Research on the Estimation of Salt Ions of Vegetation Leaves Based on Band Combination

Zhe Li; Fei Zhang; Haikuan Feng; Lihua Chen; Xiaoqiang Zhu

doi:10.3788/AOS201737.1128002

Acta Optica Sinica, Volume. 37, Issue 11, 1128002(2017)

Research on the Estimation of Salt Ions of Vegetation Leaves Based on Band Combination

Zhe Li^1,2, Fei Zhang^1,2,3、*, Haikuan Feng⁴, Lihua Chen⁵, and Xiaoqiang Zhu^1,2

Author Affiliations

¹ College of Resource and Environment Sciences, Xinjiang University, Urumqi, Xinjiang 830046, China

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

³ General Institutes of Higher Learning Key Laboratory of Smart City and Environmental Modeling, Xinjiang University, Urumqi, Xinjiang 830046, China

⁴ Beijing Research Center for Information in Agriculture, Beijing 100097, China

⁵ Area Management Bureau of Ebinur Lake Wetland Natural Reserve, Bole, Xinjiang 833400, China

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Figures & Tables(10)

Fig. 1. Map of study area and distribution of sampling points

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Fig. 2. Spatial distribution of halophytes with different salt ion contents. (a) Ca2+; (b) K+; (c) Mg2+; (d) Na+

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Fig. 3. Correlation coefficient between salt ion content of leaves and original spectral reflectivity

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Fig. 4. Coefficient of determination between salt ion content of leaves and DVI. (a) Ca2+; (b) K+; (c) Mg2+; (d) Na+

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Fig. 5. Coefficient of determination between salt ion content of leaves and NDSI. (a) Ca2+; (b) K+; (c) Mg2+; (d) Na+

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Fig. 6. Coefficient of determination between salt ion content of leaves and RVI. (a) Ca2+; (b) K+; (c) Mg2+; (d) Na+

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Fig. 7. P-P plot and histogram of observed values and predicted values with different vegetation indices. (a) NDSI; (b) DVI; (c) RVI

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Table 1. Quantitative relationships between salt ion content of leaves and optimum DVI

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Table 1. Quantitative relationships between salt ion content of leaves and optimum DVI

Salinity content	Spectrum parameter	Regression equation	R	V_RMSE	F-test
K⁺	$\begin{matrix} V_{DVI (R_{415}, R_{380})} \end{matrix}$	y=4.288x+0.119	0.466	0.101	10.834
		y=94.495x²+2.450x+0.114	0.489	0.100	6.252
		y=-1458.3x³+140.958x²+2.601x+0.110	0.499	0.101	4.092
Na⁺	$\begin{matrix} V_{DVI (R_{1750}, R_{1480})} \end{matrix}$	y=-0.387ln x-0.576	0.745	0.198	48.685
		y=29.573x²-10.682x+1.0726	0.759	0.196	25.741
		y=-5.142x³+31.108x²-10.813x+1.076	0.760	0.198	16.709
Ca²⁺	$\begin{matrix} V_{DVI (R_{478}, R_{440})} \end{matrix}$	y=5.129x+0.106	0.382	0.124	6.661
		y=18.973x²+4.652x+0.107	0.382	0.126	3.252
		y=-7417.186x³+246.728x²+4.248x+0.097	0.392	0.127	2.234
Mg²⁺	$\begin{matrix} V_{DVI (R_{478}, R_{440})} \end{matrix}$	y=0.043ln x+0.308	0.436	0.059	9.166
		y=1.259x^0.577	0.448	0.770	9.782
		y=-42643.662x³-1775.032x²+25.233x-0.004	0.452	0.060	3.170

Table 2. Quantitative relationships between salt ion content of leaves and optimum NDSI

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Table 2. Quantitative relationships between salt ion content of leaves and optimum NDSI

Salinity content	Spectrum parameter	Regression equation	R	V_RMSE	F-test
K⁺	$\begin{matrix} V_{NDSI (R_{412}, R_{375})} \end{matrix}$	y=0.835x+0.119	0.482	0.099	11.834
		y=3.326x²+0.520x+0.112	0.500	0.100	6.346
		y=19.853x³+0.599x²+0.466x+0.117	0.503	0.101	4.170
Na⁺	$\begin{matrix} V_{NDSI (R_{1145}, R_{1125})} \end{matrix}$	y=-9.619x-0.022	0.744	0.198	48.450
		y=-7.9612x²-10.469x-0.04	0.744	0.200	23.625
		y=-216.53x³-43.902x²-12.214x-0.0636	0.745	0.203	15.342
Ca²⁺	$\begin{matrix} V_{NDSI (R_{1825}, R_{950})} \end{matrix}$	y=-0.038x+0.145	0.329	0.085	4.721
		y=0.0002x²-0.038x+0.145	0.329	0.129	2.300
		y=0.002x³+0.004x²-0.047x+0.138	0.335	0.130	1.557
Mg²⁺	$\begin{matrix} V_{NDSI (R_{450}, R_{375})} \end{matrix}$	y=0.230x+0.066	0.403	0.060	7.571
		y=0.047exp(3.232x)	0.429	0.778	8.804
		y=-13.866x³+7.978x²-0.817x+0.076	0.610	0.053	7.304

Table 3. Quantitative relationships between salt ion content of leaves and optimum RVI

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Table 3. Quantitative relationships between salt ion content of leaves and optimum RVI

Salinity content	Spectrum parameter	Regression equation	R	V_RMSE	F-test
K⁺	$\begin{matrix} V_{RVI (R_{405}, R_{375})} \end{matrix}$	y=0.538x-0.411	0.520	0.097	14.430
		y=1.551x²-2.781x+1.345	0.549	0.096	8.179
		y=9.750x³-29.747x²+30.455x-10.332	0.561	0.096	8.268
Na⁺	$\begin{matrix} V_{RVI (R_{1100}, R_{1125})} \end{matrix}$	y=4.790x-4.789	0.634	0.229	26.274
		y=36.317x²-74.407x+38.335	0.675	0.222	15.889
		y=-620.76x³+2078.8x²-2314.3x+855.810	0.698	0.095	16.416
Ca²⁺	$\begin{matrix} V_{RVI (R_{1825}, R_{1125})} \end{matrix}$	y=-0.460x+0.722	0.324	0.125	4.588
		y=-0.693x²+1.272x-0.354	0.329	0.127	2.308
		y=8.619x³-33.457x²+42.588x-17.638	0.340	0.125	2.301
Mg²⁺	$\begin{matrix} V_{RVI (R_{1675}, R_{1475})} \end{matrix}$	y=-0.044x+0.189	0.344	0.061	5.245
		y=-0.010x²+0.003x+0.137	0.348	0.062	2.613
		y=0.009x³-0.079x²+0.170x+0.007	0.349	0.063	1.712

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Zhe Li, Fei Zhang, Haikuan Feng, Lihua Chen, Xiaoqiang Zhu. Research on the Estimation of Salt Ions of Vegetation Leaves Based on Band Combination[J]. Acta Optica Sinica, 2017, 37(11): 1128002

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Paper Information

Category: Remote Sensing and Sensors

Received: May. 8, 2017

Accepted: --

Published Online: Sep. 7, 2018

The Author Email: Zhang Fei (zhangfei3s@163.com)

DOI:10.3788/AOS201737.1128002

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology