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2020-11-14

Capability of NavIC , an Indian GNSS Constellation, for Retrieval of Surface Soil Moisture

By Vivek Chamoli, Rishi Prakash, Anurag Vidyarthi, and Ananya Ray
Progress In Electromagnetics Research C, Vol. 106, 255-270, 2020
doi:10.2528/PIERC20090904

Abstract

Study of Global Navigation Satellite System (GNSS) for various non-navigational applications is gaining importance day by day. Very recently, India's Navigation with Indian Constellation (NavIC) is a new entry in GNSS systems available worldwide such as GPS, GLONASS, Galileo and Beidou. One of the important non-navigational applications is the study of soil moisture with GNSS. NavIC is very much different from widely used and globally available GPS system. Therefore, in this paper we have analyzed and developed an algorithm for soil moisture retrieval with NavIC Carrier to Noise (C/No) ratio. Information of soil moisture is very beneficial for various applications such as groundwater estimation, management of agricultural, drought monitoring and prediction, weather forecasting and flood forecasting. Amplitude of multipath Carrier to Noise (C/No) ratio from the NavIC receiver at L−band has been utilized to determine the soil moisture from the smooth bare soil surface. The analyses of sensitivity of soil moisture have been carried out by observing the NavIC multipath data and measurement of in situ soil moisture content. The algorithm development focuses on the retrieval of multipath amplitude from the interference pattern created at the receiver due to direct signal and reflected/multipath signal. The 1st, 2nd, and 3rd order polynomials have been analyzed to detrend the signal before fitting it with sinusoidal variation. It was observed that the multipath amplitude retrieved after detrending the C/No data with the 1st order polynomial provides better correlation with observed soil moisture than the 2nd and 3rd order polynomials. An empirical relationship between multipath amplitude and soil moisture has been developed. This developed empirical relationship is capable of providing soil moisture with known multipath amplitude. The retrieved soil moisture with developed algorithm is in good agreement with observed soil moisture with RMSE of 1.43%. Obtained results indicate the promising potential for the estimation of soil moisture with NavIC C/No ratio.

Citation


Vivek Chamoli, Rishi Prakash, Anurag Vidyarthi, and Ananya Ray, "Capability of NavIC , an Indian GNSS Constellation, for Retrieval of Surface Soil Moisture," Progress In Electromagnetics Research C, Vol. 106, 255-270, 2020.
doi:10.2528/PIERC20090904
http://jpier.org/PIERC/pier.php?paper=20090904

References


    1. Prakash, R., D. Singh, and N. P. Pathak, "Microwave specular scattering response of soil texture at X-band," Adv. Space Res., Vol. 44, No. 7, 801-814, 2009.
    doi:10.1016/j.asr.2009.05.016

    2. Wan, W., H. Li, X. Chen, P. Luo, and J. Wan, "Preliminary calibration of GPS signals and its effects on soil moisture estimation," Acta Meteor. Sinica, Vol. 27, No. 2, 221-232, 2013.
    doi:10.1007/s13351-013-0207-7

    3. Phillips, A. J., N. K. Newlands, S. H. Liang, and B. H. Ellert, "Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support," Comput. Electron. Agr., Vol. 107, 73-88, 2014.
    doi:10.1016/j.compag.2014.02.011

    4. Liang, W. L., F. X. Hung, M. C. Chan, and T. H. Lu, "Spatial structure of surface soil water content in a natural forested headwater catchment with a subtropical monsoon climate," J. Hydrol., Vol. 516, 210-221, 2014.
    doi:10.1016/j.jhydrol.2014.01.032

    5. Tabibi, S., F. G. Nievinski, T. van Dam, and J. F. Monico, "Assessment of modernized GPS L5 SNR for ground-based multipath reflectometry applications," Adv. Space Res., Vol. 55, No. 4, 1104-1116, 2015.
    doi:10.1016/j.asr.2014.11.019

    6. Zhang, D., Z. L. Li, R. Tang, B. H. Tang, H. Wu, J. Lu, and K. Shao, "Validation of a practical normalized soil moisture model with in situ measurements in humid and semi-arid regions," Int. J. Remote Sens., Vol. 36, No. 19–20, 5015-5030, 2015.
    doi:10.1080/01431161.2015.1055610

    7. El Hajj, M., N. Baghdadi, M. Zribi, G. Belaud, B. Cheviron, D. Courault, and F. Charron, "Soil moisture retrieval over irrigated grassland using X-band SAR data," Remote Sensing of Environment, Vol. 176, 202-218, 2016.
    doi:10.1016/j.rse.2016.01.027

    8. Liao, W., D. Wang, G. Wang, Y. Xia, and X. Liu, "Quality control and evaluation of the observed daily data in the north american soil moisture database," J. Meteor. Res., Vol. 33, No. 3, 501-518, 2019.
    doi:10.1007/s13351-019-8121-2

    9. Pitman, A. J., "The evolution of, and revolution in, land surface schemes designed for climate models," Int. J. Climatol., Vol. 23, No. 5, 479-510, 2003.
    doi:10.1002/joc.893

    10. Istanbulluoglu, E. and R. L. Bras, "On the dynamics of soil moisture, vegetation, and erosion: Implications of climate variability and change," Water Resour. Res., Vol. 42, No. 6, 1-17, 2006.
    doi:10.1029/2005WR004113

    11. Rodriguez-Alvarez, N., A. Camps, M. Vall-Llossera, X. Bosch-Lluis, A. Monerris, I. Ramos- Perez, E. Valencia, J. F. Marchan-Hernandez, J. Martinez-Fernandez, G. Baroncini-Turricchia, and C. Perez-Gutierrez, "Land geophysical parameters retrieval using the interference pattern GNSS-R technique," IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 1, 71-84, 2011.
    doi:10.1109/TGRS.2010.2049023

    12. Bogena, H. R., J. A. Huisman, C. Oberdorster, and H. Vereecken, "Evaluation of a low-cost soil water content sensor for wireless network applications," J. Hydrol., Vol. 344, No. 1–2, 32-42, 2007.
    doi:10.1016/j.jhydrol.2007.06.032

    13. Ledieu, J., P. De Ridder, P. De Clerck, and S. Dautrebande, "A method of measuring soil moisture by time-domain reflectometry," J. Hydrol., Vol. 88, No. 3–4, 319-328, 1986.
    doi:10.1016/0022-1694(86)90097-1

    14. Robinson, D. A., S. B. Jones, J. M. Wraith, D. Or, and S. P. Friedman, "A review of advances in dielectric and electrical conductivity measurement in soils using time domain reflectometry," Vadose Zone J., Vol. 2, No. 4, 444-475, 2003.
    doi:10.2136/vzj2003.4440

    15. Cristi, F., V. Fierro, F. Suarez, J. F. Munoz, and M. B. Hausner, "A TDR-waveform approach to estimate soil water content in electrically conductive soils," Comput. Electron. Agr., No. 121, 160-168, 2016.

    16. Elder, A. N. and T. C. Rasmussen, "Neutron probe calibration in unsaturated tuff," Soil Sci. Soc. Am. J., Vol. 58, No. 5, 1301-1307, 1994.

    17. Li, J., D. W. Smith, and S. G. Fityus, "The effect of a gap between the access tube and the soil during neutron probe measurements," Soil Res., Vol. 41, No. 1, 151-164, 2003.

    18. Sreedeep, S., A. C. Reshma, and D. N. Singh, "Measuring soil electrical resistivity using a resistivity box and a resistivity probe," Geotech. Test. J., Vol. 27, No. 4, 411-415, 2004.

    19. Masters, D., P. Axelrad, and S. Katzberg, "Initial results of land-reflected GPS bistatic radar measurements in SMEX02," Remote Sensing of Environment, Vol. 92, No. 4, 507-520, 2004.

    20. Camps, A., "Spatial resolution in GNSS-R under coherent scattering," IEEE Geoscience and Remote Sensing Letters, Vol. 17, No. 1, 32-36, 2019.

    21. Gleason, S., A. O’Brien, A. Russel, M. M. Al-Khaldi, and J. T. Johnson, "Geolocation, calibration and surface resolution of CYGNSS GNSS-R land observations," Remote Sensing, Vol. 12, No. 8, 1317, 2020.

    22. Balakhder, A. M., M. M. Al-Khaldi, and J. T. Johnson, "On the coherency of ocean and land surface specular scattering for GNSS-R and signals of opportunity systems," IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, No. 12, 10426-10436, 2019.

    23. Larson, K. M., E. E. Small, E. D. Gutmann, A. L. Bilich, J. J. Braun, and V. U. Zavorotny, "Use of GPS receivers as a soil moisture network for water cycle studies," Geophys. Res. Lett., Vol. 35, No. 24, 1-5, 2008.

    24. Katzberg, S. and J. Garrison, "The application of reflected GPS signals to ocean and wetland remote sensing," Proc. 5th Int. Conf. Remote Sens. Mar. Coastal Environ., Vol. 1, 522-529, 1998.

    25. Katzberg, S., O. Torres, M. Grant, and D. Masters, "Utilizing calibrated GPS reflected signals to estimate oil reflectivity and dielectric constant: Results from SMEX02," Remote Sensing of Environment, Vol. 100, 17-28, 2006.

    26. Larson, K. M., J. J. Braun, E. E. Small, V. U. Zavorotny, E. D. Gutmann, and A. L. Bilich, "GPS multipath and its relation to near-surface soil moisture content," IEEE J-STARS, Vol. 3, No. 1, 91-99, 2010.

    27. Roussel, N., F. Frappart, G. Ramillien, J. Darrozes, F. Baup, L. Lestarquit, and M. C. Ha, "Detection of soil moisture variations using GPS and GLONASS SNR data for elevation angles ranging from 2 to 70," IEEE J-STARS, Vol. 9, No. 10, 4781-4794, 2016.

    28. Yang, T., W. Wan, X. Chen, T. Chu, and Y. Hong, "Using BDS SNR observations to measure near-surface soil moisture fluctuations: Results from low vegetated surface," IEEE Transactions on Geoscience and Remote Sensing, Vol. 14, No. 8, 1308-1312, 2017.

    29. Han, M., Y. Zhu, D. Yang, X. Hong, and S. Song, "A semi-empirical SNR model for soil moisture retrieval using GNSS SNR data," Remote Sensing, Vol. 10, No. 2, 1-19, 2018.

    30. Egido, A., M. Caparrini, G. Ruffini, S. Paloscia, E. Santi, L. Guerriero, N. Pierdicca, and N. Floury, "Global navigation satellite systems reflectometry as a remote sensing tool for agriculture," Remote Sensing, Vol. 4, No. 8, 2356-2372, 2012.

    31. Chew, C. C., E. E. Small, K. M. Larson, and V. U. Zavorotny, "Effects of near-surface soil moisture on GPS SNR data: Development of a retrieval algorithm for soil moisture," IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 1, 537-543, 2015.

    32. Small, E. E., K. M. Larson, and J. J. Braun, "Sensing vegetation growth with reflected GPS signals," Geophys. Res. Lett., Vol. 37, No. 12, 1-5, 2010.

    33. Vey, S., A. Guntner, J. Wickert, T. Blume, and M. Ramatschi, "Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: A case study for Sutherland, South Africa," Gps Solut., Vol. 20, No. 4, 641-654, 2016.

    34. Wan, W., K. M. Larson, E. E. Small, C. C. Chew, and J. J. Braun, "Using geodetic GPS receivers to measure vegetation water content," Gps Solut., Vol. 9, No. 2, 237-248, 2015.

    35. Larson, K. M., E. D. Gutmann, V. U. Zavorotny, J. J. Braun, M. W. Williams, and F. G. Nievinski, "Can we measure snow depth with GPS receivers?," Geophys. Res. Lett., Vol. 36, No. 17, 1-5, 2009.

    36. Larson, K. M. and E. E. Small, "Normalized microwave reflection index: A vegetation measurement derived from GPS networks," IEEE J-STARS, Vol. 7, No. 5, 1501-1511, 2014.

    37. Zavorotny, V. U., K. M. Larson, J. J. Braun, E. E. Small, E. D. Gutmann, and A. L. Bilich, "A physical model for GPS multipath caused by land reflections: Toward bare soil moisture retrievals," IEEE J-STARS, Vol. 3, No. 1, 100-110, 2010.

    38. Chamoli, V., R. Prakash, A. Vidyarthi, and A. Ray, "Sensitivity of NavIC signal for soil moisture variation," International Conference on Emerging Trends in Computing and Communication Technologies, 1-4, IEEE, 2017.

    39. Chamoli, V., R. Prakash, A. Vidyarthi, and A. Ray, "Analysis of NavIC multipath signal sensitivity for soil moisture in presence of vegetation," International Conference on Innovative Computing and Communications, Vol. 2, 353-364, Springer, Singapore, 2020.

    40. Martin, A., S. Ibanez, C. Baixauli, S. Blanc, and A. Julian, "Multi-constellation GNSS interferometric reflectometry with mass-market sensors as a solution for soil moisture monitoring," Hydrol. Earth Syst. Sci., Vol. 24, 3573-3582, 2020.

    41. Yang, T., W. Wan, and X. Chen, "Land surface characterization using BeiDou signal-to-noise ratio observations," Gps Solut., Vol. 23, 32, 2019.