Virtual Satellite Blending Algorithm for Vegetation Monitoring
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Solution Overview
Problem
The challenge in using satellite data for vegetation analysis is the mismatch in spatial and temporal resolutions across different satellites, along with issues like atmospheric interference and cloud cover, which complicates deriving integrated insights. Additionally, drones with near-infrared cameras require technical expertise and significant resources, limiting their practicality.
Innovation Solution
A virtual satellite system with a hardware processor and blending algorithm that reprojects and interpolates data from multiple satellites to achieve high spatio-temporal resolution vegetation indices, such as HD-NDVI, by identifying the satellite with the minimum spatial resolution, removing cloud data, and interpolating to a desired temporal resolution, while determining and smoothing pairwise biases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If satellite data from multiple satellites with different spatial and temporal resolutions is used, then comprehensive vegetation monitoring is achieved, but data integration complexity increases due to resolution mismatch
Solution Approach 1:
The patent merges data from multiple satellites (Sentinel-2, Landsat 8, MODIS) with different spatial and temporal resolutions into a unified vegetation monitoring system. The blending algorithm combines these heterogeneous data sources to produce integrated vegetation indices that leverage the strengths of each satellite while compensating for their individual limitations.
Solution Approach 2:
The patent changes the resolution parameters of satellite data through re-projection and interpolation operations. Data from satellites with coarser spatial resolution are re-projected to match the finest spatial resolution, while temporal resolution is enhanced through interpolation to achieve daily vegetation monitoring at high spatial detail.
2Area of stationary object
If satellite data is used for vegetation analysis, then large area monitoring is achieved, but data quality deteriorates due to atmospheric interference and cloud cover
Solution Approach 1:
The patent introduces cloud detection and correction algorithms as intermediary processing steps between raw satellite data and final vegetation indices. These algorithms identify and correct atmospheric interference and cloud contamination, acting as a mediator that preserves the integrity of vegetation monitoring data despite adverse atmospheric conditions.
Solution Approach 2:
The patent creates a virtual satellite system that synthesizes ideal vegetation monitoring data by blending observations from multiple actual satellites. This virtual copy compensates for deficiencies in individual satellite observations, including cloud cover and atmospheric interference, by leveraging complementary data from other satellites in the constellation.
3Measurement precision
If drones with near-infrared cameras are used for crop monitoring, then high resolution data is achieved, but operational complexity and cost increase
Solution Approach 1:
The patent creates a virtual satellite system that synthesizes high-resolution vegetation monitoring data by blending observations from multiple actual satellites. This virtual copy compensates for deficiencies in individual satellite observations, including cloud cover and atmospheric interference, by leveraging complementary data from other satellites in the constellation.
Solution Approach 2:
The patent makes the satellite-based virtual satellite system universally applicable for crop monitoring without requiring specialized drone operations. The system provides drone-like high-resolution vegetation monitoring capabilities through multi-functional satellite data blending, eliminating the need for specialized near-infrared camera equipment and expert operation while maintaining large-area coverage capability.
Data Source
AI summary
A virtual satellite system may receive, re-project to a spatial resolution and interpolate to a desired temporal resolution, georeferenced data representing an image of a geographic region from a plurality of different satellites. Bias in the georeferenced data between the plurality of satellites is determined and based on which satellite's image data contains an identified minimum spatial resolution, vegetation index data may be set to one of the satellite's data, which may or may not be adjusted. A target image may be generated based on the set vegetation index data.