Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables
Concerns about methane (CH4) emissions from rice, a staple sustaining over 3.5 billion people globally, are heightened due to its status as the second-largest contributor to greenhouse gases, driving climate change. Accurate quantification of CH4 emissions from rice fields is crucial for understandi...
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2024
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/173771 |
| _version_ | 1855516990565777408 |
|---|---|
| author | Velez, Andres Felipe Alvarez, Cesar Ivan Navarro, Fabian Guzman, Diego Bohorquez, Martha Patricia Selvaraj, Michael Gomez Ishitani, Manabu |
| author_browse | Alvarez, Cesar Ivan Bohorquez, Martha Patricia Guzman, Diego Ishitani, Manabu Navarro, Fabian Selvaraj, Michael Gomez Velez, Andres Felipe |
| author_facet | Velez, Andres Felipe Alvarez, Cesar Ivan Navarro, Fabian Guzman, Diego Bohorquez, Martha Patricia Selvaraj, Michael Gomez Ishitani, Manabu |
| author_sort | Velez, Andres Felipe |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Concerns about methane (CH4) emissions from rice, a staple sustaining over 3.5 billion people globally, are heightened due to its status as the second-largest contributor to greenhouse gases, driving climate change. Accurate quantification of CH4 emissions from rice fields is crucial for understanding gas concentrations. Leveraging technological advancements, we present a groundbreaking solution that integrates machine learning and remote sensing data, challenging traditional closed chamber methods. To achieve this, our methodology involves extensive data collection using drones equipped with a Micasense Altum camera and ground sensors, effectively reducing reliance on labor-intensive and costly field sampling. In this experimental project, our research delves into the intricate relationship between environmental variables, such as soil conditions and weather patterns, and CH4 emissions. We achieved remarkable results by utilizing unmanned aerial vehicles (UAV) and evaluating over 20 regression models, emphasizing an R2 value of 0.98 and 0.95 for the training and testing data, respectively. This outcome designates the random forest regressor as the most suitable model with superior predictive capabilities. Notably, phosphorus, GRVI median, and cumulative soil and water temperature emerged as the model’s fittest variables for predicting these values. Our findings underscore an innovative, cost-effective, and efficient alternative for quantifying CH4 emissions, marking a significant advancement in the technology-driven approach to evaluating rice growth parameters and vegetation indices, providing valuable insights for advancing gas emissions studies in rice paddies. |
| format | Journal Article |
| id | CGSpace173771 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1737712025-11-11T17:42:38Z Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables Velez, Andres Felipe Alvarez, Cesar Ivan Navarro, Fabian Guzman, Diego Bohorquez, Martha Patricia Selvaraj, Michael Gomez Ishitani, Manabu machine learning remote sensing emissions trading rice fields-paddy fields Concerns about methane (CH4) emissions from rice, a staple sustaining over 3.5 billion people globally, are heightened due to its status as the second-largest contributor to greenhouse gases, driving climate change. Accurate quantification of CH4 emissions from rice fields is crucial for understanding gas concentrations. Leveraging technological advancements, we present a groundbreaking solution that integrates machine learning and remote sensing data, challenging traditional closed chamber methods. To achieve this, our methodology involves extensive data collection using drones equipped with a Micasense Altum camera and ground sensors, effectively reducing reliance on labor-intensive and costly field sampling. In this experimental project, our research delves into the intricate relationship between environmental variables, such as soil conditions and weather patterns, and CH4 emissions. We achieved remarkable results by utilizing unmanned aerial vehicles (UAV) and evaluating over 20 regression models, emphasizing an R2 value of 0.98 and 0.95 for the training and testing data, respectively. This outcome designates the random forest regressor as the most suitable model with superior predictive capabilities. Notably, phosphorus, GRVI median, and cumulative soil and water temperature emerged as the model’s fittest variables for predicting these values. Our findings underscore an innovative, cost-effective, and efficient alternative for quantifying CH4 emissions, marking a significant advancement in the technology-driven approach to evaluating rice growth parameters and vegetation indices, providing valuable insights for advancing gas emissions studies in rice paddies. 2024-06 2025-03-21T07:23:20Z 2025-03-21T07:23:20Z Journal Article https://hdl.handle.net/10568/173771 en Open Access application/pdf Springer Velez, A.F.; Alvarez, C.I.; Navarro, F..; Guzman, D.; Bohorquez, M.P.; Selvaraj, M.G.; Ishitani, M. (2024) Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables. Environmental Monitoring and Assessment 196(6): 574. ISSN: 0167-6369 |
| spellingShingle | machine learning remote sensing emissions trading rice fields-paddy fields Velez, Andres Felipe Alvarez, Cesar Ivan Navarro, Fabian Guzman, Diego Bohorquez, Martha Patricia Selvaraj, Michael Gomez Ishitani, Manabu Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables |
| title | Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables |
| title_full | Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables |
| title_fullStr | Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables |
| title_full_unstemmed | Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables |
| title_short | Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables |
| title_sort | assessing methane emissions from paddy fields through environmental and uav remote sensing variables |
| topic | machine learning remote sensing emissions trading rice fields-paddy fields |
| url | https://hdl.handle.net/10568/173771 |
| work_keys_str_mv | AT velezandresfelipe assessingmethaneemissionsfrompaddyfieldsthroughenvironmentalanduavremotesensingvariables AT alvarezcesarivan assessingmethaneemissionsfrompaddyfieldsthroughenvironmentalanduavremotesensingvariables AT navarrofabian assessingmethaneemissionsfrompaddyfieldsthroughenvironmentalanduavremotesensingvariables AT guzmandiego assessingmethaneemissionsfrompaddyfieldsthroughenvironmentalanduavremotesensingvariables AT bohorquezmarthapatricia assessingmethaneemissionsfrompaddyfieldsthroughenvironmentalanduavremotesensingvariables AT selvarajmichaelgomez assessingmethaneemissionsfrompaddyfieldsthroughenvironmentalanduavremotesensingvariables AT ishitanimanabu assessingmethaneemissionsfrompaddyfieldsthroughenvironmentalanduavremotesensingvariables |