Cropping System Stability Mapping With Remote Sensing Time Series
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Solution Overview
Problem
Existing methods fail to effectively map the temporal and spatial stability and sustainability of cropping systems, which are crucial for optimizing crop management and reducing environmental impact across large land areas.
Innovation Solution
A method utilizing remote sensing imagery and crop models to determine optical vegetative indices (OVIs) and variability parameters, enabling the creation of stability and sustainability maps that guide crop management decisions based on spatial and temporal data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If remote sensing imagery and crop models are used to map temporal and spatial stability and sustainability, then measurement precision and information completeness are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the large-scale cropping system into multiple small-scale image subunits, allowing independent analysis of each subunit's temporal and spatial stability. This segmentation enables precise mapping of stability patterns across different field zones while managing computational complexity through modular processing of individual subunits rather than treating the entire field as a single unit.
Solution Approach 2:
The patent adds temporal dimension by analyzing multiple time series elements (historical and current growing seasons) to map stability over time. This multi-dimensional approach transforms static field measurements into dynamic stability maps that capture temporal variations, improving measurement precision by considering both spatial and temporal dimensions of crop system behavior.
2Productivity
If detailed stability maps are created for optimizing crop management, then productivity and resource use efficiency are improved, but loss of time and computational resources increase
Solution Approach 1:
The patent performs preliminary analysis by segmenting the field into small-scale subunits and establishing baseline stability patterns using historical time series data before the current growing season. This preliminary mapping of temporal and spatial stability allows farmers to proactively optimize crop management decisions at the beginning of the season, avoiding time-consuming analysis during critical growth periods.
Solution Approach 2:
The patent uses remote sensing imagery to create optical vegetative index maps that serve as proxies for actual crop conditions and stability patterns. These optical copies allow rapid assessment of field stability without requiring extensive ground-based measurements, significantly reducing the time needed to generate actionable stability maps while maintaining measurement precision.
3Measurement precision
If optical vegetative indices are determined for multiple small-scale image subunits, then measurement precision and spatial resolution are improved, but quantity of data and processing requirements increase
Solution Approach 1:
The patent extracts only the essential optical vegetative index information from remote sensing imagery for each small-scale image subunit, focusing on key temporal and spatial stability parameters rather than processing all available spectral and spatial data. This selective extraction reduces data volume while maintaining the precision needed for stability mapping by concentrating on the most relevant vegetative indicators.
Solution Approach 2:
The patent merges multiple time series elements (historical and current season data) into integrated stability maps that synthesize temporal patterns. By combining historical OVI data with current season measurements, the system creates comprehensive stability assessments that leverage accumulated knowledge, reducing the need to process each season's data independently and thereby managing data volume more efficiently.
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
Provides detailed maps for optimizing crop management and resource use, improving productivity and reducing environmental impact by identifying stable and unstable areas within cropping systems.
Implementation Method 1
remote sensing imagery over multiple time series elements (such as for past growing seasons) is used to characterize small-scale field stability and variability
Data Source
AI summary
Crop modeling methods can be used for mapping temporal and spatial stability and sustainability of a cropping system. In some methods, remote sensing imagery over multiple time series elements (such as for past growing seasons) is used to characterize small-scale field stability and variability relative to larger surrounding land areas. In some methods, remote sensing imagery over multiple time series elements (such as for the growing season) is used to characterize small-scale field stability and variability relative to larger surrounding land areas. In some methods, a crop model is used to determine dependent cropping system parameters related to agricultural sustainability, which can be used to characterize small-scale field sustainability scores for such parameters relative to larger surrounding land areas. Such stability and sustainability maps can inform crop management activities for fields in the larger land areas and on the smaller scales, for example using crop models to determine such crop management activities to improve crop productivity, improve economic productivity, and/or reduce adverse environmental impact for the field as a whole and/or sub-regions thereof.