Estimation of Chlorophyll Content of Long-Staple Cotton Based on Canopy Spectrum Characteristics

Arkin Ansardin; Sawut Mamat; Jinzhao Li

doi:10.3788/LOP202259.0530001

Journals >Laser & Optoelectronics Progress >Volume 59 >Issue 5 >Page 0530001 > Article

Laser & Optoelectronics Progress
Vol. 59, Issue 5, 0530001 (2022)

Estimation of Chlorophyll Content of Long-Staple Cotton Based on Canopy Spectrum Characteristics

Arkin Ansardin^1、2、3, Sawut Mamat^{1、2、3、*}, and Jinzhao Li^1、2、3

Author Affiliations

¹College of Resources and Environmental Science, Xinjiang University, Urumqi , Xinjiang 830046, China

²Key Laboratory of Oasis Ecology of Ministry of Education, Urumqi , Xinjiang 830046, China

³Key Laboratory for Wisdom City and Environmental Modeling, Urumqi , Xinjiang 830046, China

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DOI: 10.3788/LOP202259.0530001 Cite this Article Set citation alerts

Arkin Ansardin, Sawut Mamat, Jinzhao Li. Estimation of Chlorophyll Content of Long-Staple Cotton Based on Canopy Spectrum Characteristics[J]. Laser & Optoelectronics Progress, 2022, 59(5): 0530001 Copy Citation Text

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Fig. 1. Study area diagram and sampling area distribution

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Fig. 2. Statistics of chlorophyll content in cotton leaves

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Fig. 3. Spectral characteristics of cotton leaves. (a) Spectral reflectance of long-staple cotton with different chlorophyll contents; (b) spectral reflectance of different chlorophyll contents in the visible light range

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Fig. 4. Changes of spectrum after mathematical transformation. (a) R; (b) R′; (c) R_cr

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Fig. 5. Correlation between chlorophyll contents and reflectances. (a) R; (b) R′; (c) R_cr

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Fig. 6. Optimized spectral indices. (a) DSI; (b) NDSI; (c) RSI

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Fig. 7. Estimation R² of SPAD using different SVM kernels. (a) Calibration dataset; (b) validation dataset

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Fig. 8. SVM regression model's fitting analysis result diagrams of measured and predicted values

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Index type	Formula	Reference
VI	S_SR=R₇₅₀/R₇₁₀	［29］
	N_NDVI=（R₇₅₀- R₇₀₅）/ R₇₅₀+R₇₀₅）	［30］
	M_MTCI=（R₇₅₀-R₇₁₀）/（R₇₁₀-R₆₈₀）	［31］
	C_CARI=［（R₇₀₀-R₆₇₀）-0.2×（R₇₀₀-R₅₅₀）］	［32］
OSI	R_RSI（λ₁，λ₂）=R_λ₁/R_λ₂
	D_DSI（λ₁，λ₂）=R_λ₁-R_λ₂	［33］
	N_NDSI（λ₁，λ₂）=（R_λ₁-R_λ₂）/（R_λ₁+R_λ₂）

Table 1. Index calculation formulas

Algorithm	Characteristic band /nm	Correlation coefficient	Mean
R	712， 713， 714， 715， 716，	‒0.43， 0.43， ‒0.43， ‒0.43， ‒0.43，	0.43
R	717， 718， 719， 720， 721	‒0.43， ‒0.43， ‒0.43， ‒0.43， ‒0.43	0.43
R′	513， 524， 527， 572， 589，	‒0.53， ‒0.56， ‒0.58， 0.55， 0.53，	0.54
R′	691， 692， 697， 698， 699	‒0.53， ‒0.51， ‒0.54， ‒0.54， ‒0.53	0.54
R_cr	761， 762， 763， 764， 817，	‒0.50， ‒0.49， ‒0.46， ‒0.46， ‒0.47，	0.47
R_cr	818， 823， 829， 830， 831	‒0.47， ‒0.46， ‒0.48， ‒0.47， ‒0.46	0.47

Table 2. Cotton spectral characteristic bands and correlation coefficients

Vegetation index	R	R′	R_cr
SR	0.35^**	0.41^**	0.35^**
NDVI	0.27^*	0.46^**	0.28^*
MTCI	0.49^**	0.46^**	0.49^**
CARI	-0.52^**	-0.51^**	-0.42^**

Table 3. Correlation coefficients between vegetation index and chlorophyll content

Optimized spectral index	Band combination	Correlation coefficient	Mean
RSI	942 945， 941 947，950 956， 1028 1051，928 1051	0.48， 0.43，0.42， 0.39，0.39	0.422
DSI	716 719， 693 936，565 738， 610 740，636 741	0.77， 0.76，0.75， 0.74，0.73	0.75
NDSI	941 952， 941 947，956 950， 928 1051，1028 1051	0.46， 0.43，0.42， 0.39，0.38	0.418

Table 4. Correlation between chlorophyll contents and optimal spectral indices

Model	Item	Calibration			Validation
Model	Item	R²	R_RMSE	R_RE	R²	R_RMSE	R_RE
Algorithm	R	0.41	2.88	1.26	0.29	1.53	0.43
	R′	0.72	1.99	0.72	0.84	0.72	0.14
	R_cr	0.39	2.91	1.1	0.83	0.69	0.16
VI	R₄	0.51	2.61	1.1	0.64	1.11	0.31
	R′₄	0.49	2.74	1.18	0.43	1.37	0.39
	R_cr4	0.38	2.96	1.36	0.50	1.29	0.35
OSI	D_DSI	0.49	3.02	1.34	0.23	1.6	0.52
	N_NDSI	0.45	2.82	1.09	0.58	1.19	0.25
	R_RSI	0.53	2.62	0.99	0.58	1.18	0.24

Table 5. Comparison of modeling results of SVM regression model RBF kernel type

Arkin Ansardin, Sawut Mamat, Jinzhao Li. Estimation of Chlorophyll Content of Long-Staple Cotton Based on Canopy Spectrum Characteristics[J]. Laser & Optoelectronics Progress, 2022, 59(5): 0530001

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