Abstract
Global Navigation Satellite System-interferometric reflectometry (GNSS-IR) has become an important means to monitor soil moisture content (SMC). It works based on the assumption that the detrended signal-to-noise ratio (DSNR) sequence has a cosine waveform. However, when the signal reflection ground is undulating, its pattern may deviate from the assumed waveform, eventually influencing the SMC retrieval. To resolve this limitation, we proposed an arc editing method whose basic idea is to edit the DSNR sequence and keep only the DSNR data whose interference pattern is a typical cosine waveform. We tested the arc editing method in comparison with the conventional method in SMC retrieval using 4-year DSNR data from 4 simulated satellite arcs and 3-year DSNR data from 3 GNSS stations deployed in undulating terrains. The simulation results show that the new method eliminated the multi-dominant frequencies appeared in the DSNR sequences and improved the dominant frequency power, resulting in better SMC retrieval accuracy than the conventional method. When comparing the retrieval results to in situ probe-measured SMC data, the SMC retrieved by the new method has an average correlation coefficient of 0.758 and an average root mean square error (RMSE) of 0.045 cm3 cm−3, which are 48.1% and 27.0% better than those from the conventional method (0.512 cm3 cm−3 and 0.062 cm3 cm−3). These results suggest that the new method can improve the retrieval accuracy of SMC in undulating terrains, which helps in applying the GNSS-IR to much wider regions.
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Data availability
All GPS observation files were downloaded from ftp://data-out.unavco.org/pub/rinex/obs. The reference SMC data all came from https://ismn.geo.tuwien.ac.at/en. The high-precision Lidar Point Cloud data was obtained from https://coast.noaa.gov/inventory/ and https://viewer.nationalmap.gov/basic/#/. The soil texture data around each station can be found in https://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx. The precipitation data were downloaded from https://gis.ncdc.noaa.gov/maps/ncei/summaries/daily.
References
Ban W, Yu K, Zhang X (2017) GEO-satellite-based reflectometry for soil moisture estimation: signal modeling and algorithm development. IEEE Trans Geosci Remote Sens 56(3):1829–1838. https://doi.org/10.1109/TGRS.2017.2768555
Beaudette DE, O’Geen AT (2009) Soil-Web: an online soil survey for California, Arizona, and Nevada. Comput Geosci 35(10):2119–2128. https://doi.org/10.1016/j.cageo.2008.10.016
Bell JE et al (2013) US climate reference network soil moisture and temperature observations. J Hydrometeorol 14(3):977–988. https://doi.org/10.1175/JHM-D-12-0146.1
Bilich A, Larson KM (2007) Mapping the GPS multipath environment using the signal-to-noise ratio (SNR). Radio Sci. https://doi.org/10.1029/2007RS003652
Brocca L, Ciabatta L, Massari C, Camici S, Tarpanelli A (2017) Soil moisture for hydrological applications: open questions and new opportunities. Water 9(2):140. https://doi.org/10.3390/w9020140
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. https://doi.org/10.1109/TGRS.2013.2242332
Chew CC, Small EE, Larson KM, Zavorotny VU (2014) Vegetation sensing using GPS-interferometric reflectometry: theoretical effects of canopy parameters on signal-to-noise ratio data. IEEE Trans Geosci Remote Sens 53(5):2755–2764. https://doi.org/10.1109/TGRS.2014.2364513
Chew C, Small EE, Larson KM (2016) An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil. GPS Solut 20(3):525–537. https://doi.org/10.1007/s10291-015-0462-4
Childs EC (1940) The use of soil moisture characteristics in soil studies. Soil Sci 50(4):239–252. https://doi.org/10.1097/00010694-194010000-00001
Engman ET (1991) Applications of microwave remote sensing of soil moisture for water resources and agriculture. Remote Sens Environ 35(2–3):213–226. https://doi.org/10.1016/0034-4257(91)90013-V
Entekhabi D, Rodriguez-Iturbe I, Castelli F (1996) Mutual interaction of soil moisture state and atmospheric processes. J Hydrol 184(1–2):3–17. https://doi.org/10.1016/0022-1694(95)02965-6
Griffin DM (1963) Soil moisture and the ecology of soil fungi. Biol Rev 38(2):141–166. https://doi.org/10.1111/j.1469-185X.1963.tb00781.x
Kumar SV, Wang S, Mocko DM, Peters-Lidard CD, Xia Y (2017) Similarity assessment of land surface model outputs in the North American land data assimilation system. Water Resour Res 53(11):8941–8965. https://doi.org/10.1002/2017WR020635
Larson KM, Braun JJ, Small EE, Zavorotny VU, Gutmann ED, Bilich AL (2009) GPS multipath and its relation to near-surface soil moisture content. IEEE J Sel Top Appl Earth Observ Remote Sens 3(1):91–99. https://doi.org/10.1109/JSTARS.2009.2033612
Larson KM, Nievinski FG (2013) GPS snow sensing: results from the earthscope plate boundary observatory. GPS Solut 17(1):41–52. https://doi.org/10.1007/s10291-012-0259-7
Larson KM, Ray RD, Nievinski FG, Freymueller JT (2013) The accidental tide gauge: a GPS reflection case study from Kachemak Bay, Alaska. IEEE Geosci Remote Sens Lett 10(5):1200–1204. https://doi.org/10.1109/LGRS.2012.2236075
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. https://doi.org/10.1007/s10291-007-0076-6
Martín A, Ibáñez S, Baixauli C, Blanc S, Anquela AB (2020) Multi-constellation GNSS interferometric reflectometry with mass-market sensors as a solution for soil moisture monitoring. Hydrol Earth Syst Sci 24(7):3573–3582. https://doi.org/10.5194/hess-24-3573-2020
Matz V, Ramos PM, Brás NB, Serra AC (2006) A comparative evaluation between frequency estimation algorithms for power quality assessment in DSP implementation. In XVIII IMEKO World Congress, Metrology for a Sustainable Development. doi: 10.1.1.529.8623
Nievinski FG, Larson KM (2014a) Forward modeling of GPS multipath for near-surface reflectometry and positioning applications. GPS Solut 18(2):309–322. https://doi.org/10.1007/s10291-013-0331-y
Nievinski FG, Larson KM (2014b) An open source GPS multipath simulator in Matlab/octave. GPS Solut 18(3):473–481
Ostergren CL, Decker D, Carswell Jr, WJ (2019) The 3D Elevation Program—Supporting California's Economy (No. 2019–3029). U.S. Geological Survey
Rodríguez-Iturbe I, Porporato A (2007) Ecohydrology of water-controlled ecosystems: soil moisture and plant dynamics. Cambridge Univy Press. https://doi.org/10.1029/2005EO380008
Roesler C, Larson KM (2018) Software tools for GNSS interferometric reflectometry (GNSS-IR). GPS Solut 22(3):80. https://doi.org/10.1007/s10291-018-0744-8
Roussel N, Frappart F, Ramillien G, Darrozes J, Baup F, Lestarquit L, Ha MC (2016) Detection of soil moisture variations using GPS and GLONASS SNR data for elevation angles ranging from 2 to 70. IEEE J Sel Top Appl Earth Observ Remote Sens 9(10):4781–4794. https://doi.org/10.1109/JSTARS.2016.2537847
Schwarz GE, Alexander RB (1995) Soils data for the conterminous United States derived from the NRCS State Soil Geographic (STATSGO) data base. U.S. Geological Survey Open-File Report, 95–449
Small EE, Larson KM, Chew CC, Dong J, Ochsner TE (2016) Validation of GPS-IR soil moisture retrievals: Comparison of different algorithms to remove vegetation effects. IEEE J Sel Top Appl Earth Observ Remote Sens 9(10):4759–4770. https://doi.org/10.1109/JSTARS.2015.2504527
Tabibi S, Geremia-Nievinski F, van Dam T (2017) Statistical comparison and combination of GPS, GLONASS, and multi-GNSS multipath reflectometry applied to snow depth retrieval. IEEE Trans Geosci Remote Sens 55(7):3773–3785. https://doi.org/10.1109/TGRS.2017.2679899
Vey S, Güntner A, Wickert J, Blume T, Ramatschi M (2016) Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland South Africa. GPS Solut 20(4):641–654. https://doi.org/10.1007/s10291-015-0474-0
Zhang S, Calvet JC, Darrozes J, Roussel N, Frappart F, Bouhours G (2018) Deriving surface soil moisture from reflected GNSS signal observations from a grassland site in southwestern France. Hydrol Earth Syst Sci 22(3):1931–1946. https://doi.org/10.5194/hess-22-1931-2018
Zhang S, Wang X, Zhang Q (2017) Avoiding errors attributable to topography in GPS-IR snow depth retrievals. Adv Space Res 59(6):1663–1669. https://doi.org/10.1016/j.asr.2016.12.031
Acknowledgements
We would like to thank the editor and reviewers for their valuable comments on the manuscript. And we are grateful to Larson Research Group for their contributions in the GNSS-IR field. Actually, the email exchange with Professor Larson and Doctor Chew helped us a lot. We also want to thank to Professor Nievinski since we could not have used the actual DEM to simulate the SNR data without his help. The NSF's Earth Scope Plate Boundary Observatory (PBO) network and the US Geological Survey provide the GPS observation data and the high-precision Lidar Point Cloud data. The US Climate Reference Network provided the reference SMC data. It is hard to finish the work without these open resources. This study is supported by the National Nature Science Foundation of China (Nos. 41721003, 42074035, 41874033, 41704004).
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Ran, Q., Zhang, B., Yao, Y. et al. Editing arcs to improve the capacity of GNSS-IR for soil moisture retrieval in undulating terrains. GPS Solut 26, 19 (2022). https://doi.org/10.1007/s10291-021-01206-y
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DOI: https://doi.org/10.1007/s10291-021-01206-y