# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 5 Discussion
(ii) As previously discussed, the CYGNSS data products, which are determined with the use of internal and external parameters (such as GPS EIRP and receiver gain, as well as bistatic ranges due to topography and erroneous SP calculation) would introduce an added layer of uncertainties to the input data of the learning process. A subset of these could be partially corrected by future effort; for instance, the bistatic ranges would be obtained more accurately by a corrected SP calculation strategy with the help of DEM data. (iii) The present method assumes the dominance of coherent reflections over flat and relatively smooth lands covered with non-heavy vegetation canopy, as followed by several previous studies [11; 12; 22; 28]. Indeed, high variability of the topography and vegetation covers makes it impossible to consider pure coherent or incoherent regimes for global applications. In addition,changes in the observation geometry (such as increasing incidence angles) would introduce increasing incoherent scattering that might be comparable to the coherent reflections. The attempt of this study to introduce coherency/incoherency indicators to the learning process would be improved further by incorporating additional CYGNSS observations (such as the entire DDM) to provide and improved coherency detection. It should be also noted that the use of such parameters would necessitate a trade-off between increased accuracy and decreased spatial resolution. Ancillary data are required since CYGNSS measurements are dependent on several bio/geophysical parameters in conjunction with SM, but data sources are not perfect. NDVI is used to account for vegetation attenuation because there is no global data set of another parameter on which the reflectivity is more dependent (such as vegetation optical depth or VWC). NDVI is indeed a metric of vegetation \"greenness\" and does not fully correlate to the attenuation. Moreover, it is derived from optical imaging instruments, meaning that it is vulnerable to the atmospheric or illumination effects such as clouds or night. Such issues can only be diminished to some degree as this study performs such that a sliding window averaging can be employed. NDVI-derived VWC data and h-parameter even have additional biases as we employ land cover-based LUT values for _stem factor_. On the other hand, all of these parameters prove increased accuracy to the algorithm outcomes. Improved acquisition of such ancillary parameters as well as involvement of new input features (such as several vegetation indices) would be of interest in the future. Internal steps and decisions as well as assumptions and simplifications of the proposed methodology might have led to issues that are not clear for the time as well. To illustrate, the use of 4-km grid for averaging the terrain parameters (such as NDVI, elevation, and slope) around the CYGNSS SP might be too simplistic; nonetheless, it relies for now on the present uncertainty of the CYGNSS footprint over land, and it appears to be working. Future work would examine different sizes of such a grid as well as introducing a new grid that is computed accordingly with the along-track direction of the SP. Although we perform quality controls such as applications of CYGNSS quality control flags, removal of measurements with freezing weather conditions, and exclusion of inland water body-exposed observations, modification of the current method (Such as a different percentage threshold for inland water bodies) and/or addition of further quality flags would be investigated. However, it is evident from the data statistics before and after the quality controls of the present study that roughly half of the data samples are thrown away. This implies that further quality controls would result in a reduced size of training data and would require the addition of new observations. In addition, the current quality controls such as removal of inland water bodies above one percent might not be sufficient to overcome erroneous observations.