*4.3. Discussion*

Compared to SMAP data, the regions with more errors in the inversion results of CYGNSS data are mainly located in high altitude and inland permanent water regions, such as the Western Ghats and the Malwa plateau. The reason for such errors is that CYGNSS uses the DTU10 digital elevation model to calibrate the locations of specular points [23], which does not sufficiently consider the effect of land topography, resulting in high errors in the estimated positions of specular points with altitudes greater than 600 m on land. In addition, the difference in spatial resolution between CYGNSS data and SMAP data can also increase errors in the inversion results. For example, a small area of water may be identified by CYGNSS data, but not by SMAP data with a lower spatial resolution, which will increase the errors in inversion results.

Although the DBNN method could achieve a relatively higher inversion accuracy in flood monitoring, its accuracy would decrease in some special areas, such as flat areas. The DBNN method may misclassify the flat areas as water bodies because both of them have less surface roughness, thus producing similar coherent DDMs. In future research, inversion accuracy in flat areas is expected to be improved by extracting new physical features. In addition, considering that the DBNN method needs to learn an abundance of parameters and takes a large amount of computation to construct the optimal neural network model compared with the traditional methods, more lightweight models for flood monitoring should be anticipated to highly improve their operational efficiency in the future.

#### **5. Conclusions**

This study proposes a DBNN model for GNSS-R flood monitoring, which is mainly composed of a CNN module and a BP neural network module. The former is adopted to extract the underlying abstract features from DDMs, while the latter takes typical GNSS-R physical features and vegetation information as input. This kind of dual-branch neural network scheme can adequately combine GNSS-R physical features with the abstract features mined by CNN, which helps the DBNN model better utilize GNSS-R data for flood inversion and dynamic monitoring of inundation. Taking the study area in South Asia as an example, the effectiveness of the DBNN method was verified by comparison with the SR and PR methods. Then, the 2020 flood inundation in the study area was retrieved by the DBNN method, and it was found that DBNN had significant flood monitoring capabilities and could track the evolution process of floods over time. This study indicates that although CYGNSS is designed to observe ocean surface wind speed during hurricanes, it also possesses a promising future in flood monitoring applications.

**Author Contributions:** Conceptualization, methodology, D.S. and Q.Z.; software, Q.Z.; validation, writing—original draft preparation, D.S., Q.Z. and B.W.; writing—review and editing, D.S., Q.Z., C.Y. and J.X.; visualization, Q.Z.; funding acquisition, D.S. and B.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the National Key Research and Development Program of China under Grant 2019YFC1509202, the National Natural Science Foundation of China under Grant 61371189, 41701513, 41772350, and the Key Research and Development Program of Shandong Province under Grant 2019GGX101033.

**Data Availability Statement:** The CYGNSS datasets used during the study are available from NASA, https://podaac.jpl.nasa.gov (accessed on 10 September 2021). The SMAP datasets analyzed during the current study are available in https://nsidc.org/data/SPL3SMP\_E/versions/4 (accessed on 16 October 2021).

**Acknowledgments:** The authors are grateful to the public CYGNSS data from the NASA Physical Oceanography Distributed Active Archive Center and SMAP data provided by National Snow and Ice Data Center.

**Conflicts of Interest:** There is no conflict of interest among the authors of this paper.
