Using the BFAST Algorithm and Multitemporal AIRS Data to Investigate Variation of Atmospheric Methane Concentration over Zoige Wetland of China
Using the BFAST Algorithm and Multitemporal AIRS Data to Investigate Variation of Atmospheric Methane Concentration over Zoige Wetland of China
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DOI：
10.3390/rs12193199
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Yuanyuan Yang;Yong F. Wang
中科院分区：
文献类型：
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作者：
Yuanyuan Yang;Yong F. Wang
在全球甲烷排放和气候变化的背景下，监测湿地甲烷（CH4）排放至关重要。利用遥感多时相大气红外探测仪（AIRS）CH4数据和加性季节与趋势中断（BFAST）算法对2002年至2018年中国若尔盖湿地大气CH4动态进行了探测。大气CH4总体浓度以5.7±0.3ppb/年的速度稳步上升。使用 BFAST 算法对 CH4 数据的时间序列进行分解后，我们发现季节和误差分量没有异常。趋势成分随着时间的推移而增加，四个单元格内总共检测到七个中断。其中六次主要由气温异常得到了很好的解释，但有一次则不能。研究了参数 h 对分解结果的影响，因为它会影响趋势分量的中断数量。随着 h 的增加，中断次数减少。感兴趣的观察结果、断裂数和统计显着性的相互作用应确定 h 值。
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.