Satellite Remote Sensing for Field-Level Carbon and Water Quantification
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Solution Overview
Problem
Current techniques for quantifying carbon, water, and nutrient implications and footprints for agricultural crops are limited by the need for costly and sparse fixed flux measurement sites, lacking scalability and accuracy across large regions and at field level.
Innovation Solution
A system and method that collect data through ground sampling, remote sensing, and satellite sensing, developing models to fuse this data and perform life-cycle analysis, enabling scalable quantification of carbon, water, and nutrient outcomes at field level across entire regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed flux measurement sites are used to quantify carbon, water, and nutrient footprints, then measurement precision is improved, but device complexity and cost increase, and scalability to large regions deteriorates
Solution Approach 1:
The patent uses satellite imagery and remote sensing data as copies or proxies for direct flux measurements. Instead of deploying physical flux measurement equipment at every location, the system captures electromagnetic radiation signals from satellites that correlate with carbon, water, and nutrient processes, creating a scalable measurement approach that maintains quantification accuracy without the complexity and cost of fixed flux sites.
Solution Approach 2:
The patent replaces mechanical flux measurement systems (physical sensors and towers) with remote sensing and satellite-based observational systems. This substitution eliminates the need for complex ground-based measurement infrastructure while maintaining the ability to quantify agricultural footprints across large regions through algorithmic processing of satellite data.
2Measurement precision
If fixed flux measurement sites are deployed, then measurement precision is improved, but productivity and scalability across large regions deteriorates due to sparse distribution
Solution Approach 1:
The patent makes the measurement system universal by using satellite imagery that can observe any location on Earth with consistent methodology. The same satellite-based approach can quantify carbon, water, and nutrient footprints across diverse agricultural regions, crop types, and scales—from individual fields to entire continents—without requiring location-specific calibration or deployment of physical infrastructure.
Solution Approach 2:
By using satellite remote sensing signals as proxies for direct measurements, the system can rapidly 'copy' measurement capabilities across thousands of locations simultaneously. This allows the quantification method to scale from local to regional to continental levels without the linear increase in complexity and cost associated with deploying additional fixed flux sites.
3Productivity
If ground sampling and remote sensing are combined with satellite data and model fusion, then productivity and scalability are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent merges multiple data sources (ground sampling, remote sensing, satellite imagery) and modeling approaches into an integrated framework. This combination allows the system to leverage the strengths of each component—ground truth validation, spatial coverage of remote sensing, and temporal consistency of satellite data—while using data fusion algorithms to synthesize comprehensive agricultural footprint assessments at scale.
Solution Approach 2:
The patent introduces data fusion models and algorithms as intermediaries that process and integrate raw data from multiple sources. These intermediary processing layers transform heterogeneous data (ground measurements, satellite signals, remote sensing imagery) into unified quantitative assessments of carbon, water, and nutrient footprints, managing system complexity through structured data integration rather than direct combination of all components.
Data Source
AI summary
A methodology is used to quantify implications and/or footprints of carbon, water, and/or nutrients of a particular crop in a region on a large scale and at field-level. A methodology is used to quantify, calculate, and/or visualize cover crop traits, tillage practices, and/or their outcomes at large scale. A methodology is used to accurately derive, estimate, and/or predict large-scale, long-term, and field-level cover crop adoption and biomass information using remote sensing time series.