Multi-Pass Interferometric SAR for Crop Growth Estimation
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Solution Overview
Problem
Measuring crop growth using satellite-based radar is challenging due to sensitivity to rain, wind, and crop growth exceeding radar wavelength between satellite passes, leading to inaccurate measurements.
Innovation Solution
A method utilizing synthetic aperture radar (SAR) data from multiple satellite passes, processed into coherence, interferometric, and polarimetric data, is combined with a trained crop growth estimation model to generate accurate crop growth predictions, accounting for noise and artifacts using coherence values and neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If satellite-based radar is used to measure crop growth, then global coverage and efficiency are improved, but measurement accuracy deteriorates due to sensitivity to rain, wind, and crop growth exceeding radar wavelength
Solution Approach 1:
The patent combines multiple SAR data subsets from different satellite passes to form a composite dataset. By merging multiple observations taken at different times, the system achieves both global coverage efficiency and improved measurement accuracy through data fusion and coherence analysis
Solution Approach 2:
The patent performs preliminary processing of SAR data into coherence data and interferometric data before final crop growth estimation. This preliminary action of transforming raw SAR data into processed features removes noise and artifacts early in the pipeline, improving subsequent measurement accuracy
2Measurement precision
If multiple satellite passes are used to improve measurement accuracy, then crop growth estimation precision is improved, but data processing complexity increases
Solution Approach 1:
The patent extracts coherence data and interferometric data from multiple SAR subsets, separating the essential measurement information from noise and artifacts. This extraction process isolates the relevant signals needed for accurate crop growth estimation while discarding problematic data elements
Solution Approach 2:
The patent introduces coherence analysis and interferometric processing as intermediary steps between raw SAR data and final crop growth estimates. These intermediary processes act as filters that simplify the relationship between multiple satellite passes and the final measurement, managing complexity through structured transformation
3Reliability
If SAR data is processed into coherence and interferometric data, then noise and artifacts are reduced, but processing time and computational requirements increase
Solution Approach 1:
The patent performs coherence calculation and interferometric processing as preliminary steps to clean the SAR data before final analysis. By addressing noise and artifacts early through these transformations, the system establishes high-quality input data for subsequent crop growth estimation, reducing the need for iterative corrections
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise crop growth measurements at centimeter-level accuracy by improving data resolution and handling noise, providing continuous and granular estimates of crop growth over time.
Implementation Method 1
Satellite-based radar enables measurements, such as seismological ground shifts, to be collected around the globe in a highly efficient manner
Implementation Method 2
measuring the growth of crops in fields using existing technology is difficult due to sensitivity to rain, wind, and/or crop growth that exceeds radar wavelength between satellite passes
Data Source
AI summary
A computerized method estimates crop growth in a geographic area using satellite-collected synthetic aperture radar (SAR) data. SAR data of the geographic area is obtained from a plurality of satellite passes by one or more satellites. The obtained SAR data is processed into coherence data and interferometric data. The processed data is associated with a comparison between a first SAR data subset from a first satellite pass of the plurality of satellites passes and a second SAR data subset from a second satellite pass of the plurality of satellite passes. The processed data is provided to a trained crop growth estimation model and a crop growth prediction associated with the geographic area is generated using the trained crop growth estimation model. In some examples, the obtained SAR data is processed into additional data types, such as amplitude data and/or polarimetric SAR data.