Assessing methane emissions from rice fields in large irrigation projects using satellite-derived land surface temperature and agronomic flooding: A spatial analysis
Synthetic aperture radar (SAR) imagery, notably Sentinel-1A’s C-band, VV, and VH polarized SAR, has emerged as a crucial tool for mapping rice fields, especially in regions where cloud cover hinders optical imagery. Employing multi-temporal characteristics, SAR data were regularly collected and para...
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2024
|
| Acceso en línea: | https://hdl.handle.net/10568/163823 |
| _version_ | 1855518338345598976 |
|---|---|
| author | Pazhanivelan, Sellaperumal Sudarmanian, N.S. Geethalakshmi, Vellingiri Deiveegan, Murugesan Ragunath, Kaliaperumal Sivamurugan, A.P. Shanmugapriya, P. |
| author_browse | Deiveegan, Murugesan Geethalakshmi, Vellingiri Pazhanivelan, Sellaperumal Ragunath, Kaliaperumal Shanmugapriya, P. Sivamurugan, A.P. Sudarmanian, N.S. |
| author_facet | Pazhanivelan, Sellaperumal Sudarmanian, N.S. Geethalakshmi, Vellingiri Deiveegan, Murugesan Ragunath, Kaliaperumal Sivamurugan, A.P. Shanmugapriya, P. |
| author_sort | Pazhanivelan, Sellaperumal |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Synthetic aperture radar (SAR) imagery, notably Sentinel-1A’s C-band, VV, and VH polarized SAR, has emerged as a crucial tool for mapping rice fields, especially in regions where cloud cover hinders optical imagery. Employing multi-temporal characteristics, SAR data were regularly collected and parameterized using MAPscape-Rice software, which integrates a fully automated processing chain to convert the data into terrain-geocoded σ° values. This facilitated the generation of rice area maps through a rule-based classifier approach, with classification accuracies ranging from 88.5 to 91.5 and 87.5 percent in 2017, 2018, and 2022, respectively. To estimate methane emissions, IPCC (37.13 kg/ha/season, 42.10 kg/ha/season, 43.19 kg/ha/season) and LST (36.05 kg/ha/season, 41.44 kg/ha/season, 38.07 kg/ha/season) factors were utilized in 2017, 2018 and 2022. Total methane emissions were recorded as 19.813 Gg, 20.661 Gg, and 25.72 Gg using IPCC and 19.155 Gg, 20.373 Gg, and 22.76 Gg using LST factors in 2017, 2018 and 2022. Overall accuracy in methane emission estimation, assessed against field observations, ranged from (IPCC) 85.71, 91.32, and 80.25 percent to (LST) 83.69, 91.43, and 84.69 percent for the years 2017, 2018 and 2022, respectively, confirming the efficacy of remote sensing in greenhouse gas monitoring and its potential for evaluating the impact of large-scale water management strategies on methane emissions and carbon credit-based ecosystem services at regional or national levels. |
| format | Journal Article |
| id | CGSpace163823 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1638232025-12-08T10:29:22Z Assessing methane emissions from rice fields in large irrigation projects using satellite-derived land surface temperature and agronomic flooding: A spatial analysis Pazhanivelan, Sellaperumal Sudarmanian, N.S. Geethalakshmi, Vellingiri Deiveegan, Murugesan Ragunath, Kaliaperumal Sivamurugan, A.P. Shanmugapriya, P. Synthetic aperture radar (SAR) imagery, notably Sentinel-1A’s C-band, VV, and VH polarized SAR, has emerged as a crucial tool for mapping rice fields, especially in regions where cloud cover hinders optical imagery. Employing multi-temporal characteristics, SAR data were regularly collected and parameterized using MAPscape-Rice software, which integrates a fully automated processing chain to convert the data into terrain-geocoded σ° values. This facilitated the generation of rice area maps through a rule-based classifier approach, with classification accuracies ranging from 88.5 to 91.5 and 87.5 percent in 2017, 2018, and 2022, respectively. To estimate methane emissions, IPCC (37.13 kg/ha/season, 42.10 kg/ha/season, 43.19 kg/ha/season) and LST (36.05 kg/ha/season, 41.44 kg/ha/season, 38.07 kg/ha/season) factors were utilized in 2017, 2018 and 2022. Total methane emissions were recorded as 19.813 Gg, 20.661 Gg, and 25.72 Gg using IPCC and 19.155 Gg, 20.373 Gg, and 22.76 Gg using LST factors in 2017, 2018 and 2022. Overall accuracy in methane emission estimation, assessed against field observations, ranged from (IPCC) 85.71, 91.32, and 80.25 percent to (LST) 83.69, 91.43, and 84.69 percent for the years 2017, 2018 and 2022, respectively, confirming the efficacy of remote sensing in greenhouse gas monitoring and its potential for evaluating the impact of large-scale water management strategies on methane emissions and carbon credit-based ecosystem services at regional or national levels. 2024-03-19 2024-12-19T12:53:03Z 2024-12-19T12:53:03Z Journal Article https://hdl.handle.net/10568/163823 en Open Access MDPI Pazhanivelan, Sellaperumal; Sudarmanian, N. S.; Geethalakshmi, Vellingiri; Deiveegan, Murugesan; Ragunath, Kaliaperumal; Sivamurugan, A. P. and Shanmugapriya, P. 2024. Assessing methane emissions from rice fields in large irrigation projects using satellite-derived land surface temperature and agronomic flooding: A spatial analysis. Agriculture, Volume 14 no. 3 p. 496 |
| spellingShingle | Pazhanivelan, Sellaperumal Sudarmanian, N.S. Geethalakshmi, Vellingiri Deiveegan, Murugesan Ragunath, Kaliaperumal Sivamurugan, A.P. Shanmugapriya, P. Assessing methane emissions from rice fields in large irrigation projects using satellite-derived land surface temperature and agronomic flooding: A spatial analysis |
| title | Assessing methane emissions from rice fields in large irrigation projects using satellite-derived land surface temperature and agronomic flooding: A spatial analysis |
| title_full | Assessing methane emissions from rice fields in large irrigation projects using satellite-derived land surface temperature and agronomic flooding: A spatial analysis |
| title_fullStr | Assessing methane emissions from rice fields in large irrigation projects using satellite-derived land surface temperature and agronomic flooding: A spatial analysis |
| title_full_unstemmed | Assessing methane emissions from rice fields in large irrigation projects using satellite-derived land surface temperature and agronomic flooding: A spatial analysis |
| title_short | Assessing methane emissions from rice fields in large irrigation projects using satellite-derived land surface temperature and agronomic flooding: A spatial analysis |
| title_sort | assessing methane emissions from rice fields in large irrigation projects using satellite derived land surface temperature and agronomic flooding a spatial analysis |
| url | https://hdl.handle.net/10568/163823 |
| work_keys_str_mv | AT pazhanivelansellaperumal assessingmethaneemissionsfromricefieldsinlargeirrigationprojectsusingsatellitederivedlandsurfacetemperatureandagronomicfloodingaspatialanalysis AT sudarmanianns assessingmethaneemissionsfromricefieldsinlargeirrigationprojectsusingsatellitederivedlandsurfacetemperatureandagronomicfloodingaspatialanalysis AT geethalakshmivellingiri assessingmethaneemissionsfromricefieldsinlargeirrigationprojectsusingsatellitederivedlandsurfacetemperatureandagronomicfloodingaspatialanalysis AT deiveeganmurugesan assessingmethaneemissionsfromricefieldsinlargeirrigationprojectsusingsatellitederivedlandsurfacetemperatureandagronomicfloodingaspatialanalysis AT ragunathkaliaperumal assessingmethaneemissionsfromricefieldsinlargeirrigationprojectsusingsatellitederivedlandsurfacetemperatureandagronomicfloodingaspatialanalysis AT sivamuruganap assessingmethaneemissionsfromricefieldsinlargeirrigationprojectsusingsatellitederivedlandsurfacetemperatureandagronomicfloodingaspatialanalysis AT shanmugapriyap assessingmethaneemissionsfromricefieldsinlargeirrigationprojectsusingsatellitederivedlandsurfacetemperatureandagronomicfloodingaspatialanalysis |