High-Resolution Estimation of Methane Emissions from Boreal and Pan-Arctic Wetlands Using Advanced Satellite Data

• Published in: Remote Sensing
• Main Authors: Yousef A. Y. Albuhaisi, Ype van der Velde, Richard De Jeu, Zhen Zhang, Sander Houweling
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: soil moisture; CH 4; carbon budget; wetlands; satellite soil moisture; hydrological model; Science; Q; Arctic; Nash; Sutcliffe
• Online Access: https://doi.org/10.3390/rs15133433; https://doaj.org/article/f42b5ad07ec243718061e6c4e228ba84

This paper investigates the use of soil moisture data from satellites and a hydrological model as inputs to a simplified CH 4 emission model (MeSMOD) for estimating CH 4 emissions from boreal and pan-Arctic regions between 2015 and 2021. MeSMOD is calibrated using FLUXNET—CH 4 sites and the predictive performance is evaluated using several metrics, including the Nash-Sutcliffe efficiency (NSE). Using satellite soil moisture with 100 m resolution, MeSMOD has the highest performance (NSE = 0.63) compared with using satellite soil moisture of 10 km and hydrological model soil moisture of 10 km and 50 km (NSE = 0.59, 0.56, and 0.53, respectively) against site-level CH 4 flux. This study has upscaled the estimates to the pan-Arctic region using MeSMOD, resulting in comparable mean annual estimates of CH 4 emissions using satellite soil moisture of 10 km (33 Tg CH 4 yr −1 ) and hydrological model soil moisture of 10 km (39 Tg CH 4 yr −1 ) compared with previous studies using random forest technique for upscaling (29.5 Tg CH 4 yr −1 ), LPJ-wsl process model (30 Tg CH 4 yr −1 ), and CH 4 CAMS inversion (34 Tg CH 4 yr −1 ). MeSMOD has also accurately captured the high methane emissions observed by LPJ-wsl and CAMS in 2016 and 2020 and effectively caught the interannual variability of CH 4 emissions from 2015 to 2021. The study emphasizes the importance of using high-resolution satellite soil moisture data for accurate estimation of CH 4 emissions from wetlands, as these data directly reflect soil moisture conditions and lead to more reliable estimates. The approach adopted in this study helps to reduce errors and improve our understanding of wetlands’ role in CH 4 emissions, ultimately reducing uncertainties in global CH 4 budgets.