Soil moisture at station Marquardt derived from L2C and L5 dataL5 data

Soil moisture is the final product of the analysis of the time series of the interference patterns between the direct and reflected signals at elevation angles below 30 degrees.

For these measurements from the MARQ receiver for the L2C signals 13 satellite paths are selected for analysis and divided into four sectors (shown in figure 5.9). The indicated soil moisture estimates in each of the four sectors is different. The differences between the sectors can be explained with the lack of homogeneity in the soils around the GNSS site. Figure 5.8 shows that the reflection in sector 1 comes from a grassland area, in sector 2 the reflections are very close to a concrete surface, in sector 3 the reflections are coming from mostly bare soil, similar to the reflection from sector 4. The results show that the grass covered soil retains more moisture, compared to bare soil during dry periods. Similar results are achieved using the same receiver, antenna and reflections for the L5 GPS signal.

Both final calibrated sets of soil moisture measurements from the L2C and L5 signals show good correlation with the collocated TDR measurements at different depths (shown in figures 5.10). The 1cm sondes are most sensitive to any changes in the atmosphere

-Figure 5.9: Sector soil moisture retreivals for L2C signals. The 4 sectors show slightly different behaviour in soil moisture. The presented data are before final outliers removal and calibration.

the response to precipitation and morning due are the highest at this layer. The deeper layers at 3 and 5cmshow slower responses to precipitation. These layers are less exposed to the atmosphere and evaporate less water. The lowest layer at 9cmis much more inert, then the layers closer to the surface. It conserves much more water and usually disposes of it through infiltration.

The RMSE of the soil moisture comparisons between the L2C signals products and TDR shows highest values for the 1cm layer. The deeper layers show lower RMSE’s with the lowest of 4.3V ol% for the 9cm TDR’s (see figure 5.11). The seasonal analysis shows different behaviour for each season. The season with the highest RMSE is au-tumn (September, October, November (SON)), while the season with the lowest is spring (March, April and May (MAM)). The average RMSE between GNSS-R and 5cm TDR from these experiments is 4.5V ol%.

The soil moisture datasets can be divided into cases season by season, based on the difference in correlation between winter and the rest of the year (see figure 5.11). This seasonal analysis shows a slightly different situation for each period. During the colder months the soil moisture estimations from the GNSS differ greatly from the TDR mea-surements. One of the reasons for this discrepancy is the fact, that it is not possible to distinguish snow cover from bare soil in the winter period. Whenever there is snow in the region of Marquardt, the total snow cover is in the range of between 0-5cm, which

5.3 Monitoring at stations Marquardt and Fürstensee 83

Figure 5.10: Scatter plots of TDR measurements at different depths and GNSS soil mois-ture. The GNSS-R retrievals show a wet bias when compared to the TDR measurements.

The correlations between TDR at 3, 5 and 9cm and GNSS-R are very similar.

is within the reflection height estimation standard deviation. Thus soil moisture is the GNSS-R observable even when the bare soil is covered, which leads to additional uncer-tainty during the winter. Moreover winter is a season with much lower variation of the soil moisture, thus correlation can be a misleading metric. The expected correlation between the datasets is highest during spring, summer and autumn, because of the large variety of soil moisture values and the trends during these seasons.

Since the seasonal correlation (in figure 5.11) suggests two different modes in the behaviour of soil moisture, the winter cases are discussed separately from the warmer periods of the year. The winter soil moisture retrievals not only show lower correlation

Figure 5.11: Seasonal comparrisson of correlation and RMSE for station MARQ L2C signals and TDR. Seasons are defined as DJF for winter, MAM for spring, JJA for summer and SON for autumn.

with TDR, but also their response to precipitation is not as significant. Temperature of 10^oC is the threshold below which this transition from lower to higher correlation occurs.

Plots with the comparison between the two datasets can be seen in figures 5.12. These figures show two modes in the soil moisture measurements from GNSS, depending on 2m air temperature.

First winter case is when the air temperature is in the interval between 0 and 10^oC. The average values, recorded by the method are very close to the TDR measurements, thus GNSS is able to show high amounts of soil moisture during the winter season. This is expected, due to the lower evaporation when the temperatures are low. The precipitation amounts during winter are characterised with much lower precipitation rate and longer precipitation periods.

Second winter case is when the air temperature is below 0^oC. These are periods with conditions for water to freeze in the soil. Such conditions are available during every winter in NE Germany. The GNSS reflection model, developed by Zavorotny et al.

(2010) is applicable only to liquid water in soils, rather than frozen water. Thus only the decrease of liquid water in the soil (which is referred to as soil moisture) is apparent in our measurements, while the frozen water can’t be measured with this technique. The drop in soil moisture is present in both GNSS and TDR measurements, thus suggesting, that in these low temperatures, the GNSS reflections are indeed detecting soil moisture.

5.3 Monitoring at stations Marquardt and Fürstensee 85

Figure 5.12: Winter soil moisture in Marquardt. Figure on the top is for winter 2014-2015, while on the bottom - 2016-2017. Light blue color indicates temperatures in the interval between 0 and 10^oC, while dark blue indicates temperatures below 0^oC.

The second winter case shows, that during winter time, the measured phase changes from the reflections are indeed dependent on the amount of soil moisture in the soil. The difference in the measured values by the two methods can be explained by the following differences in the methods themselves:

• TDR measures confined volume around the probes, while GNSS has large footprint, in case of Marquardt - more than 300m²,

• TDR measures at specific depths, while the penetration of the GNSS signal depends on the VWC in the soil,

• TDR is independent on above-ground vegetation, while GNSS is dependent on both above-ground vegetation and on roots

• GNSS signals get reflected from the snow cover, when one is present, thus corrupting the soil moisture retrieval.

Figure 5.13: Summer soil moisture in Marquardt. Figure on the top is for the warm season (2m temperature above 10^oC) of 2015, while on the bottom - 2016. Light blue color indicates temperatures in the interval between 0 and 10^oC.

These differences contribute to an entirely different measurement between the two meth-ods, thus explaining partly the biases and not very high correlation between the datasets.

The differences between TDR and GNSS as soil moisture measuring techniques can be observed in the summer variation of both datasets as well (seen in figure 5.13). Several precipitation events occur between 20th August and the end of September 2016 in the premises of Marquardt. TDR shows very low to non-existing response to these precipi-tation events, while the response of GNSS can clearly be seen. One of the main reasons why TDR is not sensible to these precipitation events is lack of infiltration in the soil.

The lack of infiltration could be triggered by the high temperatures during this part of the year (daily high temperatures in the range of 30^oC). All of the precipitation events are recorded in the afternoon hours, when the soil is most heated and the potential for evaporation from it is highest. This hypothesis is also supported by the response of the 1cm TDR probes, which indicate a short increase in measured VWC.

The GNSS estimates of soil moisture are done by averaging the soil moisture retrievals

5.3 Monitoring at stations Marquardt and Fürstensee 87 from each individual satellite. Since the reflections are happening non-uniformly through-out the day, some of the satellites contribute to the daily soil moisture values with through morning reflections, while some satellites contribute with evening reflections. This is the reason why in some cases the VWC, calculated using GNSS, shows local maximum values one day after the precipitation. Since precipitation events are more often an afternoon event, than a morning event, the dryer morning reflections contribute with much smaller values. The TDR values are also daily averages and also shows such behaviour.

Figure 5.14: Severe summer rainfall - high resolution GNSS soil moisture experiment.

This event happened in the end of June, beginning of July 2017 with recorded daily precipitation values of 35mm/day on the 29th of June. On the right axis 10 minutes precipitation rate plotted (blue bars). The red line indicates L2C derived soil moisture, while the black line is 5cm TDR.

In order to investigate the potential of GNSS to access soil moisture on a sub-daily scale, an experiment with several rainfall events is carried out. The averaging of the soil moisture is done using 24 hour sliding windows shown in figure 5.14). This small investigation showed that the local maximum in soil moisture coincides with the time of precipitation. The soil moisture retrieval with such high resolution of 3 hours in a sliding window configuration is unreasonable. The figure suggests an increase in soil moisture before the precipitation event, which physically is not possible. On the other hand creating an algorithm, which would sharpen the difference before and after rainfall without using the rainfall measurements themselves is going to be artificial and will not represent soil moisture correctly in many other cases. Follow up studies with this approach for high resolution GNSS-R soil moisture estimations are questionable, since such temporal resolution is not necessary neither for agriculture, nor for weather analysis.