# Spaceborne GNSS-R Observation of Global Lake Level: First Results from the TechDemoSat-1 Mission
## 3 Results and Discussions
### Limitations of the Data and Methods  
This paper demonstrates the feasibility of using spaceborne GNSS-R data to estimate global lake level, and presents the comparisons with results derived from other altimetry satellites such as CryoSat-2, Jason, and Envisat (the latter two are collected by Hydroweb). Although the consistency found is good, the RMSEs between TDS-1 and other data are quite large (tens of meters). The error sources are identified below.  
Figure 4: Spatial distributions of water level with respect to WGS84 ellipsoid over the Caspian Sea. (**a**) Map of three TDS-1 tracks for the Caspian Sea from 06/07/2015 (red), 08/08/2016 (green), and 06/11/2017 (blue), respectively. The background map shows the spatial variations of water levels observed by CryoSat-2 in 2015; (**b–d**) spatial variations of water levels observed by the corresponding TDS-1 tracks in (a), with CryoSat-2 derived water levels of the same year as a contrast.  
First, the miscorrected effects in the raw GPS data. The magnitudes of orbit error from GPS and receivers are reported as 1 m and 2.6 m [24]. Atmospheric effects (e.g., ionospheric effect, tropospheric effect) can result in an error as high as 20 m, which is double the error in one-way GNSS links [26]. The spatial variability of the above-mentioned effects on the extent of each lake is a matter of centimeters [27].  
Second, poor precision of the TDS-1 L1b data. The L1b data have limited delay resolution (224.3942 ns), limited receiver bandwidth (\(\sim\)2 MHz), and low peak antenna gain (13.3 dB). The mean observed SNR of the reflected signal over lake surface is \(\sim\)7 dB, which corresponds to a tracking noise of \(\sim\)10 m standard deviation [24]. Previous studies have suggested that an ideal GNSS-R configuration for altimetry would require high-gain antennas, wideband ranging codes, and a dual-frequency signal tracking strategy, so that the achievable height precision for a 1 s GNSS-R measurement would be in the order of 1 m [28]. Also, the data have noise that affects the DDM waveforms. These issues could lead to an overall error between 3.5 m and 7 m [29]. Better results can be obtained with larger directivity antennas and less noisy receivers [30]. To guarantee sufficient data samples, we filter the data using thresholds which are relatively lower than the SNR (5 dB), the antenna gain (5 dBi) and the elevation angle (30\({}^{\circ}\)). These thresholds are not optimal since the SGR-ReSI is not designed for altimetry measurements.  
Third, effects from sampling and co-location tolerance. As shown in Figure 4, for large lakes, it is not accurate to use one single value to represent the overall water level without considering the spatial variations. In addition, although the reference of WGS84 ellipsoid is converted to geoid in order to minimize the co-location tolerance, it is not absolutely accurate. The assumed time window tolerance (e.g., the entire summer months) could also lead to the altimetry dispersion, particularly in large lakes.  
Finally, uncertainties from the retrieval algorithm. The altimetry precision obtained using the HALF method cannot represent the truly achievable precision [31], since the tracking HALF point only takes the information near the specular point. One 0.25 chip delay increment could correspond to a height increment of 37-50 m [3]. This study uses the same method as that for retrieving sea surface height in [3]. Although the waveform of lake better maintains the specular characteristics than that over sea surface, the accuracy of water level estimates should be affected by the water-land interactions, particularly for lakes that are not large enough. Several theoretical studies predicted that per-second altimetry precision can range from 0.2 to 3 m depending on GNSS-R mission configurations and processing algorithms [23; 29; 31].  
Figure 5: Water level changes of the Poyang Lake observed by (**a**) TDS-1, CryoSat-2, Hydroweb, and the hydrologic station in 2015, and (**b**) TDS-1, CryoSat-2, and the hydrologic station in 2017.