Retrieval Model for Soil Moisture Content Using GPS-Interferometric Reflectometry

WU Jizhong; WANG Tian; WU Wei

doi:10.13203/j.whugis20160088

Volume 43 Issue 6

Jun. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(6): 887-892. > DOI: 10.13203/j.whugis20160088

WU Jizhong, WANG Tian, WU Wei. Retrieval Model for Soil Moisture Content Using GPS-Interferometric Reflectometry[J]. Geomatics and Information Science of Wuhan University, 2018, 43(6): 887-892. DOI: 10.13203/j.whugis20160088

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Retrieval Model for Soil Moisture Content Using GPS-Interferometric Reflectometry

1.
College of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, China

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The National Natural Science Foundation of China 41504024

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WU Jizhong, PhD, associate professor, specializes in GNSS precise position. E-mail: jzwumail@163.com
Received Date: September 08, 2016
Published Date: June 04, 2018

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Abstract

Abstract

GPS-interferometric reflectometry can be used to infer temporal changes in near surface, soil moisture content. To estimate reliable GPS interferogram metrics such as phase and amplitude, an improved estimation method based on the GPS signal-to-noise ratio (SNR) data is presented, and a retrieval model for soil moisture content is constructed. Experiments indicate that the improved method is capable of estimating accurate and reliable GPS interferogram metrics as compared to the common method. The SNR phase shows a nearly linear relationship to the soil moisture content. Furthermore, a linear retrieval model for soil moisture content can be achieved, and the model is sensitive to consecutive precipitation.
- GPS-interferometric reflectometry,
- reflections,
- soil moisture content,
- parameter estimation,
- linear model,
- signal-to-noise ratio

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[1]	Katzberg S J, Torres O, Grant M S, et al. Utilizing Calibrated GPS Reflected Signals to Estimate Soil Reflectivity and Dielectric Constant:Results from SMEX02[J]. Remote Sensing of Environment, 2005, 100(1):17-28 http://www.sciencedirect.com/science/article/pii/S0034425705002932
[2]	Rodriguez-Alvarez N, Bosch-Lluis X, Camps A, et al. Soil Moisture Retrieval Using GNSS-R Techniques:Experimental Results Over a Bare Soil Field[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(11):3616-3624 doi: 10.1109/TGRS.2009.2030672
[3]	刘经南, 邵连军, 张训械, 等.GNSS-R研究进展及其关键技术[J].武汉大学学报·信息科学版, 2007, 32(11):955-960 http://ch.whu.edu.cn/CN/abstract/abstract2030.shtml Liu Jingnan, Shao Lianjun, Zhang Xunxie, et al. Advances in GNSS-R Studies and Key Technologies[J]. Geomatics and Information Science of Wuhan University, 2007, 32(11):955-960 http://ch.whu.edu.cn/CN/abstract/abstract2030.shtml
[4]	严颂华, 龚健雅, 张训械, 等.GNSS-R测量地表土壤湿度的地基实验[J].地球物理学报, 2011, 51(11):2735-2744 doi: 10.3969/j.issn.0001-5733.2011.11.003 Yan Songhua, Gong Jianya, Zhang Xunxie, et al. Ground Based GNSS-R Observations for Soil Moisture[J]. Chinese Journal of Geophysics, 2011, 54(11):2735-2744 doi: 10.3969/j.issn.0001-5733.2011.11.003
[5]	Alonso-Arroyo A, Camps A, Aguasca A, et al. Dual-polarization GNSS-R Interference Pattern Technique for Soil Moisture Mapping[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(5):1533-1544 doi: 10.1109/JSTARS.2014.2320792
[6]	Wan Wei, Bai Weihua, Zhao Limin, et al. Initial Results of China's GNSS-R Airborne Campaign:Soil Moisture Retrievals[J]. Science Bulletin, 2015, 60(10):964-971 doi: 10.1007/s11434-015-0789-9
[7]	Larson K M, Small E E, Gutmann E, et al. Using GPS Multipath to Measure Soil Moisture Fluctuations:Initial Results[J]. GPS Solutions, 2008, 12(3):173-177 doi: 10.1007/s10291-007-0076-6
[8]	Larson K M, Braun J, Small E E, et al. GPS Multipath and Its Relation to Near-surface Soil Moisture Content[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2010, 3(1):91-99 doi: 10.1109/JSTARS.2009.2033612
[9]	Chew C, Small E E, Larson K M, et al. Effects of Near-surface Soil Moisture on GPS SNR Data:Development of a Retrieval Algorithm for Volumetric Soil Moisture[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1):537-543 doi: 10.1109/TGRS.2013.2242332
[10]	Vey S, Wickert J, Güntner A, et al. Derivation of Near-surface Soil Moisture from the GNSS Site Sutherland, South Africa[C]. Workshop of GNSS-Reflectometry, Brest, France, 2013
[11]	敖敏思, 朱建军, 胡友健, 等.利用SNR观测值进行GPS土壤湿度监测[J].武汉大学学报·信息科学版, 2015, 40(1):117-120 http://ch.whu.edu.cn/CN/abstract/abstract3167.shtml Ao Minsi, Zhu Jianjun, Hu Youjian, et al. Comparative Experiments on Soil Moisture Monitoring with GPS SNR Observations[J]. Geomatics and Information Science of Wuhan University, 2015, 40(1):117-120 http://ch.whu.edu.cn/CN/abstract/abstract3167.shtml
[12]	Nolan M, Fatland D R. Penetration Depth as a DInSAR Observable and Proxy for Soil Moisture[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(3):532-537 doi: 10.1109/TGRS.2003.809931
[13]	Comp C J, Axelrad P. Adaptive SNR-based Carrier Phase Multipath Mitigation Technique[J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(1):264-276 doi: 10.1109/7.640284

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Retrieval Model for Soil Moisture Content Using GPS-Interferometric Reflectometry

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