Integrating NDVI-Based Within-Wetland Vegetation Classification in a Land Surface Model Improves Methane Emission Estimations

Abstract

Earth system models (ESMs) are a common tool for estimating local and global greenhouse gas emissions under current and projected future conditions. Efforts are underway to expand the representation of wetlands in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) by resolving the simultaneous contributions to greenhouse gas fluxes from multiple, different, sub-grid-scale patch-types, representing different eco-hydrological patches within a wetland. However, for this effort to be effective, it should be coupled with the detection and mapping of within-wetland eco-hydrological patches in real-world wetlands, providing models with corresponding information about vegetation cover. In this short communication, we describe the application of a recently developed NDVI-based method for within-wetland vegetation classification on a coastal wetland in Louisiana and the use of the resulting yearly vegetation cover as input for ELM simulations. Processed Harmonized Landsat and Sentinel-2 (HLS) datasets were used to drive the sub-grid composition of simulated wetland vegetation each year, thus tracking the spatial heterogeneity of wetlands at sufficient spatial and temporal resolutions and providing necessary input for improving the estimation of methane emissions from wetlands. Our results show that including NDVI-based classification in an ELM reduced the uncertainty in predicted methane flux by decreasing the model’s RMSE when compared to Eddy Covariance measurements, while a minimal bias was introduced due to the resampling technique involved in processing HLS data. Our study shows promising results in integrating the remote sensing-based classification of within-wetland vegetation cover into earth system models, while improving their performances toward more accurate predictions of important greenhouse gas emissions.

Three Decades of Wetland Methane Surface Flux Modeling by Earth System Models‐Advances, Applications, and Challenges

“…These data sets were combined with spatial estimations of wetland extent from remote sensing (Bohn et al, 2015;Gerlein-Safdi et al, 2021;Kuhn et al, 2021;Melton et al, 2013) to generate regional and global button-up estimates of methane fluxes from wetlands (Bloom et al, 2017;Poulter et al, 2017;Saunois et al, 2016;Saunois et al, 2020). Advancements in remote sensing continuously improve the estimate of wetland extent (Jensen & Mcdonald, 2019), and have recently started estimating water table depth, water elevation, and vegetation type characterization to improve wetland detection and characterization (Burdun et al, 2023;Domeneghetti et al, 2018;Hess et al, 2015;Ju & Bohrer, 2022;Melton et al, 2022;Yazbeck et al, 2024). Recently, the development of fast methane gas analyzers, which can be used as a part of eddy covariance systems, has led to a significant increase in surface flux research related to wetlands.…”