# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 6 Conclusions  
To the authors' knowledge, this paper is the first to provide a physics-aware, learning-based, CYGNSS SM retrieval algorithm that uses several input features from ancillary data sets. The theoretical background of ideal CYGNSS-based SM retrieval, challenges with the use of CYGNSS observables, and the applicability of the proposed method as well as input features are provided. The acquisition of the data sets are described in detail, and their distribution statistics are given. Quality controls for preprocessing of the data are proposed. The approach has been applied to reference SM data collected from 18 ISMN stations throughout North America and Australia by using a randomly cross-validated train/test split fashion, and encouraging results have been reported. The trained model has an overall ubRMSE of 0.0544 cm\({}^{3}\)/cm\({}^{3}\) and [PERSON]'s R of 0.9009 on 4808 unseen data samples, which indicates a potential to be applied on global data sets. Individual contributions from input features have also been investigated. The use of _brcs_-derived reflectivity, TES, incidence angle, NDVI, VWC, elevation, slope, and h-parameter appears to significantly increase the model performance. The major contributions of the study to the GNSS-R based SM retrieval efforts in general are as follows:* This retrieval process is an intrinsically nonlinear regression problem and the proposed ANN model demonstrates potential to overcome this problem with its generalization performance. * ANN is a non-parametric learning algorithm, and it, in turn, removes the need for development of a parametric, explicit solution model (physical, regression, and/or empirical). Therefore, it enables using an arbitrary number of input parameters. * Once trained on reference samples, the model does not require SM information from other sources (such as SMAP or SMOS) for SM estimation. This allows the learning model to be trained with any SM source (such as the ISMN sites in this study, or some other point-scale or global-scale SM sources) and perform SM predictions independently from other SM providers. * The algorithm has the potential to provide sub-daily SM estimations as it is applicable to every single CYGNSS observation without a need for spatial gridding or temporal averaging. In other words, it is capable of making a SM prediction by using a single CYGNSS observation supported by the other input features. * The reported overall performances as well as terrain-specific accuracy could be improved by including new data samples into the training as the CYGNSS mission continues to orbit and collect new land data in coming years. * The proposed strategy can help with determination of the calibration/validation sites for CYGNSS- based SM retrieval studies in order to learn the global bio/geophysical dynamics (land covers, topography). Conceptualization: O.E., M.K., D.B. and A.C.G. ; Methodology: O.E. ; Software: O.E. ; Validation: O.E. ; Formal analysis: O.E. ; Investigation: O.E., M.K. and A.C.G. ; Resources: O.E. and M.K. ; Data curation: O.E. ; Writing--original draft preparation: O.E. ; Writing--review and editing: O.E., M.K., D.B. and A.C.G. ; Visualization: O.E. ; Supervision: M.K. ; Project administration: O.E. and M.K. ; Funding acquisition: O.E. and M.K. This research was funded by the NASA Earth and Space Science Fellowship Program--Grant#80 NSSC18K1329. The authors declare no conflict of interest.