Crop Phenology Extraction via Shape Model Fitting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for crop phenology extraction using remote sensing are limited by small scale, low accuracy, and poor versatility, being easily influenced by noise and requiring different measurement standards for various crops.
Innovation Solution
A large-scale crop phenology extraction method based on shape model fitting using satellite remote sensing data, combining ground observation data, and employing a double logistic function to smooth and fit vegetation index time sequences, with optimal scaling to reduce noise and enable macroscopic feature representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the threshold method or moving window method is used for phenology extraction, then the extraction process is simple, but the results are easily influenced by localized fluctuations and noise, reducing measurement precision
Solution Approach 1:
The patent transforms the phenology extraction problem from detecting local morphological feature points to identifying macroscopic shape characteristics of the entire vegetation index curve. By changing the extraction parameters from point-based to shape-based, the method reduces sensitivity to localized noise while maintaining extraction simplicity through standardized shape model fitting procedures.
2Measurement precision
If different measurement standards are applied for different phenological periods and crops, then the extraction accuracy for specific crops may be improved, but the device complexity and difficulty of large-scale application increase
Solution Approach 1:
The patent develops a universal shape model fitting method that can extract phenology for different crops and phenological periods using a single standardized approach. The shape model serves multiple functions across different crop types and growth stages, eliminating the need for crop-specific calibration while maintaining extraction accuracy through the inherent adaptability of shape-based characteristics.
3Measurement precision
If field observation method is used, then the data accuracy is relatively high, but the area coverage is small and it is difficult to guarantee data quality consistency, limiting scalability
Solution Approach 1:
The patent uses satellite remote sensing to create copies of ground-based phenology observations across large areas. By fitting shape models to remotely sensed vegetation index curves, the method replicates the accuracy of field observations at scale, allowing uniform data quality standards to be applied across extensive geographic regions without requiring physical presence for each measurement point.
Data Source
AI summary
Disclosed is a large-scale crop phenology extraction method based on a shape model fitting method. The method comprises: acquiring a multi-year vegetation index time sequence curve in a localized geographic region; performing smooth fitting on the vegetation index time sequence curve by using a dual logistic function fitting means; establishing shape models by using reference curves and reference points of agrometeorological stations; performing shape model fitting by means of transformation; and obtaining a phenological period extraction value of the localized geographic region by means of calculation using the optimal scaling parameter. According to the present invention, macroscopic features of the curve are used, such that the influence of localized fluctuation and noise of the curve can be reduced, and a better extraction precision is obtained; and each phenological period of a crop can be extracted at the same time.