Using the BFAST Algorithm and Multitemporal AIRS Data to Investigate Variation of Atmospheric Methane Concentration over Zoige Wetland of China.

The monitoring of wetland methane (CH₄) emission is essential in the context of global CH₄ emission and climate change. The remotely sensed multitemporal Atmospheric Infrared Sounder (AIRS) CH₄ data and the Breaks for Additive Season and Trend (BFAST) algorithm were used to detect atmospheric CH₄ dynamics in the Zoige wetland, China between 2002 and 2018. The overall atmospheric CH₄ concentration increased steadily with a rate of 5.7 ± 0.3 ppb/year. After decomposing the time-series of CH₄ data using the BFAST algorithm, we found no anomalies in the seasonal and error components. The trend component increased with time, and a total of seven breaks were detected within four cells. Six were well-explained by the air temperature anomalies primarily, but one break was not. The effect of parameter h on decomposition outcomes was studied because it could influence the number of breaks in the trend component. As h increased, the number of breaks decreased. The interplays of the observations of interest, break numbers, and statistical significance should determine the h value. [ABSTRACT FROM AUTHOR]