GNSS Reflectometry soil moisture error budget

Figure 5.45: Soil moisture retrievals for 2017 from IGS station Marlborough. Precipitation is interpolated from ERA5. The soil moisture line in purple is from ERA5.

with the lowest VWC and highest amplitude, caused by the precipitation events and the higher evapotranspiration.

SMC1 SMC2 SMC3 SMC4 SMC5

ERA5 0.99 1.35 0.55 0.18 0.22

Table 5.8: Soil moisture coefficients for station Marlborough, according to definitions in chapter 5.2. Since no SYNOP data for precipitation is available for station Marlborough, the coefficients are derived from ERA5.

5.5 GNSS Reflectometry soil moisture error budget

The measurement technique, described in this thesis for soil moisture observation is rel-ative and not absolute, which means, that in order to estimate the VWC in the soil, additional information, apart from the GNSS reflections, has to be supplied. The fact, that the approach is relative also means, that the accuracy and the precision of the mea-surements have to be estimated. Since different aspects of the data analysis contribute to the accuracy and precision in a different way, these two parameters are examined separately.

Apart from the effects, described below, which have influence on the accuracy and precision of the measurements, there are also parameters, which do not. One of these are the used GPS orbits. The whole study in this work on reflectometry has been convened using broadcast satellite orbits, instead of using final orbits. The broadcast orbit is a set of parameters, transmitted in the GNSS message from the satellite to the receiver and recorded locally. The final orbits are the same set of parameters, which are later on

recalculated using a data processing software. The orbit data gives the precise position of each GPS satellite, thus enabling to determine the reflection direction and the elevation of the satellite. Although the signal strength is dependent on the elevation, the interference pattern is independent on the orbits message, and contains within itself the effect of the reflected signal. Thus the small differences between the broadcast and final orbits have no significance on the soil moisture estimation.

5.5.1 Effects influencing precision

Several experiments are carried out with data from station Marquardt. A base dataset is selected from the L2C results for 2016 from station MARQ. The data is then forcefully modified in order to estimate the sensitivity of the soil moisture derivation on different aspects of the processing procedure.

The first modification is to decrease the resolution of the signal strength data from 0.25dB − Hz to 1dB − Hz. The signal strength resolution is known from literature to deteriorate the accuracy of the LSA and the Lomb-Scargle. The resulting dataset has 0.99 correlation and 1.8Vol% RMSE. The resulting response from this manipulation is negligible (seen on figure 5.47). The soil moisture dynamics is clearly visible and dependent on precipitation events.

Figure 5.46: Reflector height modi-fied correlation and RMSE.

The second modification carried out is to de-crease the sampling rate of the data from 1 second to 10 and 30 seconds. This is done to estimate the significance of the sampling rate on the LSA. Theo-retically, the lower amount of data points, the lower the accuracy of the LSA and the Lomb-Scargle. The difference between the initial dataset and the mod-ified dataset is again very small - correlation 0.99 and RMSE of 1.2Vol% (seen on figure 5.47) for the 10 seconds and correlation of 0.99 and RMSE of 2.6Vol% for the 30 seconds sampling rate. The effect of changing the data sampling rate is distinguish-able, but insignificant from the 1 second sampling rate dataset. This is further proved by the results, obtained in the IGS stations, where the sampling

rates are 30 seconds and soil moisture can still be observed.

Thirdly, the estimated reflector height of the average reflector height is both increased and decreased to observe the influence of the reflector height estimation accuracy on the final soil moisture. As discussed in the beginning of chapter 5, the reflector height is estimated for each individual satellite for each reflection. The reflector height over time

5.5 GNSS Reflectometry soil moisture error budget 113

Figure 5.47: Effects of changing sampling rate and signal strength resolution on soil moisture retrievals. The Base sampling rate of 1 second is reduced to 10 seconds and the signal strength resolution is reduced from 0.25dB−Hz to 1dB−Hz.

varies within 10 cm from the mean. The mean reflector height is then used to determine the phase and amplitude of the reflections. The reflector height is increased in two steps by 20cm and 50cm and decreased by the same amounts. Since the height of the pole, on which the antenna sits in Marquardt is 3 meters, the changes in the reflector height are by up to 17%. The height of the antenna is not physically changed for this experiment, only the estimated reflector height by the software.

All of the resulting datasets show clear deterioration, compared to the original results, but the soil moisture signature can still be observed in these datasets. A clear trend can be observed in the mean values and the distribution of the data in general. With the GFZ soil moisture retrieval software artificial lowering of the antenna height, the monitored soil moisture has smaller standard deviation and shows lower readings. With artificially increasing the reflector height the standard deviation, mean value and data range show

Figure 5.48: Reflector height modified value distribution. In the figure in the bottom, the thinner error bars mark the minimum and maximum values, the thicker error bars mark the standard deviation and the middle mark represents the mean value for the whole data series. These distributions describe the data, plotted on the top figure.

higher and wider range of values. This experiment shows, that if a station reflector height is miscalculated, the resulting soil moisture retrieval, although showing correct dynamics, shows inaccurate mean value and standard deviation.

Another source of biases to the soil moisture estimates is the scaling coefficient γ in equation 3.26. As discussed previously, theoretically, it should be equal toγ = _{1V ol%}^0.65◦ , but in practice this coefficient can vary greatly between stations, as discussed in page 43. The most likely explanation is, that γ is dependent on the soil composition. This is another effect, which is extremely hard to measure using the data sources in this thesis. The best way to examine the differences between soil types would be to install an experimental GNSS station with several different types of soils in the different sectors and examine the differences between them. Moreover soil composition can have large variability and soil