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

Luman Li,Zhaocheng Zeng,Hao Song,Zhonghua He,Liping Lei

doi:10.3390/rs14030654

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.

Highlights

Atmospheric methane (CH4 ), as one of the most important greenhouse gases, is second only to carbon dioxide (CO2 ) in contributing to global warming [1]
We proposed a data-driven approach based on a spatiotemporal geostatistical model to generate a global land Mapping-XCH4 dataset in 1◦ × 1◦ grids and intervals of 3 days from 2009 to 2020 using XCH4 retrievals derived by Gases Observing Satellite (GOSAT) observations
We evaluated the performance of the Mapping-XCH4 dataset by primarily investigating the spatial pattern and timely variation of XCH4 and comparing it with the model simulations

Summary

Introduction

Atmospheric methane (CH4 ), as one of the most important greenhouse gases, is second only to carbon dioxide (CO2 ) in contributing to global warming [1]. The global atmospheric CH4 concentration has increased from a preindustrial level of 722 ppb to approximately 1750 ppb in 2000 and continues to increase [2,3,4,5] due to the influence of anthropogenic emissions such as fossil fuel combustion, agricultural planting, livestock. Countries all over the world have begun to take actions to reduce CH4 emission to prevent the continuous increase of atmospheric CH4 concentrations [2]. Ground-based measurement networks have provided long-term and high-precision greenhouse gas data [10,11]. These stations, are sparsely and unevenly distributed and highly costly [12]. It has become an important way to obtain global and regional atmospheric CH4 [13]

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