Estimating Methane Emissions
from Paddy Cultivation Using Remote Sensing and Machine Learning
This case study explores a comprehensive approach to estimating methane emissions from paddy cultivation across multiple scales, including farm, village, tehsil, and district levels. By leveraging remote sensing data, in-situ observations, and machine learning techniques, the study addresses the critical need to quantify methane emissions, a potent greenhouse gas, for sustainable agricultural practices and effective carbon management.
Methodology
Data Collection
- In-Situ Measurements: Methane flux is measured using fixed and mobile instruments installed across farms, providing ground-truth data for model training and validation.
- Satellite Data:
- Sentinel-5P: Provides methane concentration data.
- Sentinel-2: Delivers vegetation indices and crop monitoring insights.
- Agri Mask and LULC Layers: Aid in delineating paddy cultivation areas and refining spatial classifications.
Machine Learning Techniques
- Predictive models integrate diverse datasets to estimate methane emissions accurately. Key algorithms include:
- Support Vector Machines (SVM)
- Decision Trees
- Gaussian Process Regression (GPR)
- These models handle heterogeneous data and capture nonlinear relationships between variables such as:
- Soil properties
- Temperature
- Vegetation indices (NDVI, EVI)
- Water levels
Spatio-Temporal Analysis
- Kriging and Interpolation: Geospatial techniques fill data gaps, enabling high-resolution methane emission mapping.
- Cross-Validation: Ensures model accuracy and reliability.
- Feature Importance Analysis: Identifies key drivers of methane emissions.
Ensemble Learning
- Advanced ensemble techniques, such as stacking models, combine predictions from multiple algorithms to enhance accuracy and robustness.
Outputs and Insights
The study delivers methane emission estimates across farm, village, tehsil, and district levels, offering granular insights into emission patterns. Key outputs include:
- Methane Emission Maps: High-resolution spatial representations of emission hotspots.
- Carbon Content and Sequestration Potential: Calculations of net carbon content and the sequestration potential of agricultural practices.
- Carbon Footprint Analysis: Comprehensive evaluation of the carbon stock and greenhouse gas (GHG) emissions associated with paddy cultivation.
Policy and Practical Implications
The findings provide actionable insights for both farm-level decision-making and large-scale policy planning. Key applications include:
- Identification of Methane Hotspots: Supports targeted mitigation strategies.
- Development of Sustainable Practices: Promotes low-emission agricultural techniques.
- Climate Action Planning: Integrates results into national and regional strategies for reducing GHG emissions and enhancing carbon management.
Conclusion
By integrating remote sensing, in-situ measurements, and machine learning, this study offers a scalable and accurate methodology for estimating methane emissions from paddy cultivation. The approach identifies emission patterns and supports the development of mitigation strategies, contributing to sustainable agriculture and climate change mitigation efforts.
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