Hyperspectral inversion of nitrogen content in phyllostachys pubescens based on partial least squares regression model

Ming Ming Wang; Yun Zhi Chen; Kai Li

doi:10.1117/12.2625587

9 December 2021 Hyperspectral inversion of nitrogen content in phyllostachys pubescens based on partial least squares regression model

Ming Ming Wang, Yun Zhi Chen, Kai Li

Author Affiliations +

Proceedings Volume 12129, International Conference on Environmental Remote Sensing and Big Data (ERSBD 2021); 121290M (2021) https://doi.org/10.1117/12.2625587
Event: 2021 International Conference on Environmental Remote Sensing and Big Data, 2021, Wuhan, China

Abstract

High spectral analysis technology has the advantages of fast, accurate and nondestructive, and is widely used in the field of leaf nitrogen analysis. In order to explore the optimal inversion model for monitoring the nitrogen content of Phyllostachys pubescens. The collected Phyllostachys pubescens sample data were divided into modeling set and validation set based on SPXY (Sample Set Partitioning based on Joint X-Y Distance Sampling) method and Random method, respectively. The SPA (Successive Projections Algorithm) was used to extract the characteristic wavelengths of the original and transform spectra. And the vegetation index and red edge parameters with high correlation with the nitrogen content of Phyllostachys edulis were selected. Then the PLSR estimation model based on the nitrogen content of Phyllostachys edulis was established. The results showed that compared with the random sample partition method, the SPXY sample partition method increased the estimation accuracy R² by 0.13 on average, reduced RMSE by 0.50 on average, and increased RPD by 0.58. The PLSR estimation model of CR-FDR established had the highest fitting accuracy of N content in Phyllostachys pubescens, R² was 0.85, RMSE was 1.32, RPD was 2.42. The inversion model combined with UAV hyperspectral monitoring data can better reflect the spatial difference of Phyllostachys pubescens nitrogen content.

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Ming Ming Wang, Yun Zhi Chen, and Kai Li "Hyperspectral inversion of nitrogen content in phyllostachys pubescens based on partial least squares regression model", Proc. SPIE 12129, International Conference on Environmental Remote Sensing and Big Data (ERSBD 2021), 121290M (9 December 2021); https://doi.org/10.1117/12.2625587

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KEYWORDS

Nitrogen

Data modeling

Vegetation

Statistical modeling

Unmanned aerial vehicles

Statistical analysis

Remote sensing

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