GNSS Reflectometry soil moisture measurements
5.1 Data processing routine
5.1.1 GFZ soil moisture retrieval software
A dedicated GFZ soil moisture retrieval software for processing GNSS reflected signals from ground-based GNSS stations is in development since 2013. At the beginning of 2016 the software produces phase, amplitude and height observations and stores them into Matlab data format. For this thesis the available software is further upgraded with more consistent date formatting, more robust data processing, as well as the possibility to export the data into the GFZ Reflectometry and Atmospheric Database (GRAD) database. This allowed the assignment of processing numbers to further allow comparison of changing parameters and different observation techniques. The soil moisture extraction routine is enhanced with filtering of vegetation effects. In the older versions of the software the final soil moisture values are calculated as means from 4 different sectors, rather than as means of all observed satellites. This approach is changed to give equal weight in the soil moisture derivation to each reflection. Alteration of the phase observations calculation output is also made to improve the phase estimations and the visualization of the estimated phases on the standard output plots for internal use.
The GNSS signals, processed in this chapter, are exclusively from GPS. GPS signals, namely L1, L2C and L5, are used for the estimation of the soil moisture and in chapter 7 for snow height observation from the reflected GPS signals. Unlike GLONASS, Galileo and BeiDou, GPS orbits are chosen at such altitude, that the satellites repeat their position every sidereal day (23h 56m 4s). This orbit period ensures that each GPS satellite rises and sets from the same direction in regards to a static GNSS receiver every day. Thus the GPS orbits enable ground reflections from each satellite to be located in the same area continuously over long periods of time, enabling daily observations. GLONASS satellite orbits provide such continuity not on a daily basis, but every 8 days. Galileo provides orbit repeatability every 10 days and BeiDou - every 7 days. Thus creating reflections time series over the same reflection points from Galileo, GLONASS and BeiDou satellites can be performed at worse than weekly data rate, compared to the daily rate from GPS.
This characteristic of the constellations is a limiting factor for the single antenna GNSS-R setups, which are used in this thesis.
The processing of the GNSS data in order to acquire soil moisture and snow height data (scheme seen on figure 5.1) starts with the RINEX data files, provided from each station. Some stations provide the more modern RINEX v3 format, some the older RINEX v2. The transition is being performed, using gfzrnx - a specially developed GFZ software byNischan (2016). The RINEX v2 file is then parsed through a program, which extracts the signal strength (SS) data and records them in an observation file (Roesler and Larson, 2018). The rest of the processing is done in the in-house developed Matlab-based reflectometry retrieval software. First is the de-trending of the SS data in order to acquire
5.1 Data processing routine 73 the SNR. The first reflector height estimation (hest) is done using LSA with a pre-known reflection height from the log file of the GNSS station. The second estimation of reflector height h is done using the Lomb-Scargle frequency spectrum. The subsequent amplitude A and phaseϕ estimations are performed using the average reflector height estimation h from the Lomb-Scargle Fast Fourier transform. This two-step process is devised so that the height estimate is as precise as possible with the phase changes only being dependent on the soil conditions.
Figure 5.1: GNSS-R data flow in the GFZ soil moisture retrieval software. SS stands for Signal Strength, SNR - Signal to Noise Ratio, S1, S2 & S5 are the SS measurements on L1, L2 & L5. The final step from satellite selection to soil moisture is explained in chapter 3.5.
The surrounding of the GNSS station is divided into 4 sectors, each 100o wide: sector I covers reflections between [−95o; 5o] azimuth (NW), sector II covers [−5o; 95o] azimuth (NE), sector III covers [175o;−85o] azimuth (SW) and sector IV covers [−175o; 85o] az-imuth (SE). The overlap between the sectors is necessary to accommodate any reflections, happening on the borders between the sectors.
After the heighth, amplitudeA and phaseϕ of the SNR pattern have been calculated for each satellite and each sector. Since GPS satellites make two passes over a GPS station a day (orbital period of 11h 58m), and during each pass they appear from different directions, each satellite contributes with 4 reflections per day, which are located in each of the sectors. Thus 32 satellites in orbit can provide up to 128 reflections per sidereal day. Not all of these reflections, though, are sensible to soil moisture. Reflections from
buildings and roads cannot be used for soil moisture determination. Moreover, since the model of the reflections, presented in chapter 3.5 relies on horizontal, flat surfaces, only stations with level surroundings are suitable for this type of reflectometry measurements.
Thus the not suitable reflections have to be manually disregarded from further processing.
The selection of suitable reflections is done in two steps:
• check the stability of the height and phase estimations over time,
• check the response of the reflections to precipitation events.
The stability of the height estimates is checked through the standard deviation of the height estimates over the years. The check of the stability of the phase estimates is done on an yearly basis for each satellite with the phase changes required to be within 100o of that time. Any outliers are cleaned in this step.
The next step in the processing is a screening of the reflection amplitude estimates.
According toChew et al.(2016), if the estimated amplitude is less than 0.78Anorm1 for an extended period of time, then most likely, vegetation changes are occurring in the station surroundings. This screening has been performed for all stations and if the values of the amplitude are lower, the observations are ignored. The change of receiver, antenna and GNSS satellite influences the amplitude magnitude, thus for stations with longer datasets, these changes have to be taken into account.
After the appropriate reflections are selected, a soil moisture dataset from each satellite for each year is calculated with the lowest 5% of all estimates from each satellite for each year of measurements assigned to a predefined minimum value. The assignment is done on an yearly basis to avoid the influence of any soil erosion, or other changes leading to a trend in the estimates. Then the estimates from all reflections from all sectors are averaged to produce the final soil moisture dataset from the GNSS station. The final dataset is calibrated again to the lowest 5%, as it is done for the individual reflections. This procedure differs from the procedure, used by Chew et al. (2016), where the mean phase from the bottom 15% is used as a calibration setting for the residual soil moisture. By performing the procedure twice, first on a satellite level and then on combined estimated soil moisture level gives very close results to the 15% threshold applied once, as can be seen in the following section.
The final manipulation to the dataset is a smoothing procedure, which eliminates the noise in the data. This smoothing is the reason why the soil moisture values start rising the day before the precipitation occurs. On the other hand the soil moisture datasets have maximums exactly when they are supposed to be, so no information is lost during this procedure. This procedure is employed mainly because of the high daily variations
1Anormis the value of the top 20th percentile of the amplitude data series from all reflections for each satellite
5.2 Validation of GFZ soil moisture retrieval software 75 in the IGS stations, described in 5.4 and appendix D. Such data manipulation has been used byVey et al.(2016a).
5.1.2 GFZ Reflectometry and Atmospheric Database (GRAD)
The GRAD database is developed for the storage and analysis of soil moisture data. It contains all of the above mentioned data and is designed to enable the comparison of processings with different settings for each station. Data both with and without the final smoothing are stored in the GRAD database.
Similarly to the SUADA database a parallel database, oriented to soil products is created. The reasons to create the database are:
• to store all data from all different sources,
• to enable comparisons between different processing schemes of the same dataset,
• to create a more robust storage space, less vulnerable to data loss,
• to allow several scientists to work on the same dataset in its latest version.
The GRAD database is only used internally in GFZ. More technical details on the GRAD database can be found in appendix B.2.