Wavelengths Optimization and Chlorophyll Content Detection Based on PROSPECT Model

Jun-yi ZHANG; De-hua GAO; Di SONG; Lang QIAO; Hong SUN; Min-zan LI; Li LI

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

Journals >Spectroscopy and Spectral Analysis >Volume 42 >Issue 5 >Page 1514 > Article

Spectroscopy and Spectral Analysis
Vol. 42, Issue 5, 1514 (2022)

Wavelengths Optimization and Chlorophyll Content Detection Based on PROSPECT Model

Jun-yi ZHANG^*, De-hua GAO^1;, Di SONG^1;, Lang QIAO^1;..., Hong SUN^1;, Min-zan LI^{1; *;} and Li LI^1;|Show fewer author(s)

Author Affiliations

1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China

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DOI: 10.3964/j.issn.1000-0593(2022)05-1514-08 Cite this Article

Jun-yi ZHANG, De-hua GAO, Di SONG, Lang QIAO, Hong SUN, Min-zan LI, Li LI. Wavelengths Optimization and Chlorophyll Content Detection Based on PROSPECT Model[J]. Spectroscopy and Spectral Analysis, 2022, 42(5): 1514 Copy Citation Text

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Sensitivity analysis of chlorophyll content using PROSPECT model(a): Reflectivity curves for different chlorophyll contents; (b): Sensitivity analysis of chlorophyll content

Fig. 1. Sensitivity analysis of chlorophyll content using PROSPECT model
(a): Reflectivity curves for different chlorophyll contents; (b): Sensitivity analysis of chlorophyll content

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The measured reflectivity spectra(a): Measured in 2019; (b): Measured in 2020; (c): Wavelet denoised spectra in 2019;(d): Wavelet denoised spectra in 2020; (e): SNV processed spectra in 2019; (f): SNV processed spectra in 2020

Fig. 2. The measured reflectivity spectra
(a): Measured in 2019; (b): Measured in 2020; (c): Wavelet denoised spectra in 2019;(d): Wavelet denoised spectra in 2020; (e): SNV processed spectra in 2019; (f): SNV processed spectra in 2020

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Fig. 3. Sensitive band filtering results
(a): SEN-BAND, SPA-BAND; (b): BP-BAND

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Fig. 4. Modeling and verification results with SPA-BAND sensitive bands in 2019 and 2020
(a): In 2019; (b): In 2020

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Fig. 5. Dynamic chlorophyll distribution results based on PLSR model
(a): Field fertilization distribution; (b): Detected distribution in 2019; (c): Detected distribution in 2020

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参数	符号	单位	光谱模拟参数范围	步长
结构参数	N		(1, 5)	1
叶绿素含量	c_ab	μg·cm^-2	(10, 80)	1
胡萝卜素含量	c_ar	μg·cm^-2	4.37
花青素含量	c_anth	μg·cm^-2	(1, 2)	0.2
棕色素	c_brown		0
等效水含量	c_w	g·cm^-2	0.015
干物质含量	c_m	g·cm^-2	(0.005, 0.025)	0.005

Table 1. Input parameters of PROSPECT model

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特征	敏感波段/nm
SEN-BAND	548~610, 694~706
BP-BAND	514~532, 533~551, 590~608, 647~665, 723~741
SPA-BAND	400, 500, 517, 536, 554, 591, 676, 686, 711, 729

Table 2. Statistics of sensitive band distribution

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数据集	特征波长	变量个数	$R_{c}^{2}$	RMSEC	$R_{v}^{2}$	RMSEV
2019年数据集	SEN-BAND	76	0.806 2	2.981 6	0.727 0	3.643 5
	BP-BANG	95	0.812 8	2.930 0	0.755 5	3.508 0
	SPA-BAND	10	0.815 6	2.908 6	0.799 5	2.997 7
2020年数据集	SEN-BAND	76	0.860 2	1.621 4	0.792 0	1.785 6
	BP-BANG	95	0.962 5	0.839 7	0.803 4	2.014 6
	SPA-BAND	10	0.949 2	0.976 8	0.910 2	1.562 9

Table 3. Detection results of PLS model of the chlorophyll content

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