GPS Solutions

, Volume 20, Issue 4, pp 641–654 | Cite as

Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland, South Africa

  • Sibylle Vey
  • Andreas Güntner
  • Jens Wickert
  • Theresa Blume
  • Markus Ramatschi
Original Article

Abstract

Soil moisture is a geophysical key observable for predicting floods and droughts, modeling weather and climate and optimizing agricultural management. Currently available in situ observations are limited to small sampling volumes and restricted number of sites, whereas measurements from satellites lack spatial resolution. Global navigation satellite system (GNSS) receivers can be used to estimate soil moisture time series at an intermediate scale of about 1000 m2. In this study, GNSS signal-to-noise ratio (SNR) data at the station Sutherland, South Africa, are used to estimate soil moisture variations during 2008–2014. The results capture the wetting and drying cycles in response to rainfall. The GNSS Volumetric Water Content (VWC) is highly correlated (r2 = 0.8) with in situ observations by time-domain reflectometry sensors and is accurate to 0.05 m3/m3. The soil moisture estimates derived from the SNR of the L1 and L2P signals compared to the L2C show small differences with a RMSE of 0.03 m3/m3. A reduction in the SNR sampling rate from 1 to 30 s has very little impact on the accuracy of the soil moisture estimates (RMSE of the VWC difference 1–30 s is 0.01 m3/m3). The results show that the existing data of the global tracking network with continuous observations of the L1 and L2P signals with a 30-s sampling rate over the last two decades can provide valuable complementary soil moisture observations worldwide.

Keywords

GNSS Reflectometry Soil moisture Signal-to-noise ratio 

References

  1. Alonso-Arroyo A, Camps A, Aguasca A, Forte GF, Monerris A, Rudiger C, Walker JP, Park H, Pascual D, Onrubia R (2014) Dual-polarization GNSS-R interference pattern technique for soil moisture mapping. IEEE J Sel Top Appl Earth Obs Remote Sens 7(5):1533–1544. doi:10.1109/JSTARS.2014.2320792 CrossRefGoogle Scholar
  2. Beckmann P, Spizzichino A (1987) The scattering of electromagnetic waves from rough surfaces. Artech House Radar LibraryGoogle Scholar
  3. Brocca L, Melone F, Moramarco T, Wagner W, Naeimi V, Bartalis Z, Hasenauer S (2010) Improving runoff prediction through the assimilation of the ascat soil moisture product. Hydrol Earth Syst Sci 14(10):1881–1893. doi:10.5194/hess-14-1881-2010 CrossRefGoogle Scholar
  4. Chew CC, Small EE, Larson KM, Zavorotny VU (2013) Effects of near-surface soil moisture on gps snr data: development of a retrieval algorithm for soil moisture. IEEE Trans Geosci Remote Sens 52(1):537–543. doi:10.1109/TGRS.2013.2242332 CrossRefGoogle Scholar
  5. Dorigo WA et al (2014) Evaluation of the ESA CCI soil moisture product using ground-based observations. Remote Sens Environ. doi:10.1016/j.rse.2014.07.023 Google Scholar
  6. Drusch M (2007) Initializing numerical weather prediction models with satellite-derived surface soil moisture: data assimilation experiments with ECMWF’s integrated forecast system and the TMI soil moisture data set. J Geophys Res 112(D3):3102. doi:10.1029/2006JD007478 CrossRefGoogle Scholar
  7. Egido A, Paloscia S, Motte E, Guerriero L, Pierdicca N, Caparrini M, Santi E, Fontanelli G, Floury N (2014) Airborne GNSS-R polarimetric measurements for soil moisture and above-ground biomass estimation. IEEE J Sel Topics Appl Earth Obs Remote Sens 7(5):1522–1532. doi:10.1109/JSTARS.2014.2322854 CrossRefGoogle Scholar
  8. Entekhabi D, Njoku E, O’Neill P, Spencer M, Jackson T, Entin J, Im E, Kellogg K (2008) The soil moisture active/passive mission (SMAP). Geosci Remote Sens Symp IGARSS IEEE Int 98(5):704–716. doi:10.1109/JPROC.2010.2043918 Google Scholar
  9. Fontana RD, Cheung W, Novak PM, Thomas A (2001) The new L2P civil signal. In: Proceedings of ION ITM GPS Institute of Navigation, September, Salt Lake City UT, pp 617–631Google Scholar
  10. Gurtner W, Estey L (2007) RINEX: The receiver independent exchange format version 2.11. http://igscb.jpl.nasa.gov/igscb/data/format/rinex211.txt
  11. Katzberg SJ, Torres O, Grant MS, Masters D (2005) Utilizing calibrated GPS reflected signals to estimate soil reflectivity and dielectric constant: results from SMEX02. Remote Sens Environ 100(1):17–28. doi:10.1016/j.rse.2005.09.015 CrossRefGoogle Scholar
  12. Larson KM, Nievinski FG (2012) GPS snow sensing: results from the earthscope plate boundary observatory. GPS Solut 17(1):41–52. doi:10.1007/s10291-012-0259-7 CrossRefGoogle Scholar
  13. Larson KM, Small EE, Gutmann E, Bilich A, Axelrad P, Braun J (2008) Using GPS multipath to measure soil moisture fluctuations: initial results. GPS Solut 12(3):173–177. doi:10.1007/s10291-007-0076-6 CrossRefGoogle Scholar
  14. Larson KM, Braun JJ, Small EE, Zavorotny VU, Gutmann ED, Bilich AL (2010) GPS multipath and its relation to near-surface soil moisture content. IEEE J Sel Topics Appl Earth Obs Remote Sens 3(1):91–99. doi:10.1109/JSTARS.2009.2033612 CrossRefGoogle Scholar
  15. Martin-Neira M (1993) A passive reflectometry and interferometry system (PARIS): application to ocean altimetry. ESA J 17(4):331–355Google Scholar
  16. Masters D, Axelrad P, Katzberg S (2002) Initial results of land-reflected GPS bistatic radar measurements in SMEX02. Remote Sens Environ 92(4):507–520. doi:10.1016/J.RSE.2004.05.016 CrossRefGoogle Scholar
  17. Nievinski FG, Larson KM (2014) Forward modeling of multipath for near-surface reflectometry and positioning applications. GPS Solut 18(2):309–322. doi:10.1007/s10291-013-0331-y CrossRefGoogle Scholar
  18. Nolan M, Fatland DR (2003) Penetration depth as a DInSAR observable and proxy for soil moisture. IEEE Trans Geosci Remote Sens 41(3):532–537. doi:10.1109/TGRS.2003.809931 CrossRefGoogle Scholar
  19. Perry MA, Niemann JD (2008) Generation of soil moisture patterns at the catchment scale by EOF interpolation. Hydrol Earth Syst Sci 12(1):39–53. doi:10.5194/hess-12-39-2008 CrossRefGoogle Scholar
  20. Press WH, Rybicki GB (1989) Fast algorithm for spectral analysis of unevenly spaced data. Astrophys J 338:277–280. doi:10.1086/167197 CrossRefGoogle Scholar
  21. Rodriguez-Alvarez N, Camps A, Valencia E, Hernandez JM, Perez I (2009) Soil moisture retrieval using GNSS-R techniques: experimental results over a bare soil field. IEEE Trans Geosci Remote Sens 47(11):3616–3624. doi:10.1109/TGRS.2009.2030672 CrossRefGoogle Scholar
  22. Schaufler G, Kitzler B, Schindlbacher A, Skiba U, Sutton MA, Zechmeister-Boltenstern S (2010) Greenhouse gas emissions from European soils under different land use: effects of soil moisture and temperature. Eur J Soil Sci 61(5):683–696. doi:10.1111/j.1365-2389.2010.01277.x CrossRefGoogle Scholar
  23. Seneviratne SI, Corti T, Davin EL, Hirschi M, Jaeger EB, Lehner I, Orlowsky B, Teuling AJ (2010) Investigating soil moisture–climate interactions in a changing climate: a review. Earth-Sci Rev 99(3–4):125–161. doi:10.1016/j.earscirev.2010.02.004 CrossRefGoogle Scholar
  24. Tabibi S, Nievinski FG, van Dam T, Monico JFG (2015) Assessment of modernized GPS L5 SNR for ground-based multipath reflectometry applications. Adv Space Res 55(4):1104–1116. doi:10.1016/j.asr.2014.11.019 CrossRefGoogle Scholar
  25. Topp GC, Davis JL, Annan AP (1980) Electromagnetic determination of soil water content: measurements in coaxial transmission lines. Water Resour Res 16(3):574–582. doi:10.1029/WR016i003p00574 CrossRefGoogle Scholar
  26. Vey S, Güntner A, Wickert J, Blume T, Ramatschi M (2015) Supplement to: long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland, South Africa. GFZ German Research Center for Geosciences. doi:10.5880/GFZ.1.1.2015.001
  27. Wanders N, Karssenberg D, de Roo A, de Jong SM, Bierkens MFP (2014) The suitability of remotely sensed soil moisture for improving operational flood forecasting. Hydrol Earth Syst Sci 18:2343–2357. doi:10.5194/hess-18-2343-2014 CrossRefGoogle Scholar
  28. Wang L, Qu JJ (2009) Satellite remote sensing applications for surface soil moisture monitoring: a review. Front Earth Sci China 3(2):237–247. doi:10.1007/s11707-009-0023-7 CrossRefGoogle Scholar
  29. Zavorotny VU, Masters D, Gasiewski A, Bartram B, Katzberg S, Axelrad P and Zamora R (2003) Seasonal polarimetric measurements of soil moisture using tower-based GPS bistatic radar. In: Proceedings of IEEE 2003 international geoscience and remote sensing symposium, IGARSS 2003, vol 2, pp 781–783. doi:10.1109/IGARSS.2003.1293916

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Sibylle Vey
    • 1
  • Andreas Güntner
    • 1
  • Jens Wickert
    • 1
  • Theresa Blume
    • 1
  • Markus Ramatschi
    • 1
  1. 1.GFZ German Research Centre for GeosciencesPotsdamGermany

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