GNSS-R technique for soil moisture estimation: Framework and software implementation

doi:10.6046/gtzyyg.2017.01.32

Abstract
References
Related Articles
Metrics

Download: PDF(2894 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Soil moisture content estimation is one of the important research fields in the GNSS-R (Global Navigation Satellite System Reflectometry, GNSS-R) land surface remote sensing. In recent years, many experts have done a lot of research on the theories of soil moisture estimation, receiving and processing of GNSS reflected signals, ground-based/air-borne experiment, estimation model and accuracy evaluation, which has greatly promoted the development of GNSS-R land surface remote sensing technique. Based on the previous research results, the authors built the framework of soil moisture estimation using GNSS-R and carried out the initial software implementation by integrating different estimation models. By verifying the models and functions of the software using public datasets for GNSS-R research, it is demonstrated that the software can provide effective technical support for GNSS-R data processing and model validation in soil moisture estimation.

Keywords Procrustes manifold alignment multitemporal hyperspectral image dimension reduction classification

TP79

Issue Date: 23 January 2017

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	LU Jintao
	MA Li

Cite this article:

LU Jintao,MA Li. GNSS-R technique for soil moisture estimation: Framework and software implementation[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 213-220.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.01.32 OR https://www.gtzyyg.com/EN/Y2017/V29/I1/213

[1] 陈书林,刘元波,温作民.卫星遥感反演土壤水分研究综述[J].地球科学进展,2012,27(11):1192-1203 Chen S L,Liu Y B,Wen Z M.Satellite retrieval of soil moisture:An overview[J].Advances in Earth Science,2012,27(11):1192-1203.
[2] 李黄,夏青,尹聪,等.我国GNSS-R遥感技术的研究现状与未来发展趋势[J].雷达学报,2013,04:389-399. Li H,Xia Q,Yin C,et al.The current status of reasearch on GNN-R remote sensing technology in China and future developmeng[J].Journal of Radars,2013,04:389-399.
[3] Masters D,Zavorotny V,Katzberg S,et al.GPS signal scattering from land for moisture content determination[C].Geoscience and Remote Sensing Symposium,2000.Proceedings.IGARSS 2000.IEEE 2000 International.IEEE,2000,7:3090-3092.
[4] Masters D,Axelrad P,Katzberg S.Initial results of land-reflected GPS bistatic radar measurements in SMEX02[J].Remote Sensing of Environment,2004,92(4):507-520.
[5] 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.
[6] Egido A,Paloscia S,Motte E,et al.Airborne GNSS-R polarimetric measurements for soil moisture and above-ground biomass estimation[J].Selected Topics in Applied Earth Observations and Remote Sensing,IEEE Journal of,2014,7(5):1522-1532.
[7] 王迎强,严卫,符养,等.机载GPS反射信号土壤湿度测量技术[J].遥感学报,2009,13(4):678-685. Wang Y Q,Yan W,Fu Y.et al.Soil moisture determination of reflected GPS signals from aircraft platform[J].Journal of Remote Sensing,2009,13(4):678-685.
[8] 万玮,李黄,洪阳.作为外辐射源雷达的GNSS-R遥感多极化问题[J].雷达学报,2014,06:641-651. Wan W,Li H,Hong Y.Issues on multi-polarzation of GNSS-R for passive radar detection[J].Journal of Radars,2014,06:641-651.
[9] Larson K M,Small E E,Gutmann E D,et al.Using GPS multipath to measure soil moisture fluctuations:Initial results[J].GPS Solutions,2008,12(3):173-177.
[10] Zavorotny V U,Larson K M,Braun J J,et al.A physical model for GPS multipath caused by land reflections:Toward bare soil moisture retrievals[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2010,3(1):100-110.
[11] Chew C 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 soil moisture[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(1):537-543.
[12] 敖敏思,胡友健,刘亚东,等.GPS信噪比观测值的土壤湿度变化趋势反演[J].测绘科学技术学报,2012,29(3):140-143. Ao M S,Hu Y J,Liu Y D,et al.Inversion of soil moisture fluctuation based on signal-to-noise ration of global positioning system[J].Journal of Geomatics Science and Technology,2012,29(3):140-143.
[13] 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. Ao M S,Zhu J J,Hu Y J,et al.Comparative experiments on soil moisture moitoring with GPS SNR observations[J].Geomatics and Information Science of Wuhan University,2015,40(1):117-120.
[14] 严颂华,张训械.基于GNSS-R信号的土壤湿度反演研究.电波科学学报,2010,25(1):8-13 Yan S H,Zhang X X.Retrieving soil moisture based on GNSS-R signals[J].Chinese Journal of Ratio Science(in Chinese),2010,25(1):8-13.
[15] 宋学忠,徐爱功,杨东凯,等.GNSS反射信号在土壤湿度测量中的应用[J].测绘通报,2013,11:61-64 Song X Z,Xu A G,Yang D K,et al.Details of soil moisture measuring utilizing GNSS reflected signals[J].Bulletin of Surveying and Mapping,2013,11:61-64.
[16] 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,GE-23(1):25-34.
[17] 毛克彪,王建明,张孟阳,等.GNSS-R信号反演土壤湿度研究分析[J].遥感信息,2009(3):92-96. Mao K B,Wang J M,Zhang M Y,et al.Research on soil moisture inversion by GNSS-R signal[J].Remote Sensing Information,2009(3):92-96.
[18] Ulaby F T,El-Rayes M A.Microwave dielectric spectrum of vegetation-Part two:Dual-dispersion model[J].IEEE Transactions on Geoscience and Remote Sensing,1987,GE-25(5):550-557.
[19] Wan W,Li H,Chen X W,et al.Preliminary calibration of GPS signals and its effects on soil moisture estimation[J].Acta Meteorologica Sinica,2013,02:221-232.
[20] 万玮,李黄,洪阳,等.GNSS-R遥感观测模式及其陆面应用[J].遥感学报,2015.19(6):882-893. Wan W,Li H,Hong Y,et al. Definition and application of GNSS-R observation patterns[J].Tournal of Remote Sensing,2015,19(6):882-893.

[1]	SHI Feifei, GAO Xiaohong, XIAO Jianshe, LI Hongda, LI Runxiang, ZHANG Hao. Classification of wolfberry planting areas based on ensemble learning and multi-temporal remote sensing images[J]. Remote Sensing for Natural Resources, 2022, 34(1): 115-126.
[2]	WU Linlin, LI Xiaoyan, MAO Dehua, WANG Zongming. Urban land use classification based on remote sensing and multi-source geographic data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 127-134.
[3]	QU Haicheng, WAND Yaxuan, SHEN Lei. Hyperspectral super-resolution combining multi-receptive field features with spectral-spatial attention[J]. Remote Sensing for Natural Resources, 2022, 34(1): 43-52.
[4]	LI Yuan, WU Lin, QI Wenwen, GUO Zhengwei, LI Ning. A SAR image classification method based on an improved OGMRF-RC model[J]. Remote Sensing for Natural Resources, 2021, 33(4): 98-104.
[5]	FAN Yinglin, LOU Debo, ZHANG Changqing, WEI Yingjuan, JIA Fudong. Information extraction technologies of iron mine tailings based on object-oriented classification: A case study of Beijing-2 remote sensing images of the Qianxi Area, Hebei Province[J]. Remote Sensing for Natural Resources, 2021, 33(4): 153-161.
[6]	GAO Wenlong, ZHANG Shengwei, LIN Xi, LUO Meng, REN Zhaoyi. The remote sensing-based estimation and spatial-temporal dynamic analysis of SOM in coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(4): 235-242.
[7]	WANG Hua, LI Weiwei, LI Zhigang, CHEN Xueye, SUN Le. Hyperspectral image classification based on multiscale superpixels[J]. Remote Sensing for Natural Resources, 2021, 33(3): 63-71.
[8]	WANG Rong, ZHAO Hongli, JIANG Yunzhong, HE Yi, DUAN Hao. Application and analyses of texture features based on GF-1 WFV images in monthly information extraction of crops[J]. Remote Sensing for Natural Resources, 2021, 33(3): 72-79.
[9]	JIANG Xiao, ZHONG Chang, LIAN Zheng, WU Liangting, SHAO Zhitao. Research progress on classification criterion of geological information products based on satellite remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(3): 279-283.
[10]	LIU Yongmei, FAN Hongjian, GE Xinghua, LIU Jianhong, WANG Lei. Estimation accuracy of fractional vegetation cover based on normalized difference vegetation index and UAV hyperspectral images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 11-17.
[11]	BAI Junlong, WANG Zhangqiong, YAN Haitao. A K-means clustering-guided threshold-based approach to classifying UAV remote sensed images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 114-120.
[12]	LING Xiao, LIU Jiamei, WANG Tao, ZHU Yueqin, YUAN Lingling, CHEN Yangyang. Application of information value model based on symmetrical factors classification method in landslide hazard assessment[J]. Remote Sensing for Land & Resources, 2021, 33(2): 172-181.
[13]	HAN Yanling, CUI Pengxia, YANG Shuhu, LIU Yekun, WANG Jing, ZHANG Yun. Classification of hyperspectral image based on feature fusion of residual network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 11-19.
[14]	MENG Qing, BAI Hongying, ZHAO Ting, GUO Shaozhuang, QI Guizeng. The eco-barrier effect of Qinling Mountain on aerosols[J]. Remote Sensing for Land & Resources, 2021, 33(1): 240-248.
[15]	XU Yun, XU Aiwen. Classification and detection of cloud, snow and fog in remote sensing images based on random forest[J]. Remote Sensing for Land & Resources, 2021, 33(1): 96-101.

Viewed

Full text

Abstract

Cited

Shared

Discussed