无人机多光谱反演黄河口重度盐渍土盐分的研究

doi:10.3864/j.issn.0578-1752.2019.10.004

Abstract

Abstract:

【Objective】The purpose of this paper was to improve the extraction accuracy of soil salinity information based on remote sensing and understand accurately the degree and distribution of soil salinization. 【Method】Firstly, the severe and concentrated saline soil area of Huanghekou town, Kenli district, was selected as the experimental area, and the unmanned aerial vehicle (UAV) equipped with Sequoia multispectral camera was adopted to acquire the near earth remote sensing image from April 26th to 28th, 2018, then the image preprocessing, including image splicing, radiation correction, orthorectification and geometric correction, was performed. Secondly, the sensitive bands of soil salinity were screened by correlation analysis and grey correlation analysis, respectively, and the spectral parameters were constructed and screened. Thirdly, the soil salinity quantitative analysis models were built by multivariate linear regression (MLR), support vector machine (SVM) and partial least square (PLS) method, then the models’ accuracy was evaluated and the best one was selected. Finally, the best model was applied to the inversion and analysis of soil salinity distribution in the experimental area, and the inversion accuracy was compared with the interpolation result by inverse distance weighting (IDW) method. 【Result】The results showed that the accuracy and significance of the estimation model based on gray correlation analysis were improved by compared with the correlation analysis; Compared the three modeling methods, the prediction ability of the SVM was the best, followed by the PLS, the MLR models’ precision was the lowest, with the calibration R ² and RMSE of 0.820 and 3.626, the validation R ², RMSE and RPD of 0.773, 4.960 and 2.200, and the SVM model of soil salinity based on screened variables by grey correlation analysis was selected the best one; Based on the best model, the soil salinity content in this region was between 0.323 and 21.210 g·kg ^-1 with the average of 6.871 g·kg ^-1 and the severe salinity accounted for 58.094%, which was consistent with the result of the field investigation; The 80% of the error between the inversion result and the interpolation result by the IDW method was controlled within 20% of the sample salt content average, which showed that the two kind of result were similar. 【Conclusion】It could be concluded that the accurate extraction of severe soil salinity information could be achieved on the UAV multi-spectra.

Key words: unmanned aerial vehicle, multi-spectra, saline soil, grey correlation, yellow river estuary

WANG DanYang,CHEN HongYan,WANG GuiFeng,CONG JinQiao,WANG XiangFeng,WEI XueWen. Salinity Inversion of Severe Saline Soil in the Yellow River Estuary Based on UAV Multi-Spectra[J].Scientia Agricultura Sinica, 2019, 52(10): 1698-1709.

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References 42

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波段 Band	相关系数 Correlation coefficient	灰色关联度指数 Grey correlation index
红光 (red,r)	-0.642**	-0.684**
绿光 (gre,g)	-0.591**	-0.688**
红边 (reg)	-0.590*	-0.665*
近红 (nir)	-0.573*	-0.670*

序号 Number	光谱指数 Spcetral index	相关系数 Correlation coefficient	灰色关联度指数 Grey correlation index
1	r+nir	-0.645**	-0.720**
2	R×nir	-0.600*	-0.708**
3	r/g	-0.185	-0.756**
4	R+nir+g	-0.641**	-0.665**
5	$\sqrt{r^{2}+reg^{2}}$	-0.601**	-0.668**
6	g×nir	-0.562*	-0.658*
7	g+reg	-0.603*	-0.671*
8	(r+g)/(r-g)	-0.015	-0.471*
9	g×r	-0.145	-0.701*
10	$\sqrt{r^{2}+g^{2}}$	-0.640**	-0.700**
11	r+nir+reg	-0.631**	-0.709**
12	r×reg	-0.591*	-0.649*
13	r+reg	-0.634**	-0.691**
14	(reg-r)/( reg+r)	0.252	-0.183
15	g×nir×r	-0.556*	-0.603*
16	g+r+reg	-0.632*	-0.678*
17	g×r×reg	-0.037	-0.576*
18	g×reg	-0.124	-0.609*
19	(reg-g)/( reg+g)	0.148	-0.354
20	r-g	-0.21	-0.206
21	reg-r	0.048	-0.079
22	reg/r	-0.17	-0.709*
23	$\sqrt{g^{2}+reg^{2}+r^{2}}$	-0.601*	-0.668*
24	$\sqrt{r\times reg}$	-0.634*	-0.689*
25	$\sqrt{r\times r}$	-0.643*	-0.702*
26	$\sqrt{g^{2}+reg^{2}}$	-0.605*	-0.669*
27	(reg+g)/(g-reg)	0.117	-0.238
28	(r+g)/(r-g)	-0.015	-0.471*
29	$\sqrt{reg\times g}$	-0.631*	-0.679*
30	$\sqrt{reg \times r \times g}$	-0.613*	-0.689*
31	r+nir+g+reg	-0.631*	-0.660*
32	r+nir+g	-0.641*	-0.665*
33	nir+g+reg	-0.606*	-0.652*

分析方法 Analytical methods	建模方法 Modeling methods	建模精度Calibration accuracy		验证精度Verification accuracy
分析方法 Analytical methods	建模方法 Modeling methods	决定系数 R²	均方根误差 RMSE	决定系数 R²	均方根误差 RMSE	相对分析误差 RPD
相关性分析 Correlation analysis	MLR	0.645	5.217	0.564	5.157	1.261
	SVM	0.730	5.363	0.782	5.596	2.083
	PLS	0.711	5.545	0.707	5.362	1.870
灰色关联度 Grey correlation index	MLR	0.691	4.291	0.687	5.013	1.731
	SVM	0.820	3.626	0.773	4.960	2.203
	PLS	0.722	4.677	0.724	4.731	2.210

等级 Grade	反演图Inversion map		插值图 Interpolation map
等级 Grade	像元数 Pixel count	所占比例 Percentage (%)	像元数 Pixel count	所占比例 Percentage (%)
非盐渍土Non-saline soil (<2.0 g·kg^-1)	3027962	7.640	1025934	2.476
轻度盐渍土Mild saline soil (≥2.0-4.0 g·kg^-1)	1928771	4.871	7327621	17.691
中度盐渍土Moderate saline soil (≥4.0-6.0g·kg^-1)	10091297	25.473	11092173	26.779
重度盐渍土Severe saline soil (≥6.0-10.0 g·kg^-1)	23018069	58.094	20015014	48.321
盐土Solonchak (≥10.0 g·kg^-1)	1558977	3.932	1959849	4.731

Salinity Inversion of Severe Saline Soil in the Yellow River Estuary Based on UAV Multi-Spectra

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