基于Sentinel卫星及无人机多光谱的滨海冬小麦种植区土壤盐分反演研究——以黄三角垦利区为例

doi:10.3864/j.issn.0578-1752.2020.24.004

Abstract

Abstract:

【Objective】 The purpose of this paper was to explore an accurate and efficient remote sensing method for soil salinity extraction of wheat field in the Yellow River Delta, and obtain the degree and distribution of soil salinization of wheat fields.【Method】This study took Kenli District as the research area, and set 77 sample points in winter wheat growing area evenly. At the same time, two representative test areas and 99 grid sample points were set, and the surface soil salinity data in wheat field and the multi-spectral images of UAV in the test area were collected. The sensitive spectral parameters were screened from four spectral bands (red, green, red edge, and near-infrared) and five spectral indexes (SI, NDVI, DVI, RVI, and GRVI). Stepwise regression, partial least squares, BP neural network and support vector machine methods were used to establish models for predicting the soil salinity, and the band ratio mean method was used to obtain the correction coefficient of the corresponding band of sentinel-2A satellite image. And then the selected soil salinity estimation model was converted into an inversion model based on satellite image. After using the data from the wheat field sample points to verify the models, the best soil salinity inversion model in wheat field was selected, and two scales of soil salinity inversion are realized in the test areas and the research area.【Result】The results showed that the four bands of UAV and the spectral indexes NVDI, RVI and SI were significantly correlated with soil salinity. Among the 13 models of the four modeling methods, the four index models established by NDVI, RVI and SI were better than the other models in modeling and verifying R ²; The best inversion model was the spectral index model obtained by partial least square method: Y=-9.4774×NDVI₁+ 0.4794×RVI₁+ 3.0747×SI₁+ 5.0604, and the accuracy R² was 0.513 and RMSE was 1.379. By using this model, the soil salinity distribution map of the test area and the whole wheat area was obtained. Combined with the measured interpolation and the survey, the inversion model and spatial distribution results were proved to be accurate and reliable. 【Conclusion】In this study, the soil salinity inversion model of the coastal wheat area based on the integration of satellite and UAV was constructed, which had positive reference value for the production and management of crops in the coastal saline area.

Key words: winter wheat, unmanned aerial vehicle, sentinel-2A satellite, soil salinity, inversion model

XI Xue,ZHAO GengXing,GAO Peng,CUI Kun,LI Tao. Inversion of Soil Salinity in Coastal Winter Wheat Growing Area Based on Sentinel Satellite and Unmanned Aerial Vehicle Multi-Spectrum— A Case Study in Kenli District of the Yellow River Delta[J].Scientia Agricultura Sinica, 2020, 53(24): 5005-5016.

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References 38

[1]	王建华, 胡鹏, 龚家国. 实施黄河口大保护推动黄河流域生态文明建设. 人民黄河, 2019,41(10):7-10.
	WANG J H, HU P, GONG J G. Taking the great protection of the Yellow River estuary as the grasp to promote the construction of ecological civilization in the Yellow River basin. Yellow River, 2019,41(10):7-10. (in Chinese)
[2]	刘丽娟, 李小玉. 干旱区土壤盐分积累过程研究进展. 生态学杂志, 2019,38(3):891-898.
	LIU L J, LI X Y. Progress in the study of soil salt accumulation in arid region. Chinese Journal of Ecology, 2019,38(3):891-898. (in Chinese)
[3]	张顺, 贾永刚, 连胜利, 付腾飞, 潘玉英. 电导率法在土壤盐渍化中的改进和应用进展. 土壤通报, 2014,45(3):754-759.
	ZHANG S, JIA Y G, LIAN S L, FU T F, PAN Y Y. Application and improvement of electrical conductivity measurements in soil salinity. Chinese Journal of Soil Science, 2014,45(3):754-759. (in Chinese)
[4]	阿尔达克·克里木, 塔西甫拉提·特依拜, 张东, 依力亚斯江·努尔麦麦提. 基于高光谱的ASTER影像土壤盐分模型校正及验证. 农业工程学报, 2016,32(12):144-150.
	ARDAK K L M, TAXIPULAT T Y P, ZHANG D, ILYASJ N R M M T. Calibration and validation of soil salinity estimation model based on measured hyperspectral and Aster image. Transactions of the Chinese Society of Agricultural Engineering, 2016,32(12):144-150. (in Chinese)
[5]	吴亚坤, 刘广明, 苏里坦, 杨劲松. 多源数据的区域土壤盐渍化精确评估. 光谱学与光谱分析, 2018,38(11):3528-3533.
	WU Y K, LIU G M, SU L T, YANG J S. Accurate evaluation of regional soil salinization using multi-source data. Spectroscopy and Spectral Analysis, 2018,38(11):3528-3533. (in Chinese)
[6]	胡盈盈, 王瑞燕, 陈红艳, 李玉萍, 刘燕群. 黄河三角洲春秋两季土壤盐分遥感反演及时空变异研究. 测绘与空间地理信息, 2018,41(8):78-81.
	HU Y Y, WANG R Y, CHEN H Y, LI Y P, LIU Y Q. Research on remote sensing retrieval and temporal variation of soil salinity in the spring and autumn of the Yellow River Delta. Geomatics & Spatial Information Technology, 2018,41(8):78-81. (in Chinese)
[7]	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
[8]	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, 2019,56(2):320-330.
	LIANG J, DING J L, WANG J Z, WANG F. Quantitative estimationand 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):320-330. (in Chinese)
[9]	陈俊英, 王新涛, 张智韬, 韩佳, 姚志华, 魏广飞. 基于无人机-卫星遥感升尺度的土壤盐渍化监测方法. 农业机械学报, 2019,50(12):161-169.
	CHEN J Y, WANG X T, ZHANG Z T, HAN J, YAO Z H, WEI G F. Soil salinization monitoring method based on UAV-satellite remote sensing scale-up. Transactions of the Chinese Society for Agricultural Machinery, 2019,50(12):161-169. (in Chinese)
[10]	ZHANG S M, ZHAO G X. A harmonious satellite-unmanned aerial vehicle-ground measurement inversion method for monitoring salinity in coastal saline soil. Remote Sensing, 2019,11(14):1700. doi: 10.3390/rs11141700
[11]	SONG C Y, REN H X, HUANG C. Estimating soil salinity in the Yellow River Delta, Eastern China—An integrated approach using spectral and terrain indices with the generalized additive model. Pedosphere, 2016, 26(5):626-635.
[12]	贾吉超, 赵庚星, 高明秀, 王卓然, 常春艳, 姜曙千, 李晋. 黄河三角洲典型区域冬小麦播种面积变化与土壤盐分关系研究. 植物营养与肥料学报, 2015,21(5):1200-1208. doi: 10.11674/zwyf.2015.0513
	JIA J C, ZHAO G X, GAO M X, WANG Z R, CHANG C Y, JIANG S Q, LI J. Study on the relationship between winter wheat sowing area changes and soil salinity in the typical area of the Yellow River Delta. Journal of Plant Nutrition and Fertilizers, 2015,21(5):1200-1208. (in Chinese) doi: 10.11674/zwyf.2015.0513
[13]	张同瑞, 赵庚星, 高明秀, 王卓然, 贾吉超, 李萍, 安德玉. 基于近地面多光谱的黄河三角洲典型地区土壤含盐量估算研究. 光谱学与光谱分析, 2016,36(1):248-253. pmid: 27228776
	ZHANG T R, ZHAO G X, GAO M X, WANG Z R, JIA J C, LI P, AN D Y. Soil salinity estimation based on near-ground multispectral imagery in typical area of the Yellow River Delta. Spectroscopy and Spectral Analysis, 2016,36(1):248-253. (in Chinese) pmid: 27228776
[14]	ZHANG T T, ZENG S L, GAO Y, QUYANG Z T, LI B, FANG C M, ZHAO B. Using hyperspectral vegetation indices as a proxy to monitor soil salinity. Ecological Indicators, 2011,11(6):1552-1562. doi: 10.1016/j.ecolind.2011.03.025
[15]	张天举, 陈永金, 刘加珍. 黄河三角洲湿地不同植物群落土壤盐分分布特征. 浙江农业学报, 2018,30(11):1915-1924.
	ZHANG T J, CHEN Y J, LIU J Z. Characteristics of spatial distribution of soil salinity under different plant communities on wetland in Yellow River Delta. Acta Agriculturae Zhejiangensis, 2018,30(11):1915-1924. (in Chinese)
[16]	张雪妮, 吕光辉, 杨晓东, 贡璐, 秦璐, 何学敏, 刘昊奇. 基于盐分梯度的荒漠植物多样性与群落、种间联接响应. 生态学报, 2013,33(18):5714-5722. doi: 10.5846/stxb201306071403
	ZHANG X N, LÜ G H, YANG X D, GONG L, QIN L, HE X M, LIU H Q. Responses of desert plant diversity, community and interspecific association to soil salinity gradient. Acta Ecologica Sinica, 2013,33(18):5714-5722. (in Chinese) doi: 10.5846/stxb201306071403
[17]	安德玉, 邢前国, 赵庚星. 基于HICO波段的滨海土壤盐分遥感反演研究. 海洋学报, 2018,40(6):51-59.
	AN D Y, XING Q G, ZHAO G X. Hyperspectral remote sensing of soil salinity for coastal saline soil in the Yellow River Delta based on HICO bands. Acta Oceanologica Sinica, 2018,40(6):51-59. (in Chinese)
[18]	张素铭, 赵庚星, 王卓然, 肖杨, 郎坤. 滨海盐渍区土壤盐分遥感反演及动态监测. 农业资源与环境学报, 2018,35(4):349-358.
	ZHANG S M, ZHAO G X, WANG Z R, XIAO Y, LANG K. Remote sensing inversion and dynamic monitoring of soil salt in coastal saline area. Journal of Agricultural Resources and Environment, 2018, 35(4):349-358. (in Chinese)
[19]	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)
[20]	周晓红, 张飞, 张海威, 张贤龙, 袁婕. 艾比湖湿地自然保护区土壤盐分多光谱遥感反演模型. 光谱学与光谱分析, 2019,39(4):12 29-1235.
	ZHOU X H, ZHANG F, ZHANG H W, ZHANG X L, YUAN J. A study of soil salinity inversion based on multispectral remote sensing index in Ebinur Lake Wetland Nature Reserve. Spectroscopy and Spectral Analysis, 2019,39(4):1229-1235. (in Chinese)
[21]	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)
[22]	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. 中国农业科学, 2019,52(10):1698-1709. doi: 10.3864/j.issn.0578-1752.2019.10.004
	WANG D Y, CHEN H Y, WANG G F, CONG J Q, WANG X F, WEI X W. Salinity inversion of severe saline soil in the Yellow River estuary based on UAV multi-spectra. Scientia Agricultura Sinica, 2019,52(10):1698-1709. (in Chinese) doi: 10.3864/j.issn.0578-1752.2019.10.004
[23]	HAMZEH S, NASERI A A, ALAVIPANAH S K, MOJARADI B, BARTHOLOMEUS H M, CLEVERS J G P W, BEHZAD M. Estimating salinity stress in sugarcane fields with spaceborne hyperspectral vegetation indices. International Journal of Applied Earth Observation & Geoinformation, 2013,21:282-290. doi: 10.1016/j.jag.2012.07.002
[24]	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)
[25]	姚远, 丁建丽, 张芳, 赵振亮, 江红南. 基于高光谱指数和电磁感应技术的区域土壤盐渍化监测模型. 光谱学与光谱分析, 2013,33(6):1658-1664.
	YAO Y, DING J L, ZHANG F, ZHAO Z L, JIANG H N. Research on model of soil salinization monitoring based on hyperspectral index and EM38. Spectroscopy and Spectral Analysis, 2013,33(6):1658-1664. (in Chinese)
[26]	厉彦玲, 赵庚星, 常春艳, 王卓然, 王凌, 郑佳荣. OLI与HSI影像融合的土壤盐分反演模型. 农业工程学报, 2017,33(21):173-180.
	LI Y L, ZHAO G X, CHENG C Y, WANG Z R, WANG L, ZHENG J R. Soil salinity retrieval model based on OLI and HSI image fusion. Transactions of the Chinese Society of Agricultural Engineering, 2017,33(21):173-180. (in Chinese)
[27]	冯娟, 丁建丽, 杨爱霞, 蔡亮红. 干旱区土壤盐渍化信息遥感建模. 干旱地区农业研究, 2018,36(1):266-273.
	FENG J, DING J L, YANG A X, CAI L H. Remote sensing modeling of soil salinization information in arid areas. Agricultural Research in the Arid Areas, 2018,36(1):266-273. (in Chinese)
[28]	曹肖奕, 丁建丽, 葛翔宇, 王敬哲. 基于光谱指数与机器学习算法的土壤电导率估算研究. 土壤学报, 2020,56(4):867-877.
	CAO X Y, DING J L, GE X Y, WANG J Z. Estimation of soil electrical conductivity based on spectral index and machine learning algorithm. Acta Pedologica Sinica, 2020,56(4):867-877. (in Chinese)
[29]	张智韬, 魏广飞, 姚志华, 谭丞轩, 王新涛, 韩佳. 基于无人机多光谱遥感的土壤含盐量反演模型研究. 农业机械学报, 2019,50(12):151-160.
	ZHANG Z T, WEI G F, YAO Z H, TAN C X, WANG X T, HAN J. Soil salt inversion model based on UAV multispectral remote sensing. Transactions of the Chinese Society for Agricultural Machinery, 2019,50(12):151-160. (in Chinese)
[30]	林芬, 赵庚星, 常春艳, 王卓然, 李慧. 基于相邻轨道图像的冬小麦面积提取及长势分析. 农业资源与环境学报, 2016,33(4):384-389.
	LIN F, ZHAO G X, CHANG C Y, WANG Z R, LI H. Extraction of winter wheat area and growth analysis based on remote sensing imagery of adjacent tracks. Journal of Agricultural Resources and Environment, 2016,33(4):384-389. (in Chinese)
[31]	王卓然, 赵庚星, 高明秀, 常春艳, 姜曙千, 贾吉超, 李晋. 黄河三角洲垦利县夏季土壤水盐空间变异及土壤盐分微域特征. 生态学报, 2016,36(4):1040-1049. doi: 10.5846/stxb201406231296
	WANG Z R, ZHAO G X, GAO M X, CHANG C Y, JIANG S Q, JIA J C, LI J. Spatial variation of soil water and salt and microscopic variation of soil salinity in summer in typical area of the Yellow River Delta in Kenli county. Acta Ecologica Sinica, 2016,36(4):1040-1049. (in Chinese) doi: 10.5846/stxb201406231296
[32]	方孝荣, 高俊峰, 谢传奇, 朱逢乐, 黄凌霞, 何勇. 农作物冠层光谱信息检测技术及方法综述. 光谱学与光谱分析, 2015,35(7):1949-1955. pmid: 26717758
	FANG X R, GAO J F, XIE C Q, ZHU F L, HUANG L X, HE Y. Review of crop canopy spectral information detection technology and methods. Spectroscopy and Spectral Analysis, 2015,35(7):1949-1955 . (in Chinese) pmid: 26717758
[33]	DOUAOUI A E K, NICOLAS H, WALTER C. Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data. Geoderma, 2006,134(1/2):217-230.
[34]	鲍士旦. 土壤农化分析. 北京: 中国农业出版社, 2000.
	BAO S D. Soil and Agricultural Chemistry Analysis. Beijing: China Agricultural Press, 2000. (in Chinese)
[35]	NGUYEN K A, LIOU Y A, TRAN H, HOANG P P, NGUYEN T H. Soil salinity assessment by using nearinfrared channel and vegetation soil salinity index derived from Landsat 8 OLI data: A case study in the Tra Vinh province, Mekong Delta, Vietnam. Progress Earth and Planetary Science, 2020,7(1).
[36]	ALLBED A, KUMAR L, ALDAKHEEL Y Y. Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region. Geoderma, 2014,230/231:1-8. doi: 10.1016/j.geoderma.2014.03.025
[37]	葛哲学, 孙志强. 神经网络理论与MATLAB R2007实现. 北京: 电子工业出版社, 2005.
	GE Z X, SUN Z Q. Neural Network Theory and MATLAB R2007 Realization. Beijing: Publishing House of Electronics Industry, 2005. (in Chinese)
[38]	丁世飞, 齐丙娟, 谭红艳. 支持向量机理论与算法研究综述. 电子科技大学学报, 2011,40(1):2-10.
	DING S F, QI B J, TAN H Y. An overview on theory and algorithm of support vector machines. Journal of University of Electronic Science and Technology of China, 2011,40(1):2-10. (in Chinese)

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光谱指数 Spectral index	计算公式 Computational formula	参考文献 Reference
归一化植被指数NDVI	(b_NIR-b_R)/( b_NIR+b_R)	[32]
比值植被指数RVI	b_NIR/b_R	[32]
差值植被指数DVI	b_NIR-b_R	[32]
绿波段比值植被指数GRVI	b_NIR/b_G	[32]
盐分指数SI	$\sqrt{bG\times bR}$	[33]

土壤盐渍化等级 Soil salinity grade	非盐渍化 Non-salinized	轻度盐渍化 Mild salinized	中度盐渍化 Moderate salinized	重度盐渍化 Severe salinized	盐土 Salinized soil
分级标准 Grade standard (g·kg^-1)	SS<1	1≤SS<2	2≤SS<4	4≤SS<6	SS>6
等级 Grade	1	2	3	4	5

变量 Variable	SS	G	R	REG	NIR
SS	1
G	0.677**	1
R	0.667**	0.506**	1
REG	0.616**	0.431**	0.578**	1
NIR	-0.671**	-0.561**	-0.449**	-0.486**	1

变量 Variable	SS	SI	NDVI	DVI	RVI	GRVI
SS	1
SI	0.770**	1
NDVI	-0.787**	-0.808**	1
DVI	-0.780**	-0.768**	0.953**	1
RVI	-0.560**	-0.696**	0.890**	0.844**	1
GRVI	-0.432**	-0.690**	0.468**	0.557**	0.398**	1

建模方法 Modeling approach	光谱参量 Spectral parameter	估测模型 Estimating model	建模精度 Modeling accuracy		验证精度 Verification accuracy
建模方法 Modeling approach	光谱参量 Spectral parameter	估测模型 Estimating model	R²	RMSE	R²	RMSE
逐步回归 Stepwise regression	b_G	Y=18.609×b_G+0.169	0.458	1.267	0.401	0.882
	b_G,b_R	Y=12.546×b_G+19.044×b_R-1.120	0.599	1.089	0.600	0.729
	b_G,b_R,b_NIR	Y=8.360×b_G+15.775×b_R-9.912×b_NIR +3.116	0.673	0.983	0.650	0.847
	b_G,b_R,b_NIR,b_REG	Y=7.988×b_G+12.282×b_R-8.525×b_NIR+6.979×b_REG+2.101	0.694	0.951	0.648	0.771
	NDVI	Y=-7.507×NDVI+6.308	0.620	1.061	0.668	0.685
	NDVI,RVI	Y=-13.261×NDVI+0.69×RVI+6.734	0.715	0.918	0.692	0.897
	NDVI,RVI,SI	Y=-10.287×NDVI+0.651×RVI+13.486×SI+3.843	0.756	0.850	0.710	0.907
偏最小二乘法 Partial least squares	b_G,b_R,b_NIR,b_REG	Y=6.021×b_G+6.5986×b_R+6.2650×b_NIR-4.1737×b_REG+1.3260	0.689	1.114	0.719	1.177
偏最小二乘法 Partial least squares	NDVI,RVI,SI	Y=-9.4774×NDVI+0.4794×RVI+3.0747×SI+5.0604	0.734	0.954	0.784	0.769
BP神经网络 The BP neural network	b_G,b_R,b_NIR,b_REG	—	—	—	0.714	0.893
BP神经网络 The BP neural network	NDVI,RVI,SI	—	—	—	0.753	0.993
支持向量机 Support vector machine	b_G,b_R,b_NIR,b_REG	—	0.804	0.590	0.467	0.473
支持向量机 Support vector machine	NDVI,RVI,SI	—	0.835	0.353	0.640	0.512

Inversion of Soil Salinity in Coastal Winter Wheat Growing Area Based on Sentinel Satellite and Unmanned Aerial Vehicle Multi-Spectrum— A Case Study in Kenli District of the Yellow River Delta

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土壤盐分等级分布图 Distribution diagram of soil salinity grade	试验区A Test area A					试验区B Test area B
土壤盐分等级分布图 Distribution diagram of soil salinity grade	1	2	3	4	5	1	2	3	4	5
实测图Interpolation diagram	0	0	47.63	36.16	16.21	32.74	0	9.81	57.45	0
反演图Inversion diagram	0	0	47.34	36.38	16.28	31.27	7.84	8.10	50.63	2.16

土壤盐分等级分布图 Distribution diagram of soil salinity grade	1	2	3	4	5
实测图Interpolation diagram	0	63.07	18.28	14.45	4.20
反演图Inversion diagram	0	73.09	14.01	10.08	2.82

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