气候变化领域集成服务门户(CCPortal): Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables

CCPortal

DOI	10.1007/s10661-024-12725-9
	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
发表日期	2024
ISSN	0167-6369
EISSN	1573-2959
起始页码	196
结束页码	6
卷号	196 期号:6
英文摘要	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.
英文关键词	Methane emissions; Paddy field; GHG; Remote sensing; UAV; Machine learning
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:001230194200002
来源期刊	ENVIRONMENTAL MONITORING AND ASSESSMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/297010
作者单位	Universidad Politecnica Salesiana; Universidad Nacional de Colombia
推荐引用方式 GB/T 7714	Velez, Andres Felipe,Alvarez, Cesar Ivan,Navarro, Fabian,et al. Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables[J],2024,196(6).
APA	Velez, Andres Felipe.,Alvarez, Cesar Ivan.,Navarro, Fabian.,Guzman, Diego.,Bohorquez, Martha Patricia.,...&Ishitani, Manabu.(2024).Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables.ENVIRONMENTAL MONITORING AND ASSESSMENT,196(6).
MLA	Velez, Andres Felipe,et al."Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables".ENVIRONMENTAL MONITORING AND ASSESSMENT 196.6(2024).