Spatiotemporal Geostatistical Analysis and Global Mapping of CH4 Columns from GOSAT Observations

Abstract

Methane (CH4) is one of the most important greenhouse gases causing the global warming effect. The mapping data of atmospheric CH4 concentrations in space and time can help us better to understand the characteristics and driving factors of CH4 variation as to support the actions of CH4 emission reduction for preventing the continuous increase of atmospheric CH4 concentrations. In this study, we applied a spatiotemporal geostatistical analysis and prediction to develop an approach to generate the mapping CH4 dataset (Mapping-XCH4) in 1° grid and three days globally using column averaged dry air mole fraction of CH4 (XCH4) data derived from observations of the Greenhouse Gases Observing Satellite (GOSAT) from April 2009 to April 2020. Cross-validation for the spatiotemporal geostatistical predictions showed better correlation coefficient of 0.97 and a mean absolute prediction error of 7.66 ppb. The standard deviation is 11.42 ppb when comparing the Mapping-XCH4 data with the ground measurements from the total carbon column observing network (TCCON). Moreover, we assessed the performance of this Mapping-XCH4 dataset by comparing with the XCH4 simulations from the CarbonTracker model and primarily investigating the variations of XCH4 from April 2009 to April 2020. The results showed that the mean annual increase in XCH4 was 7.5 ppb/yr derived from Mapping-XCH4, which was slightly greater than 7.3 ppb/yr from the ground observational network during the past 10 years from 2010. XCH4 is larger in South Asia and eastern China than in the other regions, which agrees with the XCH4 simulations. The Mapping-XCH4 shows a significant linear relationship and a correlation coefficient of determination (R2) of 0.66, with EDGAR emission inventories over Monsoon Asia. Moreover, we found that Mapping-XCH4 could detect the reduction of XCH4 in the period of lockdown from January to April 2020 in China, likely due to the COVID-19 pandemic. In conclusion, we can apply GOSAT observations over a long period from 2009 to 2020 to generate a spatiotemporally continuous dataset globally using geostatistical analysis. This long-term Mpping-XCH4 dataset has great potential for understanding the spatiotemporal variations of CH4 concentrations induced by natural processes and anthropogenic emissions at a global and regional scale.

Spatial and Temporal Variations of Atmospheric CH4 in Monsoon Asia Detected by Satellite Observations of GOSAT and TROPOMI

“…In this model, the nugget/sill ratio of about 0.11 indicates a strong spatial and temporal correlation of XCH4 data in the monsoon region in Asia. Compared to the previous studies, it outperforms the correlation calculated using only GOSAT XCH4 data in the Eurasia region [19]. (3) The mapping of gridded XCH4 data…”

“…The spatiotemporal correlation analysis aims to separate spatiotemporal deterministic trends and stochastic residual components from regional variables of XCH 4 , and further characterize the spatial and temporal correlations of XCH 4 data in the study area [13,14,19]. In this paper, we applied the curve fitting method to long-term XCH 4 data in each 2 • latitude band, which referred to Sheng et al [13].…”

“…With the accumulation of geographic spatiotemporal data and the development of the theory of spatiotemporal variation function, geostatistic methods have also been extended from space to the use of spatiotemporal interpolation. Liu and Li et al [18,19] carried out global or regional XCH 4 mapping analysis using single satellite observation data based on spatiotemporal and geostatistical methods, and the results showed that the spatiotemporal distribution of regional CH 4 concentrations was correlated with the anthropogenic emission inventory, especially in eastern China, Bangladesh, the Indo-China Peninsula, and nearby regions. However, the observation time series and effective data points of a single satellite are limited in these studies.…”

Atmospheric Lengthscales for Global VSWIR Imaging Spectroscopy

“…Surface elevation changes can also induce some gradients in the total observed absorption because water vapor is concentrated in the lower troposphere. Absorption by gases like CH 4 and CO 2 is more constant (Li et al., 2022 ; Lin et al., 2004 ). Their background variability does not generally exceed 1%, a difference that is virtually undetectable by VSWIR instruments.…”

Unveiling the impact of soil methane sink on atmospheric methane concentrations in 2020

“…Due to the implementation of COVID‐19 containment policies, anthropogenic CH 4 emissions in 2020 experienced a decrease of 1.2 ± 0.1 Tg compared to 2019 (Li et al., 2022; Peng et al., 2022). However, data from the global monitoring laboratory network of the National Oceanic and Atmospheric Administration revealed an abnormal increase of 15.1 ppb in atmospheric CH 4 concentration during this year, equivalent to 14.4 Tg CH 4 (Lan et al., 2022).…”