Estimation Model of Chlorophyll-a Concentration Based on Continuous Wavelet Coefficient

Yongshi Peng; Shuisen Chen; Jinyue Chen; Jing Zhao; Chongyang Wang; Yunlan Guan

doi:10.3788/LOP202158.0828002

Journals >Laser & Optoelectronics Progress >Volume 58 >Issue 8 >Page 0828002 > Article

Laser & Optoelectronics Progress
Vol. 58, Issue 8, 0828002 (2021)

Estimation Model of Chlorophyll-a Concentration Based on Continuous Wavelet Coefficient

Yongshi Peng^1、2、3, Shuisen Chen^3、**, Jinyue Chen³, Jing Zhao³, Chongyang Wang³, and Yunlan Guan^1、2、*

Author Affiliations

¹Faculty of Geomatics, East China University of Technology, Nanchang, Jiangxi 330013, China

²Key Laboratory of Watershed Ecology and Geographical Environment Monitoring National Administration of Surveying, Mapping and Geoinformation,Nanchang, Jiangxi 330013, China

³Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangzhou Institute of Geography, Guangzhou, Guangdong 510070, China

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DOI: 10.3788/LOP202158.0828002 Cite this Article Set citation alerts

Yongshi Peng, Shuisen Chen, Jinyue Chen, Jing Zhao, Chongyang Wang, Yunlan Guan. Estimation Model of Chlorophyll-a Concentration Based on Continuous Wavelet Coefficient[J]. Laser & Optoelectronics Progress, 2021, 58(8): 0828002 Copy Citation Text

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Fig. 1. Sampling areas and sampling stations

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Fig. 2. Statistical characteristics of chlorophyll-a in different datasets

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Fig. 3. Analysis of water reflectance curve and its single-band correlation

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Fig. 4. Diagram of RSI and NDCI correlation coefficient matrix. (a) RSI; (b) NDCI

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Fig. 5. Correlation coefficient diagram of db4 wavelet coefficient and chlorophyll-a concentration

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Fig. 6. Predicted chlorophyll-a concentration based on sym6 wavelet transform and measured value

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Mother wavelet	Research object	Reference
rbio3.3bior3.3sym7db1	Maize (chlorophyll, carotenoids)	Wang Z L, et al. (2020)^[20]
db5haarmexhmorl	Winter wheat (chlorophyll)	He R Y, et al. (2018)^[21]
mexh	Zizania caduciflora (chlorophyll)	Yu Z X, et al. (2018)^[22]
db4	Broadleaf shrub,small tree (chlorophyll)	Fang S H, et al. (2015)^[23]
sym6	Maize (chlorophyll)	Liu H J, et al. (2018)^[24]

Table 1. Ten selected mother wavelet bases of continuous wavelet transform and their application fields

Sample set	Number /(μg·L^-1)	Minimum /(μg·L^-1)	Median /(μg·L^-1)	Maximum /(μg·L^-1)	Mean /(μg·L^-1)	SD /(μg·L^-1)	CV /(μg·L^-1)
Calibration dataset	48	0.434	13.100	50.338	18.510	14.500	0.783
Validation dataset	21	0.482	9.400	44.873	17.706	16.771	0.947
All dataset	69	0.434	12.700	50.338	18.265	15.106	0.827

Table 2. Statistical characteristic of chlorophyll-a concentration at sampling points

Spectral index	Model	Variable	r	R²	RMSE/ (μg·L^-1)
Reflectance	R₁	R₁=712 nm	0.328	0.153	15.063
RSI	$\frac{R_{1}}{R_{2}}$	R₁=654 nmR₂=644 nm	0.721	0.644	9.760
NDCI	$\frac{R_{1} - R_{2}}{R_{1} + R_{2}}$	R₁=658 nmR₂=632 nm	0.721	0.669	9.414
Three-band	( $R_{1}^{- 1}$ - $R_{2}^{- 1}$ )×R₃	R₁=649 nmR₂=653 nmR₃=675 nm	0.736	0.680	9.254

Table 3. Common chlorophyll-a concentration inversion models and their modeling accuracy

Mother wavelet	PC	$R_{c}^{2}$	RMSEC/ (μg·L^-1)	$R_{p}^{2}$	RMSEP/ (μg·L^-1)	RPD
rbio3.3	3	0.700	7.864	0.635	10.830	1.551
bior3.3	4	0.551	9.613	0.662	9.545	1.759
sym7	4	0.593	9.156	0.717	8.725	1.924
db1	5	0.656	8.418	0.367	14.702	1.142
db5	5	0.727	7.491	0.725	9.530	1.762
haar	9	0.692	7.966	0.399	14.472	1.160
mexh	1	0.507	10.077	0.560	10.955	1.533
morl	4	0.756	7.081	0.812	8.235	2.039
db4	7	0.779	6.751	0.668	10.974	1.530
sym6	3	0.732	7.431	0.732	6.457	2.600

Table 4. Accuracy evaluation of CWT-PLSR model

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