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

[1]	PARK S H, LEE B R, LEE J H . S nutrition alleviates salt stress by maintaining the assemblage of photosynthetic organelles in Kentucky bluegrass (Poa pratensis L.). Plant Growth Regulation, 2016,79(3):367-375.
[2]	王辉, 高玉录, 于梦, 杜远鹏, 孙永江, 翟衡 . 根灌乙酸及葡萄酒对海水胁迫下葡萄光抑制的影响. 中国农业科学, 2018,51(21):4210-4218.
	WANG H, GAO Y L, YU M, DU Y P, SUN Y J, ZHAI H . Effect of root irrigation of acetic acid and wine on photoinhibition of grape under seawater stress. Scientia Agricultura Sinica, 2018,51(21):4210-4218. (in Chinese)
[3]	WHITNEY K, SCUDIERO E, El-ASKARY H M, SKAGGS T H, ALLALI M, CORWIN D L . Validating the use of MODIS time series for salinity assessment over agricultural soils in California, USA. Ecological Indicators, 2018,93:889-898. doi: 10.1016/j.ecolind.2018.05.069
[4]	王海江, 蒋天池, YUNGER J A, 李亚莉, 田甜, 王金刚 . 基于支持向量机的土壤主要盐分离子高光谱反演模型. 农业机械学报, 2018,49(5):263-270.
	WANG H J, JIANG T C, YUNGER J A, LI Y L, TIAN T, WANG J G . Hyperspectral inverse model for soil salt ions based on support vector machine. Transactions of the Chinese Society for Agricultural Machinery, 2018,49(5):263-270. (in Chinese)
[5]	SRIVASTAVA R, SETHI M, YADAV R K, BUNDELA D S, SINGH M, CHATTARAJ S, SINGH S K, NASRE R A, BISHNOI S R, DHALE S, MOHEKAR D S, BARTHWAL A K . Visible-near infrared reflectance spectroscopy for rapid characterization of salt-affected soil in the Indo-Gangetic plains of Haryana, India. Journal of the Indian Society of Remote Sensing, 2017,45(2):307-315. doi: 10.1007/s12524-016-0587-0
[6]	WENG Y L, GONG P, ZHU Z L . Reflectance spectroscopy for the assessment of soil salt content in soils of the Yellow River Delta of China. International Journal of Remote Sensing, 2008,29(19):5511-5531. doi: 10.1080/01431160801930248
[7]	张东辉, 赵英俊, 秦凯, 赵宁博, 杨越超 . 光谱变换方法对黑土养分含量高光谱遥感反演精度的影响. 农业工程学报, 2018,34(20):141-147.
	ZHANG D H, ZHAO Y J, QIN K, ZHAO N B, YANG Y C . Influence of spectral transformation methods on nutrient content inversion accuracy by hyperspectral remote sensing in black soil. Transactions of the Chinese Society of Agricultural Engineering, 2018,34(20):141-147. (in Chinese)
[8]	朱赟, 申广荣, 项巧巧, 吴裕 . 基于不同光谱变换的土壤盐含量光谱特征分析. 土壤通报, 2017,48(3):560-568.
	ZHU Y, SHEN G R, XIANG Q Q, WU Y . Spectral characteristics of soil salinity based on different pre-processing methods. Chinese Journal of Soil Science, 2017,48(3):560-568. (in Chinese)
[9]	XU C, ZENG W Z, HUANG J S, WU J W, LEEUWEN W J V . Prediction of soil moisture content and soil salt concentration from hyperspectral laboratory and field data. Remote Sensing, 2016,8(1):42-62. doi: 10.3390/rs8010042
[10]	WIEGAND C L, EVERITT J H, RICHARDSON A J . Comparison of multispectral video and SPOT-1 HRV observations for cotton affected by soil salinity. International Journal of Remote Sensing, 1992,13(8):1511-1525. doi: 10.1080/01431169208904205
[11]	张贤龙, 张飞, 张海威, 李哲, 海清, 陈丽华 . 基于光谱变换的高光谱指数土壤盐分反演模型优选. 农业工程学报, 2018,34(1):110-117.
	ZHANG X L, ZHANG F, ZHANG H W, LI Z, HAI Q, CHEN L H . Optimization of soil salt inversion model based on spectral transformation from hyperspectral index. Transactions of the Chinese Society of Agricultural Engineering, 2018,34(1):110-117. (in Chinese)
[12]	SCUDIERO E, SKAGGS T H, CORWIN D L . Regional-scale soil salinity assessment using Landsat ETM+ canopy reflectance. Remote Sensing of Environment, 2015,169:335-343. doi: 10.1016/j.rse.2015.08.026
[13]	李晋, 赵庚星, 常春艳, 刘海腾 . 基于HSI高光谱和TM图像的土地盐渍化信息提取方法. 光谱学与光谱分析, 2014,34(2):520-525.
	LI J, ZHAO G X, CHANG C Y, LIU H T . Land salinization information extraction method based on HSI hyperspectral and TM imagery. Spectroscopy and Spectral Analysis, 2014,34(2):520-525. (in Chinese)
[14]	魏阳, 丁建丽, 王飞 . 基于Landsat OLI的绿洲灌区土壤盐度最优预测尺度分析. 中国农业科学, 2017,50(15):2969-2982.
	WEI Y, DING J L, WANG F . Optimal scale analysis of soil salinity prediction in oasis irrigated area of arid land based on Landsat OLI. Scientia Agricultura Sinica, 2017,50(15):2969-2982. (in Chinese)
[15]	SIDIKE A, ZHAO S H, WEN Y M . Estimating soil salinity in Pingluo county of China using QuickBird data and soil reflectance spectra. International Journal of Applied Earth Observation and Geoinformation, 2014,26:156-175. doi: 10.1016/j.jag.2013.06.002
[16]	NAWAR S, BUDDENBAUM H, HILL J . Estimation of soil salinity using three quantitative methods based on visible and near-infrared reflectance spectroscopy: A case study from Egypt. Arabian Journal of Geosciences, 2015,8(7):5127-5140. doi: 10.1007/s12517-014-1580-y
[17]	SCUDIERO E, CORWIN D L, MORARI F, ANDERSON R G, SKAGGS T H . Spatial interpolation quality assessment for soil sensor transect datasets. Computers and Electronics in Agriculture, 2016,123:74-79. doi: 10.1016/j.compag.2016.02.016
[18]	扶卿华, 倪绍祥, 王世新, 周艺 . 土壤盐分含量的遥感反演研究. 农业工程学报, 2007,23(1):48-54.
	FU Q H, NI S X, WANG S X, ZHOU Y . Retrieval of soil salt content based on remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 2007,23(1):48-54. (in Chinese)
[19]	梁静, 丁建丽, 王敬哲, 王飞 . 基于反射光谱与Landsat 8 OLI多光谱数据的艾比湖湿地土壤盐分估算. 土壤学报, 2018,56(2):1-12.
	LIANG J, DING J L, WANG J Z, WANG F . Quantitative estimation and mapping of soil salinity in the Ebinur Lake wetland based on Vis-NIR reflectance and Landsat 8 OLI data. Acta Pedologica Sinica, 2019,56(2):1-12. (in Chinese)
[20]	AMANI M, SALEHI B , MAHDAVIS. Temperature-vegetation-soil moisture dryness index(TVMDI). Remote Sensing of Environment, 2017,197:1-14. doi: 10.1016/j.rse.2017.05.026
[21]	汪小钦, 王苗苗, 王绍强, 吴云东 . 基于可见光波段无人机遥感的植被信息提取. 农业工程学报, 2015,31(5):152-159.
	WANG X Q, WANG M M, WANG S Q, WU Y D . Extraction of vegetation information from visible unmanned aerial vehicle images. Transactions of the Chinese Society of Agricultural Engineering, 2015,31(5):152-159. (in Chinese)
[22]	HASSANESFAHANI L, TORRESTUA A, JENSEN A . Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks. Remote Sensing, 2015,7(3):2627-2646. doi: 10.3390/rs70302627
[23]	王海峰, 张智韬, 付秋萍, 陈硕博, 边江, 崔婷 . 低空无人机多光谱遥感数据的土壤含水率反演. 节水灌溉, 2018,43(1):90-94, 102.
	WANG H F, ZHANG Z T, FU Q P, CHEN S B, BIAN J, CUI T . Inversion of soil moisture content based on multispectral remote sensing data of low-altitude UAV. Water Saving Irrigation, 2018,43(1):90-94, 102. (in Chinese)
[24]	陈硕博, 陈俊英, 张智韬, 边江, 王禹枫, 石树兰 . 无人机多光谱遥感反演抽穗期冬小麦土壤含水率研究. 节水灌溉, 2018,43(5):39-43.
	CHEN S B, CHEN J Y, ZHANG Z T, BIAN J, WANG Y F, SHI S L . Retrieving soil water content of winter wheat during heading period by multi-spectral remote sensing of Unmanned Aerial Vehicle (UAV). Water Saving Irrigation, 2018,43(5):39-43. (in Chinese)
[25]	AASEN H, GNYP M L, MIAO Y X, BARETH G . Automated hyperspectral vegetation index retrieval from multiple correlation matrices with HyperCor. Photogrammetric Engineering and Remote Sensing, 2014,80(8):785-795. doi: 10.14358/PERS.80.8.785
[26]	MORELLOS A, PANTAZI X E, MOSHOU D, ALEXANDRIDIS T, WHETTON R, TZIOTZIOS G, WIEBENSOHN J, BILL R, ABDUL M . Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy. Biosystems Engineering, 2016,152(12):104-116. doi: 10.1016/j.biosystemseng.2016.04.018
[27]	HOFFMANN H, JENSEN R, THOMSEN A, NLETO H, RASMUSSEN J, FRIBORG T . Crop water stress maps for entire growing seasons from visible and thermal UAV imagery. Biogeosciences, 2016,13(24):6545-6563. doi: 10.5194/bg-13-6545-2016
[28]	ROSA O C, BAUP F, FABRE S, FIEUZAL R, BRIOTTE X . Improvement of soil moisture retrieval from hyperspectral VNIR- SWIR data using clay content information: From laboratory to field experiments. Remote Sensing, 2015,7(3):3184-3205. doi: 10.3390/rs70303184
[29]	JIN X L, DU J, LIU H J, WANG Z M, SONG K S . Remote estimation of soil organic matter content in the Sanjiang plain, Northest China: The optimal band algorithm versus the GRA-ANN model. Agricultural and Forest Meteorology, 2016,218(12):250-260.
[30]	张宗玲, 韩增德, 刘立晶, 李晓栋, 郝付平, 董哲 . 玉米穗茎兼收割台夹持输送装置参数优化. 农业机械学报, 2018,49(3):114-121.
	ZHANG Z L, HAN Z D, LIU L J, LI X D, HAO F P, DONG Z . Parameters optimization for gripping and delivering device of corn harvester for reaping both corn stalk and spike. Transactions of the Chinese Society for Agricultural Machinery, 2018,49(3):114-121. (in Chinese)
[31]	陈红艳, 赵庚星, 陈敬春, 王瑞燕, 高明秀 . 基于改进植被指数的黄河口区盐渍土盐分遥感反演. 农业工程学报, 2015,31(5):107-114.
	CHEN H Y, ZHAO G X, CHEN J C, WANG R Y, GAO M X . Remote sensing inversion of saline soil salinity based on modified vegetation index in estuary area of Yellow River. Transactions of the Chinese Society of Agricultural Engineering, 2015,31(5):107-114. (in Chinese)
[32]	李鑫星, 朱晨光, 周婧, 孙龙清, 曹霞敏, 张小栓 . 光谱技术在水产养殖水质监测中的应用进展及趋势. 农业工程学报, 2018,34(19):184-194.
	LI X X, ZHU C G, ZHOU J, SUN L Q, CAO X M, ZHANG X S . Review and trend of water quality detection in aquaculture by spectroscopy technique. Transactions of the Chinese Society of Agricultural Engineering, 2018,34(19):184-194. (in Chinese)
[33]	URSELMANS T T, SCHMIDT H, JOERGENSEN R G, LUDWIG B . Usefulness of near-infrared spectroscopy to determine biological and chemical soil properties: Importance of sample pre-treatment. Soil Biology and Biochemistry, 2008,40(5):1178-1188. doi: 10.1016/j.soilbio.2007.12.011
[34]	翁永玲, 宫鹏 . 黄河三角洲盐渍土盐分特征研究. 南京大学学报(自然科学版), 2006,42(6):602-610.
	WENG Y L, GONG P . Soil salinity measurements on the Yellow River Delta. Journal of Nanjing University (Natural Sciences), 2006,42(6):602-610. (in Chinese)
[35]	安乐生, 赵全升, 叶思源, 刘贯群, 丁喜桂 . 黄河三角洲地下水关键水盐因子及其植被效应. 水科学展, 2011,22(5):689-695.
	AN L S, ZHAO Q S, YE S Y, LIU G Q, DING X G . Water-salt interactions factors and vegetation effects in the groundwater ecosystem in Yellow River Delta. Advances in Water Science, 2011,22(5):689-695. (in Chinese)
[36]	BEN-DOR E, GOLDSHLEGER N, ESHEL M, NIRABLIS V, BASON U . Combined active and passive remote sensing methods for assessing soil salinity. Remote Sensing of Soil Salinization, 2008(10):235-255.
[37]	GOLDSHLEGERN, BEN-DOR E, LUGASSI R, ESHEL G . Soil degra-dation monitoring by remote sensing: Examples with three degradation processes. Soil Science Society of America Journal, 2010,74(5):1433-1445. doi: 10.2136/sssaj2009.0351
[38]	徐伟杰 . 火星表面模拟矿物和卤水的光谱鉴别研究[D]. 威海:山东大学, 2018.
	XU W J . Spectral discriminant analysis of martian simulated minerals and brines[D]. Weihai: Shandong University, 2018. (in Chinese)
[39]	FAN X W, LIU Y B, TAO J M, WENG Y L . Soil salinity retrieval from advanced multi-spectral sensor with partial least square regression. Remote Sensing, 2015,7(1):488-511. doi: 10.3390/rs70100488
[40]	翁永玲, 宫鹏 . 土壤盐渍化遥感应用研究进展. 地理科学, 2006,26(3):369-375.
	WENG Y L, GONG P . A review on remote sensing technique for salt-affected soils. Scientia Georaphica Sinica, 2006,26(3):369-375. (in Chinese)
[41]	陈文娇 . 基于多源数据光谱转换的土壤盐分反演与动态分析[D]. 南京: 东南大学, 2018.
	CHEN W J . Soil salinity retrieval and dynamic analysis based on spectral intercalibration of multi-sensor data[D]. Nanjing: Southeast University, 2018. (in Chinese)
[42]	周鹏, 杨玮, 李民赞, 郑立华, 陈玉青 . 基于灰度关联-极限学习机的土壤全氮预测. 农业机械学报, 2017,48(S1):271-276.
	ZHOU P, YANG W, LI M Z, ZHENG L H, CHEN Y Q . Soil total nitrogen content prediction based on gray correlation-extreme learning machine. Transactions of the Chinese Society for Agricultural Machinery, 2017,48(S1):271-276. (in Chinese)

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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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