**1. Introduction**

The arrival of South Asia's annual southwest monsoons usually brings continuous heavy rainfall, leading to significant flood events and other natural disasters in parts of India, Nepal, and Bangladesh. The particularly noticeable flood inundation event in 2020 was South Asia's most significant flood disaster over the past decade, causing huge property losses to local people [1–3]. Due to the highly dynamic nature of floods, rapid and effective flood monitoring is important for early disaster prevention, midterm relief, and post-disaster reconstruction [4].

The existing remote sensing means for flood monitoring mainly include optical and microwave remote sensing. However, optical remote sensing means cannot be used on rainy and cloudy days due to the sensors' inherent characteristics, although they are capable of obtaining Earth surface observations with a satisfactory spatial resolution [4–7]. Conversely, passive microwave sensors, such as radiometers, have the ability to penetrate clouds and heavy fog owing to their long wavelength, which is requisite for flood monitoring as floods often occur during the rainy season. Nevertheless, the low spatial resolution of even dozens of kilometers limits the successful applications of passive microwave sensors for flood monitoring. On the other hand, active microwave remote sensing normally has a higher spatial

**Citation:** Song, D.; Zhang, Q.; Wang, B.; Yin, C.; Xia, J. A Novel Dual-Branch Neural Network Model for Flood Monitoring in South Asia Based on CYGNSS Data. *Remote Sens.* **2022**, *14*, 5129. https://doi.org/ 10.3390/rs14205129

Academic Editors: Dallas Masters, Chun-Liang Lin and Hugo Carreno-Luengo

Received: 13 August 2022 Accepted: 10 October 2022 Published: 14 October 2022

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

resolution than passive microwave remote sensing. However, it also has a relatively lower temporal resolution and is still unable to conduct dynamic flood monitoring in a timely manner [8]. The recently developed Global Navigation Satellite System Reflectometry (GNSS-R) is a novel remote sensing technology for physical parameter inversion by means of GNSS signals reflected from the Earth's surface [9–11]. The Cyclone Global Navigation Satellite System (CYGNSS), launched by NASA, provides openly accessed GNSS-R data, which has been successfully employed in the inversion of sea surface wind speed [12–14], soil moisture estimation [15–17], flood dynamics monitoring [7,18,19], and other features. The CYGNSS constellation constitutes eight small satellites, on which the receivers are mounted to capture the direct and reflected signals from the navigation satellites. The average revisit period of CYGNSS is only 7 hours, and the spatial resolution is about 3.5 km × 0.5 km on the land surface. Compared with other microwave remote sensing technologies, CYGNSS simultaneously provides observations with higher spatio-temporal resolution, which can be more suitable for dynamic flood monitoring [18,20,21].

Research on GNSS-R flood monitoring first began in 2018. Chew produced a flood inundation map using surface reflectivity (SR) on specular points [18]. Based on CYGNSS data, Wei Wan and Wentao Yang also conducted flood monitoring by surface reflectivity in 2019 and 2021, respectively [7,19]. Furthermore, Unnithan produced large-scale, highresolution flood inundation maps in 2020 by combining the feature of signal-to-noise ratio in delay-Doppler maps (DDMs) with the topographic information [22]. Through further research, Chew proposed a theoretical model for flood monitoring based on changes in surface reflectivity in different land cover types in 2020. The research results showed that surface reflectivity was mainly dependent on surface roughness. When flood events occurred, the surface reflectivity in densely vegetated areas greatly varied, while that of the relatively smooth surfaces changed little, both before and after the flood [20]. Later, Al-Khaldi proposed the power ratio (PR) method in 2021 to detect water bodies using the coherent properties of DDMs from CYGNSS, and found that over 90% of the land surface reflections presented incoherent scattering, while about 80% of the coherent reflections were related to water bodies [23].

Reviewing the current GNSS-R flood monitoring methods, it is found that most of them are only based on a specific GNSS-R physical feature, such as SR, PR, or signal-to-noise ratio. However, one single GNSS-R feature cannot simultaneously represent the dielectric constant and roughness of the reflective surface. For example, SR is calculated from the power of the specular point [7], so it mainly represents the dielectric property of the specular point rather than the roughness property of the reflective surface, which is essential for the flood inversion [24]. Furthermore, the impact of vegetation on GNSS (direct and reflected) signals has not yet been considered in the available literature [24,25]. As the primary observation data of GNSS-R, DDMs contain much useful detailed information, such as SR and PR. Some scholars have utilized DDMs-based features for flood monitoring, such as PR and signal-to-noise ratio. However, the valuable information in DDMs has not yet been fully excavated. In recent years, deep learning (DL) has been widely employed to automatically learn feature representations from data and establish the intrinsic relationship between inputs and outputs [26]. Among a variety of DL algorithms, convolution neural network (CNN) has surpassed most other DL algorithms in two-dimensional image processing due to its local connectivity, weight sharing, and down-sampling strategies, which can reduce the complexity of neural networks and successfully learn feature representations of images [26]. Moreover, the back propagation (BP) neural networks have powerful nonlinear mapping ability, which is especially suitable for solving the complicated internal mapping problem between one-dimensional input vectors and outputs [27]. Therefore, by combining a CNN and a BP neural network in parallel, a dual-branch neural network (DBNN) is constructed for better flood monitoring. In the model, the CNN takes twodimensional DDMs as input and automatically extracts the deep abstract features in DDMs. The BP neural network is fed with the existing typical GNSS-R features and the vegetation information provided by the Soil Moisture Active Passive satellite (SMAP) [28].

The rest of this paper is organized as follows. Section 2 provides the descriptions of CYGNSS data, SMAP data, and the study area. Section 3 introduces the proposed method in detail. The experimental results and discussion are given in Section 4. Finally, Section 5 concludes the study.
