# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
CYGNSS is not designed for land observations; however, a CYGNSS-based, accurate SM retrieval algorithm could enable scientists (i) to specify the requirements for dedicated SM missions of the future, (ii) to create new algorithms utilizing existing land data, and (iii) to discover new calibration/validation approaches for dedicated GNSS-R SM missions. Motivated by this, there have been increasing efforts to develop SM retrieval algorithms for CYGNSS observations. For example, [PERSON] and [PERSON] [22] correlated the changes in the CYGNSS signal-to-noise ratio (SNR) with the SMAP SM estimations, assuming that CYGNSS land measurements are dominated by the coherent reflections. They used mean SMAP SM values as reference with these correlations to obtain daily, CYGNSS-based SM estimations \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Orbit & Non-synchronous \\ Altitude & \(\sim\)520 km \\ Period & 95 min \\ Spatial Resolution & \(\sim\)25 km \(\times\) 25 km (incoherent), \(\sim\)0.6 km \(\times\) 6.6 km (coherent, theoretical minimum) \\ Revisit Times & 3 h median, 7 h mean (over the ocean) \\ Frequency & 1.57542 GHz \\ Polarization & Left-hand circularly polarized (LHCP) (Down-looking GNSS-R antenna) \\ \hline \end{tabular}
\end{table} 
Table 1: Orbital and instrumental specifications of CYGNSS [22; 25]. from SNR changes for each SMAP Equal-Area Scalable Earth (EASE) grid (36 km \(\times\) 36 km) [26]. The overall unbiased root-mean-squared error (ubRMSE) of their algorithm is 0.0450 cm\({}^{3}\)/cm\({}^{3}\). Although the estimation method itself relied on SMAP SM data as a reference, its significant benefit was that linking CYGNSS SNR to SMAP SM products allowed the use of CYGNSS observations to fill in the gaps between the adjacent SMAP observations. [PERSON] and [PERSON] [27] introduced a relative SNR (rSNR) and SM derivation from CYGNSS delay-Doppler maps (DDM) to infill the gap between adjacent SMAP revisits. They reprojected the CYGNSS SNR observations into SMAP's 9-km EASE grids and calculated the average of these grids to acquire daily SM estimations. They combined rSNR with SMAP SM values to acquire daily SM estimations over high vegetation density as well. They reported correlation results at useful levels (Pearson R of 0.77 between CYGNSS-derived SM and SMAP) over moderate vegetation density but with reduced correlations (R = 0.68) over dense vegetation; however, they did not report any error level (such as ubRMSE). [PERSON] et al. [23] did not propose a CYGNSS-based SM estimation method; however, they made use of an approximated CYGNSS reflectivity, which is the ratio of the calibrated reflected and direct SNR measurements, assuming predominant coherent reflections over land. They linked CYGNSS reflectivity approximation to the SM changes for several land cover types. [PERSON] et al. [28] introduced the trilinear regression-based reflectivity-vegetation-roughness (R-V-R) algorithm that derives daily SM estimations at a 36 km \(\times\) 36 km resolution as a function of the CYGNSS reflectivity as well as SMAP vegetation opacity and roughness coefficient. The algorithm was developed considering the dominance of the coherent reflections over land. The R-V-R performance was compared globally to the SMAP SM product and reported to have a RMSE of 0.07 cm\({}^{3}\)/cm\({}^{3}\). [PERSON] et al. [24] proposed a time-series SM retrieval algorithm that produces 3-day and 1-day SM estimates. In contrast to former studies, they assumed that the CYGNSS land returns are mostly driven by the incoherent scattering unless inland water bodies exist within the footprint. Therefore, they used the CYGNSS normalized bistatic radar cross section (NBRCS) and mean-square slope (MSS) instead of the DDM SNR. As a result, they provided SM estimations at relatively coarse resolutions (0.2\({}^{\circ}\times\)0.2\({}^{\circ}\) lat/lon grid roughly 22 km around the equator) with an overall RMSE of 0.04 cm\({}^{3}\)/cm\({}^{3}\).