PROSPECT模型的特征波长优化与作物叶绿素含量检测

doi:10.3964/j.issn.1000-0593(2022)05-1514-08

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Abstract Chlorophyll is an important biochemical parameter involved in crop growth. Accurate detection of chlorophyll in real-time has great significance for the precision management of farmland. The PROSPECT model can simulate the reflectivity and transmissibility of leaf at 400~2 500 nm based on leaf’s input structural and biochemical parameters. This study used the PROSPECT model to generate 10 650 reflectivity curves of maize leaf under different input parameters. The sensitivity of the spectral reflectance curve to the chlorophyll content parameter was analyzed when other parameters remained unchanged. The result shows that the chlorophyll content only affects the spectral reflectance curve in the range of 400~780 nm. According to the sensitivity analysis result, 76 wavelengths in 548~610 and 694~706 nm were selected as the characteristic wavelengths of chlorophyll content, which were recorded as SEN-BAND. Based on Backward Interval PLS (Bi-PLS), 5 intervals of 91 characteristic wavelengths were selected, recorded as BP-BAND. Based on the Successive Projections Algorithm (SPA), 10 characteristic wavelengths were selected in chlorophyll-influenced area in 400~780 nm, recorded as SPA-BAND. The PLS detection model of chlorophyll content based on the three characteristic wavelengths was constructed with measured field data in 2019 and 2020. The results show that the -SPA-BAND model has the best results in both 2019 and 2020 datasets. In the 2019 dataset, the coefficient of determination (R²_c) of the modeling set is 0.815 6, the root mean square error (RMSEC) of the modeling set is 2.908 6, the coefficient of determination (R²_v) of the validation set is 0.799 5, and the root means square error (RMSEV) of the validation set is 2.997 7. In the 2020 database, the coefficient of determination (R²_c) of the modeling set is 0.949 2, the root mean square error (RMSEC) of the modeling set is 0.976 8, the coefficient of determination (R²_v) of the validation set was 0.910 2, and the root means square error (RMSEV) of the validation set was 1.562 9. Therefore, the characteristic wavelength of chlorophyll content can be selected under the influence of multiple factors by constructing spectral reflectance curves with multi-parameter input based on the PROSPECT model and the characteristic wavelengths of chlorophyll content can be verified in multi-year data.

Key words：PROSPECT model; Chlorophyll; Wavelength selection; SPA; Bi-PLS; PLSR

Received: 2021-03-25 Accepted: 2021-06-02

ZTFLH:

S127

Corresponding Authors: LI Min-zan E-mail: limz@cau.edu.cn

Cite this article:

ZHANG Jun-yi,GAO De-hua,SONG Di, et al. Wavelengths Optimization and Chlorophyll Content Detection Based on PROSPECT Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1514-1521.

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http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2022)05-1514-08 OR http://www.gpxygpfx.com/EN/Y2022/V42/I05/1514

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