GFZ German research centre for geo sciences

Soil moisture from reflected GNSS signals

In the framework of the Helmholtz Alliance „Remote Sensing and Earth System Dynamics“  a three-year research project was started in 2014 in order to estimate soil moisture from data of ground-based GNSS stations (Global Navigation Satellite Systems). We  analyse GPS signals, which are reflected in the nearest surrounding of the station. The project is involved in the network of TERENO (TERrestrial ENvironmental Observatories). The scientific project tasks are carried out in very close cooperation with the section 5.4. Hydrology of the GFZ. The estimation of soil moisture helps to improve the quantification of the hydrological cycle. Measurements of soil moisture are important for irrigation management, flood prediction, contaminant and nutrient transport, weather forecast and climate studies. However, obtaining soil moisture data at the field scale is a challenge as soil moisture measurements are generally point measurements with small sampling volumes.

Observations of remote sensing satellites, e.g. SMOS, on the other hand have large footprints of several kilometres and come with the disadvantage of low temporal resolution. Therefore the use of data from ground based GNSS stations to estimate near-surface soil moisture variations seems to be a promising methodology to obtain field averages of soil moisture at high temporal resolution [1].

In a case study for Sutherland, South Africa we analyze data from a permanent GNSS station from January until July 2013. The analysis of the GNSS data is based on the signal-to-noise ratio (SNR) of the GNSS signals. From the interference pattern of the SNR, which are caused by the combination of the direct and indirect signal, we derive the penetration depth of the GNSS signal into the ground [2]. The penetration depth depends on near-surface soil moisture and can be converted to soil moisture variations [3].

The soil moisture variations obtained from GNSS data capture very well precipitation events and subsequent evapotranspiration. In comparison with the soil moisture observations from in-situ soil moisture sensors the GNSS estimates shows slightly different absolute values (Fig. 2). However, the variations in soil moisture from the two techniques agree very well.  Near-surface soil moisture estimates from GNSS observations have the potential to complement soil moisture monitoring networks at many sites worldwide.

Another case study was performed for station Marquardt, Germany, where a GNSS station was set near a hydrological station of the TERENO project. The acquired data covers the period since July 2014 (Fig. 3) and shows very good agreement with in-situ soil moisture measurements. The observations were also complemented with a calibration experiment.

In the frame work of the Helmholtz Virtual Institute DEad SEa Research (DESERVE) 3 GNSS stations were installed in the Dead Sea area, which serve amongst others for soil moisture estimation.


[1] Larson, K., J. Braun, E. Small, V. Zavorotny, E. Gutmann, and A. Bilich, GPS multipath and its relation to near-surface soil moisture content, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 3, No. 1, pp. 91–99, Mar. 2010.

[2] Vey, S., Güntner, A., Wickert, J., Blume, T., Ramatschi, M.: Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland, South Africa. - GPS Solutions, 20, 4, p. 641-654, doi:10.1007/s10291-015-0474-0, 2016.

[3] Chew, C., E. Small, K. Larson and V. Zavorotny, Effects of Near-Surface Soil Moisture on GPS SNR Data: Development of a Retrieval Algorithm for Soil, IEEE Transactions on Geosciences and Remote Sens., doi: 10.1109/TGRS.2013.2242332, Dec. 2012.

[4]  Vey, S., Güntner, A., Wickert, J., Blume, T., Thoss, H., Ramatschi, M.: Monitoring Snow Depth by GNSS Reflectometry in Built-up Areas: A Case Study for Wettzell, Germany, IEEE Journal of selected topics in applied Earth observations and Remote Sensing, Vol. 9 (10), 4809-4816, Doi: 10.1109/JSTARS.2016.2516041, 2016.

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