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遥感技术与应用  2020, Vol. 35 Issue (4): 786-796    DOI: 10.11873/j.issn.1004-0323.2020.4.0786
甘肃遥感学会专栏     
盐渍土介电特性及模型改进研究
董磊磊1,2(),王维真1,3(),吴月茹4
1.中国科学院西北生态环境资源研究院,中国科学院黑河遥感试验研究站,甘肃省遥感重点实验室,甘肃 兰州 730000
2.中国科学院大学,北京 100049
3.中国科学院寒旱区陆面过程与气候变化重点实验室,甘肃 兰州 730000
4.内蒙古农业大学沙漠治理学院,内蒙古 呼和浩特 010018
Dielectric Properties of Saline Soil and an Improved Dielectric Model
Leilei Dong1,2(),Weizhen Wang1,3(),Yueru Wu4
1.Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Chinese Academy of Sciences, Lanzhou 730000, China
4.College of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
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摘要:

基于控制试验对配制的具有不同含水量和含盐量的土壤样本进行介电特征量测和分析,同时结合Stogryn盐水介电模型和土壤溶液离子浓度、电导率、含水量和含盐量等参数对介电常数虚部的影响,将饱和度这个关键参量引入盐渍土介电模型中,提高盐渍土介电模型模拟精度。结果表明:①当土壤体积含水量较低时,土壤含盐量对介电常数的实部和虚部均未产生明显的作用。当土壤体积含水量较高时,介电常数实部则随着土壤含盐量的增加呈逐渐下降趋势,介电常数虚部以增加态势为主;②修正后的盐渍土介电模型可以较好地刻画介电常数变化特征。此外,将修正后的模型在白银采样点进行验证,同样取得较好的模拟结果,表明修正后的盐渍土介电模型对不同土壤类型具有一定的适用性。

关键词: 改进的介电模型介电常数饱和度土壤水分土壤盐分    
Abstract:

The measurement and analysis of the dielectric constant of soil samples with different moisture and salinity are achieved based control experiment. The saturation is introduced to the dielectric model of salinity soil to improve simulation accuracy by taking the Stogryn model and the influence of soil solution ion concentration, conductivity, moisture content, and salt content for dielectric constant imaginary part into consideration. The results indicate that the soil salt content has little influence on both real part and imaginary part of dielectric constant when soil volumetric moisture content is low, while soil volumetric moisture content is high, the real part of the dielectric constant decreases with the increase of soil salt content, and the imaginary part of the dielectric constant increases. The improved dielectric model of salinity soil can well reveal the changes of dielectric constant, and it is also having a great effect in Baiyin soil samples. That is to say, the improved dielectric model can apply to different soil types.

Key words: Improved dielectric model    Dielectric constant    Saturation    Soil moisture    Soil salinity
收稿日期: 2019-09-23 出版日期: 2020-09-15
ZTFLH:  S153  
基金资助: 国家自然科学基金项目(41671373);中国科学院寒旱区陆面过程与气候变化重点实验室自主研究课题(LPCC2019)
通讯作者: 王维真     E-mail: dongll@lzb.ac.cn;weizhen@lzb.ac.cn
作者简介: 董磊磊(1990-),男,甘肃秦安人,博士研究生,主要从事定量遥感研究。E?mail: dongll@lzb.ac.cn
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引用本文:

董磊磊,王维真,吴月茹. 盐渍土介电特性及模型改进研究[J]. 遥感技术与应用, 2020, 35(4): 786-796.

Leilei Dong,Weizhen Wang,Yueru Wu. Dielectric Properties of Saline Soil and an Improved Dielectric Model. Remote Sensing Technology and Application, 2020, 35(4): 786-796.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.4.0786        http://www.rsta.ac.cn/CN/Y2020/V35/I4/786

图1  土壤样本采集位置示意图
图2  不同含水量时土壤介电常数实部随含盐量增加的变化
图3  不同含水量时土壤介电常数虚部随含盐量增加的变化
图4  盐渍化土壤溶液离子浓度与电导率关系
图5  不同土壤样本介电常数实部实测值与模拟值对比
图6  不同土壤样本介电常数虚部实测值与模拟值对比
图7  不同盐渍土介电模型虚部模拟结果对比
1 Allbed A, Kumar L. Soil Salinity Mapping and Monitoring in Arid and Semi-Arid Regions Using Remote Sensing Technology: A Review[J]. Advances in Remote Sensing, 2013,2(4): 373-385.
2 Muyen Z, Moore G A, Wrigley R J. Soil Salinity and Sodicity Effects of Wastewater Irrigation in South East Australia[J]. Agricultural Water Management, 2011, 99(1): 33-41.
3 Li Jianguo, Pu Lijie, Zhu Ming, et al. The Present Situation and Hot Issues in the Salt-affected Soil[J]. Acta Geographica Sinica, 2012, 67(9): 1233-1245.
3 李建国, 濮励杰, 朱明, 等. 土壤盐渍化研究现状及未来研究热点[J]. 地理学报, 2012, 67(9): 1233-1245.
4 Fan X, Pedroli B, Liu G, et al. Soil Salinity Development in the Yellow River Delta in Relation to Groundwater Dynamics[J]. Land Degradation and Development, 2012, 23(2): 175-189.
5 Csillag F, Pásztor László, Biehl L L. Spectral Band Selection for the Characterization of Salinity Status of Soils[J]. Remote Sensing of Environment, 1993, 43(3): 231-242.
6 Evans F H, Caccetta P A. Broad-scale Spatial Prediction of Areas at Risk from Dryland Salinity[J]. Surveyor, 2000, 29(2): 33-40.
7 Wu W, Waleed M. Al-Shafie,et al. Soil Salinity Mapping by Multiscale Remote Sensing in Mesopotamia, Iraq[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(11): 4442-4452.
8 Kalra N K, Joshi D C. Potentiality of Landsat, SPOT and IRS Satellite Imagery, for Recognition of Salt Affected Soils in Indian Arid Zone[J]. International Journal of Remote Sensing, 1996, 17(15): 3001-3014.
9 Engman, Edwin T. Progress in Microwave Remote Sensing of Soil Moisture[J]. Canadian Journal of Remote Sensing, 1990, 16(3): 6-14.
10 Njoku E G, Jackson T J, Lakshmi V, et al. Soil Moisture Retrieval from AMSR-E[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(2): 215-229.
11 Zhao Tianjie. Recent Advances of L-band Application in the Passive Microwave Remote Sensing of Soil Moisture and Its Prospects[J]. Progress in Geography, 2018, 37(2): 198-213.
11 赵天杰. 被动微波反演土壤水分的L波段新发展及未来展望[J]. 地理科学进展, 2018, 37(2): 198-213.
12 Shi J C, Du Y, Du J Y, et al. Progresses on Microwave Remote Sensing of Land Surface Parameters[J]. Science China Earth Sciences, 2012, 55(7): 1052-1078.
13 Mao Kebiao, Tang Huajun, Zhou Qingbo, et al. A Survey of Soil Moisture Retrieval by Passive Microwave Remote Sensing[J]. Remote Sensing Technology and Application, 2007, 22(3): 466-470.
13 毛克彪, 唐华俊, 周清波, 等. 被动微波遥感土壤水分反演研究综述[J]. 遥感技术与应用, 2007, 22(3): 466-470.
14 Kolassa J, Gentine P, Prigent C, et al. Soil Moisture Retrieval from AMSR-E and ASCAT Microwave Observation Synergy. Part 2: Product Evaluation[J]. Remote Sensing of Environment, 2017, 195(6): 202-217.
15 Draper C S, Walker J P, Steinle P J, et al. An Evaluation of AMSR–E Derived Soil Moisture over Australia[J]. Remote Sensing of Environment, 2009, 113(4): 703-710.
16 Zhao S, Wu Y, Liu S, et al. Dielectric Properties of Saline Soils and an Improved Dielectric Model in C-Band[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(1): 440-452.
17 Tashpolat N, Ding J, Yu D. Dielectric Properties of Saline Soil based on a Modified Dobson Dielectric Model[J]. Journal of Arid Land, 2015, 7(5): 696-705.
18 Stogryn A. Equations for Calculating the Dielectric Constant of Saline Water [J]. IEEE Transactions on Microwave Theory and Techniques, 1971, 19(8): 733-736.
19 Klein L, Swift C. An Improved Model for the Dielectric Constant of Sea Water at Microwave Frequencies[J]. IEEE Transactions on Antennas and Propagation, 1977, 25(1): 104-111.
20 De Loor G P. Dielectric Properties of Heterogeneous Mixtures Containing Water. The Journal of Microwave Power, 1968, 3(2): 67-71.
21 Birchak J R, Gardner C G, Hipp J E,et al. High Dielectric Constant Microwave Probes for Sensing Soil Moisture[J]. Proceedings of the IEEE, 1974, 62(1): 93-98.
22 Wang J R. The Dielectric Properties of Soil‐Water Mixtures at Microwave Frequencies[J]. Radio Science, 1980, 15(5): 977-985.
23 Wobschall D. A Theory of the Complex Dielectric Permittivity of Soil Containing Water: The Semi-disperse Model[J]. IEEE Transactions on Geoscience Electronics, 1977, 15(1): 49-58.
24 Wang J R. Schmugge T J. An Empirical Model for the Complex Dielectric Permittivity of Soils as a Function of Water Content[J]. IEEE Transactions on Geoscience and Remote Sensing, 1980, 18(4): 288-295.
25 Dobson M C. Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric-Mixing Models[J]. IEEE Transactions on Geoscience and Remote Sensing, 1985, 23(1): 35-46.
26 Hallikainen M T, Ulaby F T, Dobson M C, et al. Microwave Dielectric Behavior of Wet Soil-Part 1: Empirical Models and Experimental Observations[J]. IEEE Transactions on Geoscience and Remote Sensing, 1985, 23(1): 25-34.
27 Topp G C, Davis J L, Annan A P. Electromagnetic Determination of Soil Water Content: Measurements in Coaxial Transmission Lines[J]. Water Resources Research, 1980, 16(3): 574-582.
28 Mironov V L, Dobson M C, Kaupp V H, et al. Generalized Refractive Mixing Dielectric Model for Moist Soils[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(4): 773-785.
29 Sreenivas K, Venkataratnam L, Narasimha P V. Dielectric Properties of Salt-Affected Soils[J] International Journal of Remote Sensing. 1995, 16(4): 641-649.
30 Shao Yun, Lü Yuan, Dong Qing. Study on Soil Microwave Dielectric Characteristic as Salinity and Water Content[J]. Journal of Remote Sensing, 2002, 6(6): 416-423.
30 邵芸, 吕远, 董庆, 等. 含水含盐土壤的微波介电特性分析研究[J]. 遥感学报, 2002, 6(6): 416-423.
31 McColl K A, Ryu D, Matic V, et al. Soil Salinity Impacts on L-Band Remote Sensing of Soil Moisture[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(2): 262-266.
32 Hu Qingrong. Studies on Microwave Dielectric Behavior of Moist Salt Soil and Its Effect on Backscattering Coefficients Extracted from Radar Image[D]. Beijing: Institute of Remote Sensing Applications, Chinese Academy of Sciences, 2003.
32 胡庆荣. 含水含盐土壤介电特性实验研究及对雷达图像的响应分析[D]. 北京:中国科学院遥感应用研究所, 2003.
33 Wu Yueru. Quantitative Retrieval of Dielectric Properties and Salt Content of Saline Soil by Microwave Remote Sensing[D]. Beijing: University of Chinese Academy of Sciences, 2012.
33 吴月茹. 盐渍土介电特性及其含盐量的微波遥感定量反演研究[D]. 北京:中国科学院大学, 2012.
34 Rhoades J D, Manteghi N A, Shouse P J, et al. Soil Electrical Conductivity and Soil Salinity: New Formulation and Calibration[J]. Soil Science Society of American Journal, 1989, 53(2): 433-439.
35 Li Bin, Wang Zhichun, Chi Chunming. The Relationship between Salt Content and Electric Conductivity of Soda Solonetz in Daan City[J]. Agricultural Research in the Arid Areas, 2006, 24(4): 168-171.
35 李彬, 王志春, 迟春明. 吉林省大安市苏打碱土含盐量与电导率的关系[J]. 干旱地区农业研究, 2006, 24(4): 168-171.
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