# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 3 Soil Moisture Retrieval Methodology
### Data Sets
#### 3.2.1 Reference Soil Moisture Data  
The present study uses daily SM measurements from in situ ISMN sites as reference for the training and validation. The decision to use these data instead of other global SM sources (such as SMAP/SMOS) is built upon three main reasons: (i) comparisons can be made better in a daily basis, compared to 2-3 day revisit time of SMAP and SMOS missions. (ii) This study is conducted to investigate high spatial resolution CYGNSS-based SM estimates; however, comparisons with missions like SMAP or SMOS would require the use of a resolution of roughly 36 km, which under-utilizes CYGNSS's potential. (iii) If the learning is done using SMAP-like satellite observations, the performance of the learning process will be limited by the retrieval performance of that source. For this purpose, a 0.0833\({}^{\circ}\times\) 0.0833\({}^{\circ}\) lat/lon grid (approximately 9 km \(\times\) 9 km around the equator) centering each SM site is considered as a representativeness window where the SM measurements are assumed to be constant. Although this assumption may not always hold as SM can vary much across short distances, it is a necessary assumption in the current state-of-the-science. For instance, the use of SMAP or SMOS missions would require to assume a constant SM value over 36-km regions due to the resolution. Moreover, similar approaches were made in the literature; for example, [PERSON] et al. [56] assumed a coarse 50-km window around the ISMN sites. Hereafter, the SM representativeness window around SM sites will be called a 9-km grid in the manuscript. SM data from 18 ISMN sites throughout North America and Australia are analyzed, considering that there are a sufficient number of data samples (CYGNSS observations, reference SM values, and corresponding ancillary data) to input to the ANN model as well as there being enough variability within each parameter (see Figure 6). Fifteen ISMN stations are chosen from Soil Climate Analysis Network (SCAN) sites from the United States of America [57], and the remaining three are from an OzNet Hydrological Monitoring Network site from Australia [58]. Detailed information about these SM sites are given in Table 2. The locations and International Geosphere-Biosphere Programme (IGBP) land cover types of these ISMN sites are shown in Figure 3, which is visualized by using the MODIS/Terra + Aqua Land Cover Type L3 Yearly Global 500 m V006 data set [59] via Google Earth Engine Python API [60]. The selection of these ISMN sites are based on the following reasons: (i) The latitudinal coverage of CYGNSS (38\({}^{\circ}\) north and 38\({}^{\circ}\) south) limits the use of several networks; for example, no station from Europe can be included in this study. (ii) Because GNSS-R is sensitive only to the top 5 cm of the soil, the ISMN sites that measure SM at this depth are considered comparable to the CYGNSS estimations [56]. For instance, the COSMOS network would be an alternative network, but their measurements are taken from a varying interval of 0-39 cm. (iii) The uniformity of the sensor technology is another constraint. Despite the internal uncertainty of each SM probe, choosing SM networks of the same SM measurement technology would avoid additional biases between the networks. SCAN and OzNet sites are chosen because most of the stations of both networks employ the same instrument (Stevens Water Inc., Hydraprobe). (iv) The diversity and temporal coverage of the published data by the networks is also significant as the present analysis needs annual SM data for 2017 and 2018 as well as the temperature measurements for a quality control. For example, the PBO_H2O network was not included in the analysis because their sites provide SM only (Additionally, their measurements are not based on a physical SM probe; instead, their sites are examples of GNSS interferometric reflectometry based SM estimation).