Journal
REMOTE SENSING OF ENVIRONMENT
Volume 290, Issue -, Pages -Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2023.113553
Keywords
Blue carbon; Salt marshes; Tidal wetlands; Leaf area; Carbon modeling; Remote sensing
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The IPCC Special Report on the Ocean and Cryosphere in a Changing Climate emphasizes the significance of blue carbon in tidal wetlands for combatting climate change. This study focuses on the uncertainty surrounding leaf area index (LAI) estimations in tidal wetlands, particularly salt marshes, which is a crucial vegetation parameter for productivity and Earth System Models (ESMs). The research presents a novel approach for measuring spatiotemporal dynamics in tidal wetland emergent leaf area index (ELAI) and demonstrates the correlations between ELAI and carbon fluxes. The study also highlights the feasibility of estimating ELAI using Sentinel-2 satellite data and addresses the underestimation and overestimation of LAI by a commonly used product, MODIS MYD15A2H.
The IPCC Special Report on the Ocean and Cryosphere in a Changing Climate highlights the importance of blue carbon in tidal wetlands in combating climate change. In this study, we highlight the uncertainty associated with leaf area index (LAI) estimations in tidal wetlands, specifically salt marshes, a key vegetation parameter for productivity models and Earth System Models (ESM). LAI, derived from satellite reflectance data, is linked to atmospheric carbon exchange and gross primary production (GPP) across vegetative ecosystems. However, estimating salt marsh LAI is challenging because canopy height and density vary across short distances, and tidal flooding alters the atmosphere-exposed leaf area, hereafter called emergent leaf area index (ELAI), at short time scales. Further, in tidal wetlands dominated by species such as Spartina alterniflora, canopy height and density vary across short distances. We present a novel approach for measuring spatiotemporal dynamics in tidal wetland ELAI. We modeled ELAI from vertical LAI profiles and created spatial estimates across tidal periods. We then linked ELAI with eddy covariance carbon (C) fluxes through footprint modeling and revealed correlations between emergent leaf area and C fluxes. Next, we demonstrated that ELAI can be readily estimated across 10-m spatial scales using Sentinel-2 satellite data, even during high tides (R2 = 0.89; NRMSE = 10%). Finally, we showed a common product, MODIS MYD15A2H, underestimated (20%) LAI during dry conditions but overestimated (7-93%) during high flooding. Dynamic ELAI could reduce uncertainties in satellite-derived global GPP products when developing blue carbon budgets for ecosystems threatened by accelerated sea level rise.
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