SOYBEAN CHLOROPHYLL A CONCENTRATION ESTIMATION MODELS BASED ON WAVELET-TRANSFORMED, IN SITU COLLECTED, CANOPY HYPERSPECTRAL DATA

doi:10.3773/j.issn.1005-264x.2008.01.017

Abstract

Abstract:

Aims A growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters. We use a regression model, based on wavelet-transformed reflectance, and vegetation indices (VI) to estimate a wide range of soybean (Glycine max) canopy reflectances to study the sensitivity of wavelet-transformed reflectance and vegetation indices to soybean chlorophyll a concentration. We modify some VI to enhance their sensitivity to variations in chlorophyll a concentration.
Methods We collected soybean canopy hyperspectral reflectance and chlorophyll a concentration data in 2003 and 2004 at two sites in the black soil belt of China. We correlated reflectance, derivative reflectance and soybean chlorophyll a concentration and regressed vegetation indices (NDVI, SAVI, RDVI and MSRI) and soybean chlorophyll a concentration. We transformed soybean canopy reflectance with wavelet analysis and applied extracted wavelet energy coefficient in a regression model for estimation of chlorophyll a concentration.
Important findings Soybean canopy reflectance shows a negative correlation with chlorophyll a concentration in the visible spectral region, while it shows a positive correlation with soybean chlorophyll a concentration in the near-infrared region. Reflectance derivative has a strong relationship with soybean chlorophyll a concentration in the blue, green and red edge spectral region, with maximum correlation coefficient in the red-edge region. Four vegetation indices have strong correlations with soybean chlorophyll a concentration, with R² >0.75. The single variable regression model based upon wavelet-extracted reflectance energy can accurately estimate soybean chlorophyll a concentration, with R ² about 0.75, while R ² was 0.85 with the multivariate regression model. Our study indicated that wavelet analysis can be applied to in-situ collected hyperspectral data for soybean chlorophyll a concentration estimation with accurate prediction and in the future wavelet analysis methods should be applied to hyperspectral data for estimation of other vegetation biophysical and biochemical parameters.

Key words: soybean (Glycine max), hyperspectral, chlorophyll a concentration, vegetation index, wavelet energy coefficient

SONG Kai-Shan, ZHANG Bai, WANG Zong-Ming, LIU Dian-Wei, LIU Huan-Jun. SOYBEAN CHLOROPHYLL A CONCENTRATION ESTIMATION MODELS BASED ON WAVELET-TRANSFORMED, IN SITU COLLECTED, CANOPY HYPERSPECTRAL DATA[J]. Chin J Plant Ecol, 2008, 32(1): 152-160.

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URL: https://www.plant-ecology.com/EN/10.3773/j.issn.1005-264x.2008.01.017

https://www.plant-ecology.com/EN/Y2008/V32/I1/152

Figures/Tables 9

Table 1 Vegetation indices evaluated in the present study

植被指数 Vegetation index	植被指数采用的公式及波段 Formula and bands applied	文献来源 References
归一化植被指数 Normalized difference vegetation index (NDVI)	(R₈₀₁-R₆₇₀)/(R₈₀₁+R₆₇₀)	Rouse et al., 1974
土壤调和植被指数 Soil-adjusted vegetation index (SAVI)	(1+L)×(R₈₀₁-R₆₇₀)/(R₈₀₁+R₆₇₀+L)	Huete, 1988
再归一植被指数 Renormalized difference vegetation index (RDVI)	RDVI=(R₈₀₁-R₆₇₀)/ $(R 801 + R 670)$	Roujean & Breon, 1995
第二修正比值植被指数 Modified second ratio index (MSR)	MSRI=( $R 801 R 670$ -1)/ $(R 801 R 670 + 1)$	Chen & Cihlar,1996

Table 1 Vegetation indices evaluated in the present study

植被指数 Vegetation index	植被指数采用的公式及波段 Formula and bands applied	文献来源 References
归一化植被指数 Normalized difference vegetation index (NDVI)	(R₈₀₁-R₆₇₀)/(R₈₀₁+R₆₇₀)	Rouse et al., 1974
土壤调和植被指数 Soil-adjusted vegetation index (SAVI)	(1+L)×(R₈₀₁-R₆₇₀)/(R₈₀₁+R₆₇₀+L)	Huete, 1988
再归一植被指数 Renormalized difference vegetation index (RDVI)	RDVI=(R₈₀₁-R₆₇₀)/ $(R 801 + R 670)$	Roujean & Breon, 1995
第二修正比值植被指数 Modified second ratio index (MSR)	MSRI=( $R 801 R 670$ -1)/ $(R 801 R 670 + 1)$	Chen & Cihlar,1996

Fig.1 Coefficient of determination (R2) derived from a linear regression of reflectance and derivative against chlorophyll a concentration

Fig.2 Coefficient of determination for all combinations of wavelength used for linear regression analysis of NDVI and SAVI against chlorophyll a concentration,respectively NDVI、SAVI: See Table 1

Fig.3 Coefficient of determination for all combinations of wavelength used for linear regression analysis of MSRI and RDVI against chlorophyll a concentration,respectively MSRI、RDVI: See Table 1

Table 2 Linear and non-linear regression models based on optimum spectral vegetation indices against chlorophyll a concentration

植被指数 Vegetation indices	线性估算模型 Linear regression model (n=100)					模型验证 Model validation (n=44)
植被指数 Vegetation indices	回归公式 Regression formula	R²		RMSE (mg·g^-1)		R²	RMSE (mg·g^-1)
NDVI (441,223)	y=5.317x-3.223		0.782		0.168	0.754	0.173
SAVI (502,32)	y=-20.621x+1.644		0.762		0.171	0.734	0.181
RDVI (488,28)	y=-17.073 0x+1.757 8		0.759		0.172	0.722	0.189
MSRI (257,128)	y=10.358 0x-9.512 9		0.769		0.170	0.731	0.182
植被指数 Vegetation indices	非线性估算模型 Non-linear regression model (n=100)					模型验证 Model validation (n=44)
植被指数 Vegetation indices	回归公式 Regression formula	R²		RMSE (mg·g^-1)		R²	RMSE (mg·g^-1)
NDVI (441,223)	y=0.019e^4.722^x		0.793		0.164	0.726	0.186
SAVI (502,32)	y=2.077e^-18.03^x		0.798		0.162	0.732	0.182
RDVI (488,28)	y=1.851e^-14.91^x		0.778		0.171	0.717	0.193
MSRI (257,128)	y=0.880x^5.81		0.805		0.158	0.747	0.176

Table 3 Regression models based on different levels of wavelet energy coefficients of soybean spectra and their validation

小波能量系数 Wavelet energy coefficient	估算模型 Regression model (n=100)					模型验证 Model validation (n=44)
小波能量系数 Wavelet energy coefficient	回归公式 Regression formula	R²		RMSE (mg·g^-1)		R²	RMSE (mg·g^-1)
第一系数 First coef.	y=0.378 2x-0.058 0		0.656		0.203	0.648	0.211
第二系数 Second coef.	y=1.191 2x+0.455 0		0.674		0.192	0.653	0.203
第三系数 Third coef.	y=2.871 3x+0.251 9		0.76		0.170	0.755	0.173
第四系数 Fourth coef.	y=4.915 6x+0.203 0		0.708		0.188	0.699	0.191
第五系数 Fifth coef.	y=23.110 0x-0.302 8		0.491		0.248	0.479	0.257
第六系数Sixth coef.	y=80.002 0x+0.107 5		0.784		0.162	0.778	0.167
第七系数 Seventh coef.	y=333.510 0x+0.055 7		0.658		0.203	0.649	0.208
第八系数Eighth coef.	y=544.510 0x+0.621 7		0.230		0.305	0.210	0.312
第九系数 Ninth coef.	y=483.310 0x+0.993 0		0.029		0.343	0.007	0.348

Table 4 Regression models based on wavelet energy coefficients from various mother functions and their validation

小波母函数 Mother wavelet functions	估算模型 Regression model (n=100)					模型验证 Model validation (n=44)
小波母函数 Mother wavelet functions	回归公式 Regression formula	R²		RMSE (mg·g^-1)		R²	RMSE (mg·g^-1)
db2	y=71.020 0x+0.115 0		0.780		0.162	0.759	0.171
db4	y=6.626 4x+0.471 5		0.732		0.180	0.724	0.221
db6	y=23.437 0x+0.101 5		0.762		0.169	0.757	0.172
db8	y=16.055 0x-0.213 8		0.763		0.168	0.759	0.171
bior33	y=5.864 1x+0.282 1		0.755		0.172	0.750	0.173
bior68	y=19.846 0x+0.651 2		0.749		0.174	0.743	0.176
rbio33	y=52.416 0x+0.079 0		0.807		0.157	0.790	0.160
ciof5	y=8.524 6x+0.224 8		0.751		0.173	0.742	0.178
dmey	y=19.333 0x+0.268 2		0.757		0.172	0.748	0.175
sym8	y=18.266 0x+0.609 8		0.755		0.173	0.747	0.175

Table 5 The trend of determination coefficient (R2) and root mean square error (RMSE) from multivariate regression model based on soybean canopy reflectance wavelet transforms energy and chlorophyll a concentration

小波母函数 Mother wavelet functions	估算模型 Regression model (n=100)					模型验证 Model validation (n=44)
小波母函数 Mother wavelet functions	回归公式 Regression formula	R²		RMSE (mg·g^-1)		R²	RMSE (mg·g^-1)
db2	y=0.292+164.4x₆-0.296x₁-184.5x₇+4.46x₅		0.831		0.142	0.825	0.145
db4	y=0.692+34.72x₆-0.77x₁+8.08x₃		0.803		0.152	0.810	0.150
db6	y=0.459+10.525x₅-0.399x₁+7.41x₃-2.22x₂		0.814		0.148	0.807	0.151
db8	y=0.342+27.72x₄-19.1x₅-0.369x₁+127.50x₈		0.817		0.147	0.819	0.146
bior33	y=0.51+10.66x₄-0.231x₁+76.66x₇		0.800		0.153	0.790	0.156
bior68	y=0.432+26.72x₅-89.19x₇+8.89x₃-5.64x₂		0.843		0.132	0.854	0.139
rbior33	y=0.045+103.76x₆-173.59x₇-3.10x₃		0.855		0.130	0.859	0.129
ciof5	y=0.377+54.97x₄+0.188x₁-6.73x₂-11.70x₃-31.21x₆		0.828		0.142	0.822	0.144
dmey	y=0.499+41.14x₄-0.234x₁-77.90x₇-6.61x₂+8.61x₃		0.818		0.146	0.810	0.150
sym8	y=18.266x₅+0.610		0.755		0.173	0.747	0.175

Fig.4 Relationship between multivariate regression based on wavelet-transformed soybean canopy spectral reflectance predicted chlorophyll a and measured chlorophyll a concentration

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