研究目的
To use imaging spectrometer to obtain rice canopy images, and spectrally purify these images to obtain rice leaf hyperspectral images, and then retrieve foliar chlorophyll content in order to obtain specific spectra using this method.
研究成果
The study concluded that spectral purification of rice canopy hyperspectral imagery can effectively improve the retrieval accuracy of foliar chlorophyll content. The proposed method lays a good foundation for the development of built-in algorithms for in-situ sensors and UAV remote sensing platforms.
研究不足
The study does not explicitly mention limitations, but potential areas for optimization could include the accuracy of the spectral purification procedure and the generalizability of the method to other crops or conditions.
1：Experimental Design and Method Selection:
The study used a Cubert S185 hyperspectral imager to collect hyperspectral images of rice canopy and a SPAD meter to measure chlorophyll content. A spectral purification procedure was established to refine the spectra for retrieving foliar chlorophyll content.
2：Sample Selection and Data Sources:
58 hyperspectral images of rice canopy were obtained at China National Rice Research Institute.
3：List of Experimental Equipment and Materials:
Cubert S185 Imaging Spectrometer, SPAD instrument.
4：Experimental Procedures and Operational Workflow:
Background of rice images was removed by a decision tree method, then rice spikes were removed by an object-oriented classification, leaving the portion of rice leaves. Vegetation indices were extracted from the refined leaf spectra.
5：Data Analysis Methods:
Partial least-squares regression model was used for analysis, and cross-validation method was used to verify the regression model.
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