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

Journals >Acta Optica Sinica >Volume 37 >Issue 11 >Page 1128002 > Article

Acta Optica Sinica
Vol. 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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DOI: 10.3788/AOS201737.1128002 Cite this Article Set citation alerts

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 Copy Citation Text

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

Salinity content	Spectrum parameter	Regression equation	R	V_RMSE	F-test
K⁺	$\begin{matrix} V_{NDSI (R_{412}, R_{375})} \end{matrix}$	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 2. Quantitative relationships between salt ion content of leaves and optimum NDSI

Salinity content	Spectrum parameter	Regression equation	R	V_RMSE	F-test
K⁺	$\begin{matrix} V_{RVI (R_{405}, R_{375})} \end{matrix}$	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

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

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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