support a generalized use of VIs across ecosystems.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 05 Nov 2025.

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