# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 3 Soil Moisture Retrieval Methodology
### Data Sets
#### 3.2.3 Ancillary Data  
The use of ancillary data as input to the retrieval process is required since the GNSS-R reflectivity is sensitive to not only SM but also other geophysical parameters such as vegetation canopy, topography, surface roughness, and soil texture. It should also be noted that the calculated reflectivity involves the effects from these parameters and are not corrected prior to the retrieval in this study. We used several data sets to represent these parameters as the following input features to the learning model: (i) NDVI, (ii) VWC, (iii) Elevation, (iv) Slope, and (v) h-parameter (Roughness parameter). These input features are computed for every CYGNSS data sample in the analysis and given as input to ANN with the corresponding CYGNSS observables and SM value. Before getting insight into the ancillary data acquisition, the spatial resolution of this algorithm should be explained. As defined in [35], the semi-major and semi-minor axes of the first Fresnel zone ellipse, where the coherent signals come from, varies between 0.6 km and 0.9 km, as well as 0.6 km and 2.3 km, respectively, for a change of SP incidence angle from 0\({}^{\circ}\) to 65\({}^{\circ}\). Depending on the relative orientation of the CYGNSS spacecrafts and GPS transmitters, the first Fresnel zone gets a varying orientation with respect to the along-track direction of the CYGNSS receivers as well. The distance traveled by the SP during the incoherent integration of the GNSS-R signals for one second is roughly 6 km, and it adds an elongation effect to the first Fresnel zone along track direction. In the marginal case where either (i) the semi-major, or (ii) semi-minor axis aligns with the along-track direction, such an elongation would only affect that axis. As a result, the final size of the CYGNSS footprint, which can no longer be an ellipse after the elongation, should, in marginal case (i), vary between 6.6 km and 8.3 km in the along-track direction, whereas the cross-track direction would only vary from 0.6 km to 0.9 km, depending on the incidence angle variations. Therefore, the spatial resolution of CYGNSS would vary between 0.6 km \(\times\) 6.6 km and 0.9 km \(\times\) 8.3 km. Similarly, in the marginal case (ii), the spatial resolution of CYGNSS would vary between 0.6 km \(\times\) 6.6 km and 2.3 km \(\times\) 6.9 km. All other possibilities of the footprint orientation on the surface would result in a spatial resolution in between the minimum and maximum of these marginal cases. On the other hand, computations of the Fresnel zone ellipse would have errors depending on the elevation because of the current SP calculation method of CYGNSS that considers Earth as an ellipsoid without topography [28]. Based on these data facts, we considered that a 0.04 \({}^{\circ}\times\) 0.04\({}^{\circ}\) (approximately 4 km \(\times\) 4 km) lat/lon grid cell that centers the SP could be capable of generating the mean terrain statistics (elevation, slope, NDVI, etc. ), which, in turn, was assumed to correspond to the CYGNSS footprint of interest and define the geophysical conditions around the SP location. This grid cell makes an approximate 4 km spatial resolution for SM retrieval since each CYGNSS observation in the analysis is used separately to generate a SM value. Hence, this grid will be called a 4-km grid from now throughout the manuscript. Figure 4 shows an example of such grids from the analyzed data set. The descriptions of the NDVI, IGBP land cover classifications, elevation and slope, and inland water body data sets will be given later in this section. NDVI represents the vegetation cover above the ground as it was previously exploited by the SM missions [6; 38]. Although by definition NDVI ranges from \(-1\) to 1, normally, it is positive, and getting values closer to 0 or 1 for sparse or dense vegetation canopies, respectively.