15 Jun 2022
15 Jun 2022
Status: this preprint is currently under review for the journal BG.

Variations in land types detected using methane retrieved from space-borne sensor

Saheba Bhatnagar1, Mahesh Kumar Sha2, Laurence Gill1, Bavo Langerock2, and Bidisha Ghosh1 Saheba Bhatnagar et al.
  • 1School of Engineering, Trinity College, Dublin, Ireland
  • 2Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium

Abstract. Methane (CH4), a potent greenhouse gas, traps heat in the atmosphere and significantly contributes to global warming. Atmospheric CH4 comes from various natural and anthropogenic sources. CH4 emissions from the decomposition of organic material by bacteria in natural wetlands, other land types, agriculture, and waste management constitute the major component of global emissions. Although there is no clear evidence that CH4 emissions from wetlands and other natural sources have increased substantially in the last decade, uncertainties remain regarding sources and their spatial extent causing discrepancies between emission estimates from inventories/models and estimates inferred by an ensemble of atmospheric inversions. Here we show that satellite-based CH4 total column measurements along with surface albedo from Sentinel-5 Precursor (S-5p) show unique sensitivity to certain land types. Consequently, the areal extent of six land types (marsh, swamp, forest, grassland, cropland, and barren-land) could be identified with high overall accuracy by analysing S-5p data over Canada utilising our classification-segmentation algorithm. Monthly and yearly inventory maps were created, which can be used to validate or complement global models where data from other sources are missing and may help in further constraining the methane budget.

Saheba Bhatnagar et al.

Status: open (until 19 Aug 2022)

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Saheba Bhatnagar et al.

Saheba Bhatnagar et al.


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Short summary
Different land types emit a different quantity of methane, with wetlands being one of the largest sources of methane emissions, contributing to climate change. This study finds variations in land types using the methane total column data from Sentinel 5-precursor satellite with a machine learning algorithm. The variations in land types were identified with high confidence, demonstrating that the methane emissions from the wetland and other land types substantially affect the total column.