Quantifying methane emissions in major rice growing areas of Tamil Nadu using remote sensing and land surface temperature model

Authors

DOI:

https://doi.org/10.14719/pst.5604

Keywords:

LST, Methane emission, rice area, static closed chamber, Synthetic Aperture, Radar

Abstract

Rice (Oryza sativa L.) is cultivated in diverse environments, contributing significantly to global methane (CH4) emissions, accounting for approximately 12% of total methane emissions worldwide. With the demand for increasing rice production, methane emissions from rice fields continue to rise. This underscores the need for reliable strategies for estimation and mitigation strategies. This study aims to estimate methane emissions from rice fields of the Cauvery Delta region of Tamil Nadu using remote sensing data. Sentinel 1A Synthetic Aperture Radar (SAR) data was used to delineate rice areas and assess agronomic flooding, while Land Surface Temperature (LST) derived from MODIS satellite data was used to estimate methane emissions. The semi-empirical methane emission model was employed to estimate methane flux based on temperature-related factors and rice area. The spatial methane estimates derived from the LST-based method were compared with field observations. The results showed that during the Kharif and Rabi seasons of 2023, a total of 169679 and 356270 ha of rice area were delineated, respectively. The total methane emissions of 7.16 and 17.09 Gg were estimated for both seasons, respectively. The agreement between estimated and observed methane for both seasons was 84.74 % and 87.52 %, respectively. This study provides an efficient empirical method for estimating methane emissions across large areas and highlights the need for continued monitoring and the development of mitigation strategies to reduce methane emissions from rice cultivation.

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Published

25-12-2024

How to Cite

1.
Tamilmounika R, Muthumanickam D, Pazhanivelan S, Ragunath KP, Kumaraperumal R, Sivamurugan AP, Satheesh S, Sudarmanian NS. Quantifying methane emissions in major rice growing areas of Tamil Nadu using remote sensing and land surface temperature model. Plant Sci. Today [Internet]. 2024 Dec. 25 [cited 2024 Dec. 27];11(sp4). Available from: https://horizonepublishing.com/journals/index.php/PST/article/view/5604

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