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

中国农业科学 ›› 2019, Vol. 52 ›› Issue (10): 1698-1709.doi: 10.3864/j.issn.0578-1752.2019.10.004

• 耕作栽培·生理生化·农业信息技术 • 上一篇下一篇

无人机多光谱反演黄河口重度盐渍土盐分的研究

王丹阳¹,陈红艳¹(),王桂峰²,丛津桥³,王向锋⁴,魏学文²

1 土肥资源高效利用国家工程实验室/山东农业大学资源与环境学院,山东泰安 271018
2 山东省棉花生产技术指导站,济南 250000
3 泰山区自然资源局,山东泰安 271000
4 垦利区国土资源局,山东东营 27500

收稿日期:2018-12-28 接受日期:2019-03-05 出版日期:2019-05-16 发布日期:2019-05-23
通讯作者: 陈红艳
作者简介:王丹阳,E-mail： 892218027@qq.com。
基金资助:
国家自然科学基金(41877003,41671346);山东省自然科学基金(ZR2019MD039);山东省重点研发计划(2017CXGC0306);“十二五”国家科技支撑计划(2015BAD23B0202);山东农业大学“双一流”奖补资金(SYL2017XTTD02)

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

WANG DanYang¹,CHEN HongYan¹(),WANG GuiFeng²,CONG JinQiao³,WANG XiangFeng⁴,WEI XueWen²

1 National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources/College of Resources and Environment, Shandong Agricultural University, Taian 271018, Shangdong
2 Shandong Cotton Production Technical Guidance Station, Jinan 250000
3 Taishan Natural Resources Bureau, Taian 271000, Shangdong
4 Kenli Land and Resources Bureau, Dongyin 27500, Shangdong

Received:2018-12-28 Accepted:2019-03-05 Online:2019-05-16 Published:2019-05-23
Contact: HongYan CHEN

摘要/Abstract

摘要：

【目的】为提高土壤盐分信息定量遥感提取精度,准确掌握土壤盐渍化程度与分布。【方法】选择垦利区黄河口镇集中连片的重度盐渍土区域为试验区,于2018年4月26日—28日采用搭载Sequoia多光谱相机的无人机进行试验区近地遥感图像采集,并进行图像拼接、辐射校正、正射校正和几何校正等预处理;然后基于相关性分析、灰色关联度分析筛选土壤盐分的敏感波段,构建并筛选光谱参量;进而分别采用多元线性回归（multivariable linear regression,MLR）、支持向量机（support vector machine,SVM）及偏最小二乘（partial least square,PLS）方法构建土壤盐分定量反演模型,并进行验证与评价;最后基于最佳模型进行试验区土壤盐分的分布反演与分析,并与反距离加权插值结果进行精度比较。【结果】相较相关性分析,通过灰色关联度分析的反演模型精度及显著性均有所提高;对比3种建模方法,SVM模型精度最高,PLS模型次之,MLR模型最低,最佳模型为基于灰色关联度分析筛选变量的支持向量机模型,其建模R ²、RMSE分别为0.820、3.626,验证R ²、RMSE、RPD分别为0.773、4.960、2.200;据此模型反演得到该区域土壤盐分含量为0.323—21.210 g·kg ^-1,平均值为6.871 g·kg ^-1,重度盐渍土占58.094%,与实地调查结果较为一致;反演结果与反距离加权插值结果的误差80%控制在样本盐分含量平均值的20%以内,亦较为相近。【结论】基于无人机多光谱可实现重度盐渍土盐分信息的准确提取。

关键词: 无人机, 多光谱, 盐渍土, 灰色关联度, 黄河口

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

王丹阳,陈红艳,王桂峰,丛津桥,王向锋,魏学文. 无人机多光谱反演黄河口重度盐渍土盐分的研究[J]. 中国农业科学, 2019, 52(10): 1698-1709.

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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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

无人机多光谱反演黄河口重度盐渍土盐分的研究

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

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