Abstract
Leaf area index (LAI) is an important parameter to evaluate crop growth and crop yield. The purpose of this study is: using grey relational analysis (GRA) to calculate the typical spectral vegetation index (VI) and LAI correlation, choice the sensitive vegetation index to leaf area index in winter wheat, called GVI; the application of correlation analysis to screen the LAI sensitive vegetation index, called CVI. Then the partial least squares (PLS) method is used to estimate the LAI of GVI and CVI respectively, and the method of determining the optimal VI is determined. The results show that grey relational analysis can improve the estimation accuracy of LAI in winter wheat.
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Acknowledgements
This research was supported by the National Natural Science Foundation of China (41501481), Henan Science and Technology Research Program (172102110055), Colleges and universities in Henan province key scientific research project (15A210028) and Youth Scientific Research foundation of BAAFS (Winter wheat growth variance monitoring through remote sensing in Beijing suburb). We would also like to thank Beijing Agro-technical Station for providing the wheat hyperspectral samples. We are also most grateful to the anonymous reviewers for their helpful comments.
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Guo, W., Dong, P., Qiao, H., Feng, H., Wang, H., Zhang, H. (2020). Filtrate Estimating Factor of Wheat LAI Based on Hyperspectral Data Using Grey Relational Analysis. In: Huang, C., Chan, YW., Yen, N. (eds) Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019). Advances in Intelligent Systems and Computing, vol 1088. Springer, Singapore. https://doi.org/10.1007/978-981-15-1468-5_30
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DOI: https://doi.org/10.1007/978-981-15-1468-5_30
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