سابِق|تال

Rolling estimation model of soil moisture based on multi-satellite fusion

  • role： First author第一作者
  • Affiliation：

    College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China

    Research Center of Precise Engineering Surveying, Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China

  • Email：lyjayq@glut.edu.cn
  • Introduction：梁月吉，1988年生，男，讲师，研究方向为GNSS遥感技术及土壤湿度分析。E-mail：lyjayq@glut.edu.cn
LIANG Yueji12,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China

    Research Center of Precise Engineering Surveying, Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China

  • Email：renchao@glut.edu.cn
  • Introduction：任超，1974年生，男，教授，研究方向为GPS高精度数据处理及其应用。E-mail：renchao@glut.edu.cn
REN Chao12*,  
  • Affiliation：

    College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China

HUANG Yibang1,  
  • Affiliation：

    College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China

    Research Center of Precise Engineering Surveying, Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China

WANG Haoyu12,  
  • Affiliation：

    College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China

    Research Center of Precise Engineering Surveying, Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China

LU Xianjian12,  
  • Affiliation：

    College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China

    Research Center of Precise Engineering Surveying, Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China

YAN Hongbo12

ملخص

Soil moisture content is an important parameter in hydrology, meteorology, and agriculture and is vital for meteorological forecast, flood disaster, and water resource cycle. Global positioning system interferometric reflectometry is a new remote sensing technique with low cost and high efficiency and resolution. This technique can be used to estimate near-surface soil moisture for the area surrounding the antenna from signal-to-noise ratio (SNR) data. In this study, a non-linear sliding estimation method of soil moisture based on multi-satellite fusion was proposed in consideration of the advantages of multi-satellite convergence and the time and space scale of soil moisture. First, the direct and reflection signals of GPS satellites were separated by means of low-order polynomial fitting. Then, the sinusoidal fitting model of reflection signals was established, and the relative delay phase of the SNR interferogram was obtained. Finally, a linear regression model was used to analyze and select the phase of SNR interferogram, and the sliding estimation method of the soil moisture using the least square support vector machine based on multi-system fusion was established. On the basis of the monitoring data provided by US Plate Boundary Observations Project, the feasibility and effectiveness of using single and multiple GPS satellites for sliding estimation of soil moisture were compared and analyzed. Results of the two types of data showed that the linear regression equation could efficiently describe the relationship between relative retardation phase and soil moisture and effectively select GPS satellites by setting the threshold range of correlation coefficient. The optimal parameters of least square support vector machine were selected by grid search method. Over-fitting did not occur in the process of multi-star fusion inversion, and the advantage of non-linear weight determination was fully exerted. When a single satellite was used for soil moisture inversion, the variation law of soil moisture was inaccurately obtained, and the error of inversion error fluctuated greatly, thereby resulting in the jump phenomenon. When the rolling multi-star fusion inversion model was used, the fluctuation of inversion error was relatively close, thereby effectively suppressing the transition phenomenon. The correlation coefficients between the estimated results and the measured values of soil moisture were 0.942 and 0.962, respectively. The root mean square errors were 0.072 and 0.032, which were at least 18.18% higher than those of some single satellites. The theoretical analysis and experiment showed that this method had fully utilized the advantages of least square support vector machine and effectively integrated the performance of each satellite. Overall, the method required less modeling data, the sliding mode could achieve long time estimation, and the estimation error was relatively stable. This method not only ensured the stability of the local error in the estimation process but also effectively restrained the abnormal jump phenomenon easily when single satellite was estimated. The inversion process was not easily affected by a single satellite. Therefore, the estimation of soil humidity can be treated as non-linear event, and multi-system fusion estimation is feasible and effective.

مفهوم

remote sensing;GPS-IR;soil moisture;multi-satellites fusion;least squares support vector machine;estimated accuracy

References

  1. 1.
    Ao M S, Zhu J J, Hu Y J, Zeng Y and Liu Y D. 2015. Comparative experiments on soil moisture monitoring with GPS SNR observations. Geomatics and Information Science of Wuhan University, 40(1): 117–120, 127
  2. 2.
    Bilich A and Larson K M. 2007. Mapping the GPS multipath environment using the signal‐to‐noise ratio (SNR). Radio Science, 42(6): RS6003
  3. 3.
    Bishop C M. 2006. Pattern Recognition and Machine Learning. New York: Springer
  4. 4.
    Cardellach E, Fabra F, Rius A, Pettinato S and D’Addio S. 2012. Characterization of dry-snow sub-structure using GNSS reflected signals. Remote Sensing of Environment, 124: 122–134
  5. 5.
    Chew C, Small E E and Larson K M. 2016. An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil. GPS Solutions, 20(3): 525–537
  6. 6.
    Chew C C, Small E E, Larson K M and Zavorotny V U. 2015. Vegetation sensing using GPS-interferometric reflectometry: theoretical effects of canopy parameters on signal-to-noise ratio data. IEEE Transactions on Geoscience and Remote Sensing, 53(5): 2755–2764
  7. 7.
    Dobson M C, Ulaby F T, Hallikainen M T and El-Rayes M A. 1985. Microwave dielectric behavior of wet soil-Part II: dielectric mixing models. IEEE Transactions on Geoscience and Remote Sensing, GE-23(1): 35–46
  8. 8.
    Ferrazzoli P, Guerriero L, Pierdicca N and Rahmoune R. 2011. Forest biomass monitoring with GNSS-R: theoretical simulations. Advances in Space Research, 47(10): 1823–1832
  9. 9.
    Hallikainen M T, Ulaby F T, Dobson M C, El-Rayes M A and Wu L K. 1985. Microwave dielectric behavior of wet soil-part 1: empirical models and experimental observations. IEEE Transactions on Geoscience and Remote Sensing, GE-23(1): 25–34
  10. 10.
    Huang XL, Shi L and Suykens J A K. 2014. Asymmetric least squares support vector machine classifiers. Computational Statistics and Data Analysis, 70: 395–405
  11. 11.
    Jin S G, Zhang Q Y and Qian X D. 2017. New progress and application prospects of Global Navigation Satellite System Reflectometry (GNSS+R). Acta Geodaetica et Cartographica Sinica, 46(10): 1389–1398
  12. 12.
    Johnson M L, Correia J J, Yphantis D A and Halvorson H R. 1981. Analysis of data from the analytical ultracentrifuge by nonlinear least-squares techniques. Biophysical Journal, 36(3): 575–588
  13. 13.
    Larson K M, Braun J J, Small E E, Zavorotny V U, Gutmann E D and Bilich A L. 2010. GPS multipath and its relation to near-surface soil moisture content. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(1): 91–99
  14. 14.
    Larson K M and Nievinski F G. 2013. GPS snow sensing: results from the Earth Scope Plate Boundary Observatory. GPS Solutions, 17(1): 41–52
  15. 15.
    Le Hégarat-Mascle S, Zribi M, Alem F, Weisse A and Loumagne C. 2002. Soil moisture estimation from ERS/SAR data: toward an operational methodology. IEEE Transactions on Geoscience and Remote Sensing, 40(12): 2647–2658
  16. 16.
    Martin-Neira M. 1993. A passive reflectometry and interferometry system (PARIS): application to ocean altimetry. ESA Journal, 17(4): 331–355
  17. 17.
    Njoku E G and Entekhabi D. 1996. Passive microwave remote sensing of soil moisture. Journal of Hydrology, 184(1/2): 101–129
  18. 18.
    Peng X F, Wan W, Li F and Chen X W. 2017. The suitability analysis of soil moisture retrieval using GNSS-R technology. Journal of Remote Sensing, 21(3): 341–350
  19. 19.
    Powell M J D and Yuan Y. 1990. A trust region algorithm for equality constrained optimization. Mathematical Programming, 49(1/3): 189–211
  20. 20.
    Rodriguez-Alvarez N, Bosch-Lluis X, Camps A, Vall-Llossera M, Valencia E, Marchan-Hernandez J F and Ramos-Perez I. 2009. Soil moisture retrieval using GNSS-R techniques: Experimental results over a bare soil field. IEEE Transactions on Geoscience and Remote Sensing, 47(11): 3616–3624
  21. 21.
    Sabater J M, Rüdiger C, Calvet J C, Fritz N, Jarlan L and Kerr Y. 2008. Joint assimilation of surface soil moisture and LAI observations into a land surface model. Agricultural and Forest Meteorology, 148(8/9): 1362–1373
  22. 22.
    Suykens J A K and Vandewalle J. 1999. Least squares support vector machine classifiers. Neural Processing Letters, 9(3): 293–300
  23. 23.
    Tsang L, Kong J A and Shin R T. 1985. Theory of Microwave Remote Sensing. London: Wiley-Interscience
  24. 24.
    Wu X R, Li Y and Xu J. 2012. Theoretical study on GNSS-R vegetation biomass//Proceedings of 2012 IEEE International Geoscience and Remote Sensing Symposium. Munich, Germany: IEEE: 6380-6383 [DOI: 10.1109/IGARSS.2012.6352719
  25. 25.
    Xie S F, Liang Y J, Zheng Z T and Liu H F. 2017. Combined forecasting method of landslide deformation based on MEEMD, approximate entropy, and WLS-SVM. ISPRS International Journal of Geo-Information, 6(1): 5
  26. 26.
    Yan S H, Gong J Y, Zhang X X and Li D X. 2011. Ground based GNSS-R observations for soil moisture. Chinese Journal of Geophysics, 54(11): 2735–2744
  27. 27.
    Yang L X, Yang S Y, Li S J, Zhang R, Liu F and Jiao L C. 2015. Coupled compressed sensing inspired sparse spatial-spectral LSSVM for hyperspectral image classification. Knowledge-Based Systems, 79: 80–89
  28. 28.
    Ye X F. 2016. Research on Processing Method of GNSS Station Environment Error and Its Application Using SNR Data. Wuhan: China University of Geosciences (Wuhan).
  29. 29.
    Zavorotny V U, Larson K M, Braun J J, Small E E, Gutman E D and Bilich A L. 2010. A physical model for GPS multipath caused by land reflections: toward bare soil moisture retrievals. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(1): 100–110
  30. 30.
    Zhang S C, Dai K Y, Liu K, Hou X W and Zhao Y H. 2016. Research of GPS-MR on snow depth monitoring. Progress in Geophysics, 31(4): 1879–1884

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website