ESTIMATION PERFORMANCE OF SOIL MOISTURE WITH GPS-IR METHOD

Abstract

This study aims to retrieve soil moisture from Global Positioning System (GPS) Signal-to-Noise Ratio (SNR) data with varying analysis to compute the best-fitting analyzing methodology. Phase, amplitude, and reflector height, which are SNR-derived interferogram metrics are examined and results are proofed with respect to correlation coefficients compared with in-situ measurements. Here, Soil Moisture Content (SMC) is estimated from SNR data with four data analyzing strategies using Lomb Scargle Periodogram to retrieve dominant frequency as; (1) considering it is four times greater than background noise assuming the reflector height is inconstant in each day, (2) considering it is three times greater than background noise assuming the reflector height is inconstant in each day, (3) assuming the reflector height is constant and median values are used for overall estimations in each day, (4) assuming the reflector height is constant and median values are used in each day. To do that, the GPS Interferometric Reflectometry (GPS-IR) method was implemented to the data of OSOR station, installed in Chile (within the scope of the CAP Andes GPS Network Project carried out by UNAVCO), during 213 days from 01 January 2015 to 31 July 2015. Validation of the estimates is done by the recorded soil moistures from the Oromo Calibration Site in the LAB-net network. Results show that SMC estimated from SNR-derived metrics shows well agreement with in-situ measurements i.e. as the highest correlation of 95%; whilst the second strategy was followed.

Keywords

• Global Positioning System Interferometric Reflectometry (GPS-IR)
• Signal-to-Noise Ratio (SNR)
• Soil Moisture Content (SMC)
• Lomb Scargle Periodogram (LSP).

References

1. [1] Jackson, T., Schmugge, J., Engman, E. (1996). Remote sensing applications to hydrology: soil moisture. Hydrological Sciences, 41(4): 517-530.
2. [2] Larson, K.M., Small, E.E., Gutmann, E.D., Bilich, A.L., Braun, J.J. and Zavorotny, V.U. (2008). Use of GPS receivers as a soil moisture network for water cycle studies. Geophysical Research Letters, Vol. 35, L24405, doi:10.1029/2008GL036013.
3. [3] Larson, K.M., Small, E.E., Gutmann, E.D., Bilich, A.L., Axelrad, P., Braun, B. (2008). Using GPS multipath to measure soil moisture fluctuations: initial results. GPS Solutions, 12:173–177, doi:10.1007/s10291-007-0076-6.
4. [4] Jin, S., Komjathy, A. (2010). GNSS Reflectometry and Remote Sensing: New Objectives and Results. Advances in Space Research, 46, 111–117.
5. [5] Tunalıoğlu, N., Doğan, A. H., Durdağ, U. M. (2019). GPS sinyal gürültü oranı verileri ile kar kalınlığının belirlenmesi (in Turkish). Jeodezi ve Jeoinformasyon Dergisi, 6(1), 1-9.
6. [6] Bilskie, J. (2001). Soil water status: content and potential. Campbell Scientific, Inc. App. Note: 2S-1 http://s.campbellsci.com/documents/ca/technical-papers/ soilh20c.pdf.
7. [7] Ocalan, T., Erdogan, B., Tunalioglu, N., Durdag, U.M. (2016). Accuracy investigation of PPP method versus relative positioning using different satellite ephemerides products near/under forest environment. Earth Sciences Research Journal 20 (4), D1-D9
8. [8] Dogan, A.H., Tunalioglu, N., Erdogan, B., Ocalan, T. (2018). Evaluation of the GPS Precise Point Positioning technique during the 21 July 2017 Kos-Bodrum (East Aegean Sea) Mw 6.6 earthquake. Arabian Journal of Geosciences, 11: 775, https://doi.org/10.1007/s12517-018-4140-z

9. [9] Hofmann-Wellenhof, B., Lichtenegger, H., Wasle, E. (2007). GNSS–global navigation satellite systems: GPS, GLONASS, Galileo, and more. Springer Science & Business Media.
10. [10] 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, Vol. 3, No. 1, pp. 91-99, doi: 10.1109/JSTARS.2009.2033612.
11. [11] Han, M., Zhu, Y., Yang, D., Chang, Q., Hong, X., and Song, S. (2020). Soil moisture monitoring using GNSS interference signal: proposing a signal reconstruction method. Remote Sensing Letters, 11:4, 373-382, DOI: 10.1080/2150704X.2020.1718235
12. [12] Larson, K. M., Small, E. E. (2016). Estimation of snow depth using L1 GPS signal-to-noise ratio data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(10), 4802-4808
13. [13] Roesler, C., Larson, K.M. (2018). Software tools for GNSS interferometric reflectometry (GNSS-IR). GPS Solutions (2018) 22:80, https://doi.org/10.1007/s10291-018-0744-8
14. [14] Rodriguez-Alvarez, N, Bosch-Lluis, X., Camps, A., Aguasca, A., Vall-llossera, M., Valencia, E., Ramos-Perez, I., Park, H. (2011). Review of crop growth and soil moisture monitoring from a ground-based instrument implementing the interference pattern GNSS-R technique. Radio Science, vol. 46, no. 06, pp. 1-11, Dec. 2011, doi: 10.1029/2011RS004680.
15. [15] Larson, K. M., Nievinski, F. G. (2013). GPS snow sensing: results from the EarthScope Plate Boundary Observatory. GPS Solutions, 17(1), 41-52.
16. [16] Nievinski, F. G., Larson, K. M. (2014). Inverse modeling of GPS multipath for snow depth estimation-Part I: Formulation and simulations. IEEE Transactions on Geoscience and Remote Sensing, 52(10), 6555-6563.
17. [17] Small, E. E., Larson, K. M., Chew, C. C., Dong, J., Ochsner, T. E. (2016). Validation of GPS-IR soil moisture retrievals: Comparison of different algorithms to remove vegetation effects. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(10), 4759-4770.
18. [18] Ban, W., Yu, K., Zhang, X. (2017). GEO-satellite-based reflectometry for soil moisture estimation: Signal modeling and algorithm development. IEEE Transactions on Geoscience and Remote Sensing, 56(3), 1829-1838.
19. [19] Martín, A., Ibáñez, S., Baixauli, C., Blanc, S., Anquela, A. B. (2020). Multi-constellation GNSS interferometric reflectometry with mass-market sensors as a solution for soil moisture monitoring. Hydrology and Earth System Sciences, 24(7), 3573-3582.
20. [20] Swinscow, T.D.V., Campbell, M.J. (2002). Statistics at square one, 10th edn. BMJ Books. ISBN 0-7279-1552-5
21. [21] Wessel, P., Smith, W.H.F. (1998). New improved version of Generic mapping tools released, EOS Trans. AGU 79 (47) 579.