Cohort Analysis for High-Resolution GHG Emission Estimates
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Solution Overview
Problem
Current methods for measuring greenhouse gas (GHG) emissions in agricultural sectors face challenges due to low spatial and temporal resolution, cloud cover, and accuracy issues in remote sensing data, making it difficult to attribute emissions accurately.
Innovation Solution
The method enhances spatial and temporal resolution of GHG emission estimates using cohort analysis techniques, combining non-GHG remote sensing data with contextual information, and employing process-based models and time series learning models to calculate bias corrections and generate updated emission estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If satellite remote sensing data is used to measure GHG emissions globally, then global coverage is achieved, but spatial resolution and temporal resolution are reduced
Solution Approach 1:
The patent segments the agricultural landscape into cohorts based on shared characteristics (crop type, management practices, climate zone). By grouping similar fields together, the system can apply cohort-level estimates to individual fields, effectively enhancing spatial resolution without requiring dense sensor networks. This segmentation allows global coverage to be maintained while providing more granular emission estimates at the field level through the aggregation of satellite data with cohort-specific information.
2Measurement precision
If sensors are deployed to measure GHG emissions in agricultural fields, then measurement precision is improved, but implementation becomes infeasible due to the open field nature of agriculture
Solution Approach 1:
The patent introduces cohorts as an intermediary layer between satellite remote sensing and individual agricultural fields. Instead of deploying physical sensors in each field, the system uses cohorts (groups of fields with similar characteristics) as mediators. Satellite data provides cohort-level estimates, which are then distributed to individual fields within each cohort. This intermediary approach achieves field-level precision without the implementation complexity of deploying sensors across millions of agricultural fields.
3Area of stationary object
If satellite data is used for GHG emission estimation, then global coverage is achieved, but data accuracy is reduced due to cloud cover and other factors
Solution Approach 1:
The patent merges multiple data sources including satellite remote sensing data, cohort-level information, and field-specific characteristics. By combining these diverse data streams, the system compensates for the limitations of any single source (such as cloud cover affecting satellite data). The cohort-level estimates serve as a bridge that integrates global satellite observations with local field conditions, producing more reliable emission estimates that maintain global coverage while improving accuracy through data fusion.
Data Source
AI summary
Methods, systems, and computer program products for enhancing spatial and temporal resolution of greenhouse gas emission estimates for agricultural fields using cohort analysis techniques are provided herein. A computer-implemented method includes obtaining non-greenhouse gas remote sensing data and contextual information pertaining to agricultural fields; determining cohorts among the agricultural fields by deriving agricultural field-specific features from the obtained data and contextual information; computing agricultural field-level time series of greenhouse gas emission estimates for the cohorts by processing the obtained data and contextual information using a process-based model; calculating bias corrections for the cohorts by processing, using a time series learning model, the time series and background greenhouse gas emission estimates; generating resolution-enhanced greenhouse gas emission estimates for the cohorts based on initial greenhouse gas emission estimates derived from greenhouse gas remote sensing data and the calculated bias corrections; and performing automated actions based on the updated greenhouse gas emission estimates.