# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 3 Soil Moisture Retrieval Methodology
### Data Sets
#### 3.2.4 Quality Controls
For reference, the initial numbers of data samples before the application of the quality controls are given annually in the second and third columns. Percent losses in the data set due to the actual application of the entire quality controls are also given in the fourth and fifth columns. Overall data set sizes and percent changes are given in the bottom-most row. Year 2017 data for the ISMN sites, Reese Center and Willow Wells, have not been analyzed because these stations are located over 1000 meters, and CYGNSS has an elevation upper limit of 600 m for the SP calculation algorithm until December 2017 ([PERSON] et al., 2019). Year 2018 data for the Riesel station have not been analyzed as well due to large blocks of invalid SM data. to filter out the CYGNSS data in this study are as follows: _S-band powered up_, _Large spacecraft attitude error_, _Black-body DDM_, _DDM is test pattern_, _Low confidence GPS EIRP estimate_. CYGNSS measurements with a negative receiver gain estimation are discarded from the analysis. Table 3 shows the percent changes in the data set as if each of the quality flags was applied directly to the original data set. This approach helps demonstrate the true impact of each individual quality control to the data set. The negative receiver gain holds a reasonably big portion (26%) of the CYGNSS observations. Observations with a DDM peak value from outside the range \([7,10]\) (zero-delay corresponds to the 8 th delay bin) are also removed to ensure the error in the CYGNSS SP location estimation due to the terrain elevation is within a reasonable range [22; 35]. In addition, CYGNSS data points with an incidence angle above 65\({}^{\circ}\) are removed due to the poor observation quality, similar to [24]. Inland water bodies have a critical impact on the SM retrieval process because GNSS-R signals get a step reflectivity waveform (sharp increase in the reflectivity) due to the very strong coherency over water surface [12; 20; 22; 24; 35]. Such impacts should be removed prior to the retrieval because they would not reflect SM effects in case the surface water is sufficiently large within the CYGNSS footprint. Being \"sufficiently large\" is commonly considered as even smaller than the first Fresnel zone [22; 35]. Regarding the fact that the CYGNSS spatial resolution would range from a theoretical minimum of 0.6 km to 8.3 km depending on the incidence angles, and relative orientations of the instruments, we considered a size for the open water bodies that is close to the minimum resolution that would work to initiate investigations in this study. Hence, we removed the CYGNSS observations where more than one percent of the 4-km grid is covered by temporary (seasonal) or permanent surface water. We exploited the JRC Yearly Water Classification History, v1.0 data set (a.k.a. Pekel data set) [61], which is a 30 meter-resolution surface water database. Since the data set is only available from 1984 to the year 2015, we used 2015 data for this study. There are four values (0: No data, 1: Not water, 2: Seasonal-temporary water, 3: Permanent water) for any given pixel. We used the values 2 and 3 to perform water body removal, ignoring when or how long the seasonal water body existed in the year. It is evident from Table 3 that around 23% of the entire data set is excluded from the analysis due to inland water bodies. This effect is much higher for particular ISMN sites such as Sellers Lake (Florida, US), Fort Reno (Oklahoma, US), and Starkville (Mississippi, US). Inland water body maps of the ISMN sites can be seen in Figure 5.