Large-Scale Flood Detection and Mapping in the Yangtze River Basin (2016–2021) Using Convolutional Neural Networks with Sentinel-1 SAR Images

Abstract

Synthetic Aperture Radar (SAR) technology offers unparalleled advantages by delivering high-quality images under all-weather conditions, enabling effective flood monitoring. This capability provides massive remote sensing data for flood mapping, while recent rapid advances in deep learning (DL) offer methodologies for large-scale flood mapping. However, the full potential of deep learning in large-scale flood monitoring utilizing remote sensing data remains largely untapped, necessitating further exploration of both data and methodologies. This paper presents an innovative approach that harnesses convolutional neural networks (CNNs) with Sentinel-1 SAR images for large-scale inundation detection and dynamic flood monitoring in the Yangtze River Basin (YRB). An efficient CNN model entitled FloodsNet was constructed based on multi-scale feature extraction and reuse. The study compiled 16 flood events comprising 32 Sentinel-1 images for CNN training, validation, inundation detection, and flood mapping. A semi-automatic inundation detection approach was developed to generate representative flood samples with labels, resulting in a total of 5296 labeled flood samples. The proposed model FloodsNet achieves 1–2% higher F1-score than the other five DL models on this dataset. Experimental inundation detection in the YRB from 2016 to 2021 and dynamic flood monitoring in the Dongting and Poyang Lakes corroborated the scheme’s outstanding performance through various validation procedures. This study marks the first application of deep learning with SAR images for large-scale flood monitoring in the YRB, providing a valuable reference for future research in flood disaster studies. This study explores the potential of SAR imagery and deep learning in large-scale flood monitoring across the Yangtze River Basin, providing a valuable reference for future research in flood disaster studies.

1. Introduction

In the context of global warming, the frequency of extreme weather-related disasters is escalating, resulting in significant human and economic consequences [1]. Among these disasters, floods stand out as the most devastating. Between 1995 and 2005, weather-related disasters affected an average of 205 million people annually, causing substantial casualties and damage [2]. The Yangtze River, Asia’s longest and the world’s third-longest river, holds paramount importance for China. It traverses diverse ecosystems, irrigates one-fifth of China’s land, and sustains one-third of the nation’s population. Historically, the region has been plagued by severe flooding, particularly during the East Asian monsoon season, primarily in June and July [3,4]. Notably, the Yangtze River Basin (YRB) experienced 24 major flood events caused by heavy rainstorms, ranking among China’s top ten natural disasters from 2005 to 2020, resulting in significant loss of life and property [5]. Given the YRB’s socio-economic significance and susceptibility to frequent floods, effective real-time flood monitoring is crucial, not only for the YRB but for all of China [6].

Satellite remote sensing plays a crucial role in flood detection by swiftly and accurately delineating inundated areas [7]. Flood monitoring primarily relies on two types of satellite data: optical images from sensors such as MODIS, Landsat, and Sentinel-2, and Synthetic Aperture Radar (SAR) images [8,9,10]. While optical sensors are valuable, their effectiveness can be hindered by heavy cloud cover associated with flood events, impeding the acquisition of precise surface information [11,12,13]. In contrast, SAR offers distinct advantages in flood monitoring, functioning under all-weather and all-day conditions, unaffected by cloud cover [11,14,15]. Moreover, SAR’s ability to capture variations in backscattering intensity across different land cover types makes it adept at identifying water bodies within its images. These capabilities have established SAR images as a cornerstone in real-time flood disaster monitoring [16,17,18].

Unlike optical imagery, SAR images require intricate pre-processing due to their imaging mechanisms and noise, which involve procedures such as orbit correction, radiometric calibration, and topographic corrections [18,19]. Various SAR-based flood detection methods have emerged, including threshold-based, region-growing, change detection, and deep learning approaches [20,21,22,23]. The threshold method, a swift and effective image segmentation technique, distinguishes floodwater from the background based on global or regional thresholds [24,25]. However, using a single unchanged threshold for the entire image may compromise accuracy. To address this limitation, subdividing the study area with distinct regional thresholds and constraints [7,26], with the Otsu method, a statistic-based threshold segmentation method, was widely employed in flood detection [27,28]. Moreover, region-growing and change detection methods, each with unique advantages and drawbacks, can be frequently found in relevant studies [29,30,31]. Region-growing excels at boundary segmentation, while change detection effectively rectifies issues such as mistaking mountain shadows for water. Nonetheless, the computational demands for the region-growing scheme are high, while the change detection approach is highly sensitive to the noise of SAR images [32,33]. In summary, the laborious pre-processing and extensive expert involvement in traditional flood detection methods significantly undermine their accuracy and efficiency, rendering them unsuitable for real-time flood monitoring.

To address the limitations of conventional flood monitoring and mapping techniques, deep learning methodologies have gained substantial prominence across diverse remote sensing disciplines, encompassing classification, target detection, risk assessment, and beyond [34,35]. In particular, Convolutional Neural Networks (CNNs), designed as end-to-end image processing models tailored for handling large-scale datasets and intricate classification tasks, have garnered significant attention in recent years. The noteworthy progress in this field includes the introduction of the unsupervised classification model “Felz CNN” for flood mapping in the Yangtze River region [36] as well as the comprehensive evaluation of various CNN models used for flood monitoring in the Poyang Lake region (such as HRNet, DenseNet, SegNet, ResNet, and DeepLab-v3+) [37]. It is noteworthy that global flood datasets have become available for extensive flood-related studies. The United Nations Satellite Centre (UNOSAT) has compiled a repository of over 200 flood events since 2007, resulting in the creation of the UNOSAT Flood Dataset, which leverages various satellite sensors. Additionally, Sen1floods11, a flood detection dataset tailored for deep learning applications, utilizes Sentinel-1 and Sentinel-2 images [38]. Numerous studies have scrutinized the performance of CNNs using these datasets [39,40,41]. In comparison to traditional methodologies, CNNs offer distinct advantages, including high automation, robust scalability, and superior efficiency and accuracy. Nonetheless, the utilization of deep learning technology in the realm of large-scale flood monitoring remains underexplored. For instance, while Reference [37] applied CNNs to flood monitoring in Poyang Lake, this approach has not been extended to large-scale monitoring across the Yangtze River Basin. Additionally, there is a current lack of publicly accessible datasets that can be directly utilized for basin-wide flood monitoring in the YRB.

In this study, we propose an efficient CNN model named FloodsNet, designed for large-scale flood detection and mapping. This model was deployed to perform inundation detection and flood mapping from 2016 to 2021 within the YRB, with the key highlights of the study summarized as follows:

(1)

An accurate flood dataset employing a semi-automatic approach with region thresholding and manual interpretation involved was generated.

(2)

An efficient CNN model, FloodsNet, that harnesses feature reuse and a spatial pyramid mechanism to enhance flood detection capabilities was proposed.

(3)

Inundation detection accuracy under various pre-processing strategies, including polarization, decibel conversion, and DEM adjustment, was systematically assessed.

(4)

Flood detections with the FloodsNet in the Yangtze River Basin spanning from 2016 to 2021, and dynamic monitoring for the floods in Dongting Lake in 2017 and Poyang Lake in 2020 were implemented.

The paper’s structure is organized as follows. Section 2 introduces the study area, data sources, and the proposed methodology. Section 3 outlines the comparative experiments conducted and presents their corresponding results. In Section 4, a synthesis of the experimental findings is provided, along with comprehensive discussions. Finally, Section 5 offers concluding remarks summarizing the key insights and implications of the study.

2. Materials and Methods

2.1. Study Area

The Yangtze River Basin (YRB), depicted in Figure 1, stands as China’s largest basin, covering an expansive drainage area of approximately 1.8 million square kilometers, equivalent to 18.8% of China’s total territory. Originating from the Tanggula Mountains, the Yangtze River stretches an impressive length of 6300 km, traversing 11 provinces. The YRB’s intricate topography showcases a network of tributaries cascading through plateaus, mountains, hills, and plains, before converging in eight other provinces, resulting in its river channels winding through a total of 19 provinces.

The YRB features diverse landforms, including the elevated Qinghai-Tibet Plateau, the Sichuan Basin, the plains within the middle and lower reaches of the Yangtze River, and the hilly regions of Jiangnan, among others. Renowned for its robust agricultural and economic activities, the basin plays a pivotal role in China’s overall grain output and Gross Domestic Product (GDP), contributing approximately 32% and 35% of the nation’s totals, respectively.

The region experiences the influence of a monsoon climate, resulting in uneven temporal and spatial distribution of rainfall. The YRB’s rainy season begins in April and extends through September. Particularly, the middle and lower reaches witness concentrated and sustained rainfall, known as “plum rain,” occurring predominantly from June to July. This climatic pattern often leads to flooding in flat, lower-lying areas, notably in the Dongting and Poyang Lake regions.

Characterized by its advanced agricultural and economic sectors, coupled with recurring flood events, the YRB serves as an exemplary region for in-depth studies related to flood disasters.

2.2. Data

In the preliminary assessment of satellite image availability for our study area, it became apparent that optical satellite images collected during flooding events were significantly obscured by cloud cover. Therefore, this study focused on the period from 2016 to 2021, specifically targeting flooding events within the Yangtze River Basin (YRB) as documented in the annual hydrological reports.

To meet the data requirements of this study, we utilized Sentinel-1, a C-band Synthetic Aperture Radar (SAR) satellite launched by the European Space Agency (ESA) in 2014. The YRB presented challenges due to cloud-covered optical images during flooding, but Sentinel-1’s SAR data proved indispensable. For this analysis, we acquired the Single Look Complex (SLC) and Ground Range Detected (GRD) products obtained in the interferometric wide swath mode (IW) from Sentinel-1. Given that flood detection relies on backscatter intensity information, the GRD data was chosen as the primary data source with an azimuthal resolution of 20 m. Additionally, we integrated a 12.5-m Digital Elevation Model (DEM) generated by ALOS-PALSAR as supplementary data, to assess the performance of our Convolutional Neural Network (CNN) model.

Table 1. Sentinel-1 SAR images available for flooding events from 2016 to 2021 in the YRB. Each image used for training and testing purposes is appropriately labeled. ‘Application’ denotes the utilization of the trained model for direct image prediction without labeled data.

Flood Events	District	Product ID	Date	Train or Test
1	Dongting Lake	011CB0_5A05	2016/06/09	Train and Test
		0127C5_F17D	2016/07/03	Train and Test
2	Poyang Lake	011822_A928	2016/05/30	Train and Test
		012E7C_86E8	2016/07/17	Train and Test
3	Middle reaches of the YRB	011D9A_0801	2016/06/11	Train and Test
		0128B9_886D	2016/07/05	Train and Test
4	Poyang Lake	00A8F1_7632	2017/06/12	Train and Test
		00B2FB_3091	2017/07/06	Train and Test
5	Juzhang River	02747C_139D	2018/07/05	Train and Test
		027F52_9A16	2018/07/29	Train and Test
6	Huaihe River	02836E_FDCB	2018/08/07	Train and Test
		028919_2CEB	2018/08/19	Train and Test
7	Middle reaches of the YRB	032778_40DE	2019/07/02	Train and Test
		032CC4_6DEF	2019/07/14	Train and Test
8	Ruan Jiang	03E75D_6DAE	2020/07/30	Test
		03ED1D_5ADE	2020/08/11	Test
9	Dongting Lake	01C150_503B	2017/06/04	Application
		01D14B_23A9	2017/07/10	Application
10	Poyang Lake	029F8B_298B	2020/06/20	Application
		02AF8A_BF3A	2020/07/26	Application
11	Chaohu Lake	03DB5D_91FD	2020/07/03	Application
		03E612_6A3E	2020/07/27	Application
12	Fujiang River	03EE9F_8BAF	2020/08/14	Application
		04012C_41B2	2020/09/19	Application
13	Dongting Lake	03D52B_49F4	2020/06/19	Application
		03E52E_6E8E	2020/07/25	Application
14	Middle and lower reaches of the YRB	03D2E0_90D3	2020/06/14	Application
		03DD85_0B97	2020/07/08	Application
15	Middle and lower reaches of the YRB	03D2E0_261F	2020/06/14	Application
		03DD85_725A	2020/07/08	Application
16	Upper reaches of the YRB	04A272_97F5	2021/08/16	Application
		04B46E_4D61	2021/09/21	Application

presents detailed information on the image data utilized in this study. Each flood event considered in the analysis was represented by two images acquired during the flooding period and a non-flooding period for comparative purposes. These images were distinctly labeled based on their intended use for either training, testing, or application. The criteria for dividing the image data into training and testing datasets will be elucidated in Section 2.3.2.

In summary, this study involved 14 images capturing 7 flood events for both training and testing, 2 images from 1 flood event reserved exclusively for testing, and 16 images spanning the remaining 8 flood events, utilized solely for application purposes. An additional 16 images were simultaneously utilized for flood monitoring in Dongting Lake and Poyang Lake.

2.3. Methodology

The workflow of this study, depicted in Figure 2, comprises three fundamental components, each of which is elaborated upon in subsequent sections. The methodology includes:

• Data Preprocessing: Prior to employing the threshold method for inundation area extraction, essential preprocessing steps were conducted. The significance and impact of these processes will be elucidated in the subsequent section.
• Flood Dataset Generation: Deep learning methodologies critically depend on the quality and quantity of available datasets. To address this requirement, we introduced a semi-automatic approach that combined the global threshold method with regional thresholding, facilitating the creation of comprehensive flood datasets specific to the YRB.
• CNN Model Development and Flood Detection and Dynamic Monitoring: This study involved a comparative evaluation, contrasting our proposed FloodsNet with a variety of classic CNN models. Subsequently, these models were applied in large-scale flood detection across the Yangtze River Basin, spanning the years 2016 to 2021. Additionally, our investigation encompassed a dynamic flood monitoring aspect, focusing on significant flood-prone regions such as Dongting Lake in 2017 and Poyang Lake in 2020 during their respective flood seasons.

2.3.1. Data Preprocessing

Given the inherent complexities of SAR imaging and the presence of substantial image noise, a series of preprocessing steps are indispensable. In accordance with previous studies [5,19] and Snap 8.0 software guidelines, we implemented six essential preprocessing algorithms:

(1)

Orbit Correction: This involved updating satellite orbit status information within the metadata file.

(2)

Thermal Noise Removal: Our objective was to eliminate noise originating from the SAR satellite system, particularly thermal noise.

(3)

Radiometric Calibration: Intensity data underwent systematic conversion into backscatter coefficient data, thereby enhancing the accuracy of our analysis.

(4)

Speckle Filtering: A crucial step dedicated to removing random speckle noise arising from radar echoes.

(5)

Terrain Corrections: Rectification of distortions induced by factors such as foreshortening, layover, or shadowing effects through the utilization of DEM.

(6)

Decibel Conversion: Converting the radar backscatter values from linear scale to dB scale: We performed the decibel conversion using logarithmic functions to enhance visualization and interpretation. The formula is dB = 10 × log10(P), where P is the intensity of radar echo.

These measures collectively ensure the preparedness of our SAR data for further processing and analysis, aligning with established best practices in remote sensing.

2.3.2. Label Annotation and Flood Dataset

To ensure accurate labeling, we adopted a semi-automatic approach for extracting inundated areas. Initially, we utilized a water index (WI) method based on Sentinel-1 data for image segmentation, employing the following formula:

$$WI=\mathit{ln}\left(10\times VH\times VV\right)-8$$

(1)

Here, VH and VV denote the two polarization bands after preprocessing. Subsequently, we applied a global threshold ranging from 0.3 to 0.4 to roughly segment 16 images corresponding to 8 flood events, taking into account image disparities. Concurrently, specific regions were earmarked as training and test datasets to optimize the efficacy of the deep learning model (further details on the selection strategy will be provided in the subsequent section).

It is pertinent to acknowledge that the similarity in backscattering coefficients between water and mountain shadows can lead to misclassification. To mitigate this, we utilized auxiliary DEM data and the region threshold method to refine the initial segmentation results. In undisturbed mountainous regions, we observed that applying a water index threshold of 0.15–0.2 alongside a slope threshold of 10° effectively rectified the preliminary segmentation outcomes.

Subsequently, the segmented results from the preceding step underwent manual annotation. During manual labeling, we referred to the Sentinel-1 images and water index to modify the previous segmentation results. Significant enhancements are evident in the outcomes, as illustrated in Figure 3, particularly with the incorporation of the global threshold method. The spatial distribution of the 16 flood events spanning from 2016 to 2021 in the YRB, derived from the annual hydrological report, is delineated in Figure 4.

Figure 4 also presents the distribution of the training and test data, encompassing various land cover types such as mountainous regions, hilly terrain, lakes, rivers, towns, and cultivated land. This diversity ensures the model’s ability to generalize and provides objectivity in evaluation. The selection of input channels holds significance in applying deep learning to remote sensing. As DEM was utilized in the threshold method during labeling, it was included as one of the channels for model training. To address the urgency of flood detection tasks, we retained the original VH and VV bands (Sentinel-1 GRD product data), designated as VH_ORI and VV_ORI, as input channels. Additionally, preprocessed VH and VV bands, labeled VH_PRE and VV_PRE, respectively, were integrated, bringing the total input channels to five.

The training dataset, derived from 14 images capturing 7 flood events, was partitioned into 256 × 256 tiles, resulting in a total of 5296 images. The test dataset was divided into two segments: test dataset 1, extracted from the training images (as depicted in Figure 4b–e), and test dataset 2, sourced exclusively from images designated for testing (as illustrated in Figure 4f). Test dataset 1 shares the same imaging conditions as the training data, while test dataset 2 is derived from a completely unfamiliar image. These datasets comprised a combined total of 13 images in test dataset 1 and 14 images in test dataset 2, each containing 3000²–5000² pixels. Their primary purpose was to evaluate the generalization capability and robustness of the model.

During the testing and application phase, we utilized a sliding window algorithm with a window size of 256 and a step size of 128. Predicted results were generated within the middle quarter of the prediction window (128 × 128). Subsequently, all window predictions were aggregated to form the complete image.

2.3.3. The Proposed Deep Learning (DL) Model FloodsNet for Flood Mapping

In this study, we developed an efficient flood detection model, referred to as FloodsNet, outlined in Figure 5, aimed at extracting multi-scale features and facilitating the reuse of multi-level features for flood mapping. FloodsNet adopts the UNet architecture [42] as its base, with values such as 256², 128², and 64² representing the size of the feature map. The number of channels in all layers is reduced to 128 to minimize the model’s parameter count. The model is structured into down-sampling and up-sampling layers. The down-sampling layer comprises five convolutional blocks, each integrating a ResBlock [43] and a max-pooling layer. The ResBlock consists of three convolutional layers and incorporates a shortcut connection. To augment the model’s receptive field and enable the concurrent extraction of multi-scale features, we introduced two Atrous Spatial Pyramid Pooling (ASPP) layers in the last two convolutional blocks. At each up-sampling layer, we utilize the Skip Connection (SC) structure to merge features between adjacent layers, thereby enhancing feature reuse efficiency.

Atrous Spatial Pyramid Pooling (ASPP)

Figure 6a–c illustrate the operational mechanism of atrous convolution with dilated rates of 1, 2, and 3, respectively, enabling a larger receptive field than traditional convolution to capture features more effectively. The original ASPP employed dilated rates of 6, 12, and 18 to acquire a larger receptive field, which is beneficial for segmenting objects of different sizes.

However, flood segmentation prioritizes obtaining multi-scale detailed features rather than global features through a larger receptive field. Unlike natural images where the same object may exhibit features of varying sizes, floods in remote sensing images often cover a substantial area, rendering extensive dilated rates unnecessary. Figure 6d exhibits the improved structure of ASPP in FloodsNet, where the original dilated rates of 6, 12, and 18 were replaced with rates of 1, 2, and 3, respectively. Simultaneously, feature extraction was performed using an average pooling kernel with a size of 2 and a stride of 1. Inspired by ResNet, we aggregate convolution and pooling results together and add them with input feature maps to enhance the model’s multi-scale feature extraction and reuse capabilities. The ASPP structure can be expressed as:

$$F_{ASPP}=F_O+{Concat(A{C}_1,{AC}_2,{AC}_3,P_2)}_{F_o}$$

(2)

where F_O denotes the original feature maps of the input. AC₁, AC₂, AC₃, P₂ denote the atrous convolution with the dilated rates of 1, 2, 3 and the average pooling with the kernel size of 2, respectively. Concat denotes the function that aggregates the four feature maps.

Skip Connection (SC)

UNet’s architecture stands out for its ability to integrate features from both encoding and decoding layers, effectively addressing the challenge of losing edge and detail features. However, while this design leverages shallow features, it may not fully capture the semantic richness offered by deeper features. To bridge this gap, as illustrated in Figure 7, we introduced the SC structure, strategically employed to enhance deep semantic features during the decoding phase.

As illustrated in Figure 7, it can be observed that deep semantic features from the decoding phase are merged with shallow features from the encoding phase. The resultant integration is then combined with resampled deep features from the preceding layer, facilitating effective reuse of deep features. The SC structure can be expressed as:

$$F_{SC}=Concat\left({DC}_3+C_3\right)+R_2$$

(3)

where DC₃ denotes the deconvolution with kernel size of 3. C₃ denotes the encoding feature maps of the corresponding encoding layer, that is, the shallow features. R₂ denotes the feature map of up-sampling at the decoding layer, that is, the deep features. Concat denotes the function that aggregates the two feature maps.

The Cutting-Edge Models Adopted for Comparisons

In this section, we will revise and enhance the description of five well-established CNN models adopted in present study for comparisons in the comprehensive assessment of inundation detection and flood mapping in the YRB with our proposed FloodsNet.

Table 2. The Comparison Models.

Models	References	Characteristic
FCN-8	[44]	The first CNN segmentation model uses deconvolution instead of fully connected layers.
UNet	[42]	Symmetric encoder decoder architecture and skip connection design.
SegNet	[45]	Its decoder’s use of pooling indices for upsampling, enabling precise segmentation while maintaining low computational overhead and model size.
DeepLab-v3	[46]	Its Atrous Spatial Pyramid Pooling (ASPP) module captures multi-scale context
DeepResUNet	[47]	Reducing model parameters while ensuring segmentation accuracy

presents the seminal literature and key characteristics of each model.

2.3.4. Evaluation Metrics and Experimental Parameters

For the evaluation of flood detection, we utilize a range of crucial metrics to gauge the model’s performance. These metrics are vital for binary classification and offer a comprehensive perspective on the outcomes. The key indicators employed include:

Overall Accuracy (OA): OA offers an overall assessment of the model’s performance. However, it may not be the most suitable metric when dealing with imbalanced positive and negative samples.

$$OA={\displaystyle \frac{TP+TN}{TP+TN+FP+FN}}$$

(4)

Precision: Precision measures the proportion of correctly predicted positive samples out of all the samples predicted as positive.

$$Precision={\displaystyle \frac{TP}{TP+FP}}$$

(5)

Recall: Recall quantifies the proportion of correctly predicted positive samples out of all the actual positive samples.

$$Recall={\displaystyle \frac{TP}{TP+FN}}$$

(6)

F1_score: F1_score is a comprehensive metric that balances precision and recall, providing an overall measure of classification performance.

$$F1\_score={\displaystyle \frac{2\times Precision\times Recall}{Precision+Recall}}$$

(7)

Kappa: Kappa assesses the agreement between observed accuracy and expected accuracy, accounting for random chance.

$$Kappa={\displaystyle \frac{P_0-Pe}{1-Pe}}$$

(8)

$$P_0=OA$$

(9)

$$Pe={\displaystyle \frac{(TP+FP)\times (TP+FN)+(FP+TN)\times (FN+TN)}{{(TP+TN+FP+FN)}^2}}$$

(10)

To compute these metrics, we analyze the model’s predictions, categorizing them as True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN). These metrics collectively provide a comprehensive evaluation of our flood detection model, as elucidated in

Table 3. Confusion matrix.

Confusion Matrix		Label
		Positive	Negative
Predict	Positive	TP	FP
	Negative	FN	TN

.

The experiments were conducted using the Tensor-flow framework on an NVIDIA GeForce RTX 2080Ti GPU. The specific parameter configurations employed in the experiments are detailed in

Table 4. Experimental parameter settings.

Parameters	Setup
Optimizer	Adam
Batch size	10
Training times	60,000
Initial learning rate	0.0001
Decay strategy	Exponential decay
Decay frequency	10,000 times/0.8

. These parameters are determined through model training and are uniformly applied across all models.

3. Experiments and Results

3.1. Ablation Experiments

In our ablation experiments, we aimed to dissect the contributions of individual components within the flood monitoring model. Quantitative comparisons of CNN model performance were conducted using two distinct test datasets, and the results are summarized in

Table 5. Results of ablation experiments conducted with two distinct test datasets. The first line of each model represents the evaluation metrics obtained with test dataset 1, while the second line represents those with test dataset 2. Values highlighted in bold indicate the highest numbers for corresponding metrics obtained.

Model	OA	Precision	Recall	F1_score	Kappa
Baseline	0.980	0.993	0.938	0.965	0.951
	0.970	0.986	0.867	0.923	0.904
Baseline + Resblock	0.982	0.991	0.948	0.969	0.956
	0.975	0.984	0.893	0.937	0.921
Baseline + Resblock + ASPP	0.987	0.984	0.973	0.978	0.969
	0.981	0.968	0.938	0.953	0.941
Baseline + Resblock + SC	0.985	0.985	0.965	0.975	0.965
	0.979	0.970	0.926	0.947	0.934
FloodsNet	0.990	0.994	0.972	0.983	0.976
	0.985	0.987	0.940	0.963	0.954

.

The baseline model exhibited the lowest values across all evaluation indicators, except for precision. The inclusion of Resblock led to modest improvements in comprehensive evaluation metrics, specifically F1-score and Kappa. However, when ASPP and SC structures were integrated, substantial enhancements in all evaluation indicators were observed. Importantly, the ASPP structure demonstrated more significant improvement compared to the SC structure, highlighting the superior performance of a multi-scale feature structure over a feature reuse structure in flood mapping.

Our FloodsNet model, as introduced in this study, outperformed all the compared models across various metrics, particularly excelling in recall. It is worth noting that these models exhibited better performance with dataset 1 compared to dataset 2, primarily due to the markedly distinct imaging conditions in dataset 2, which encompass variations in factors such as atmospheric conditions, angles of incidence, system noise, and more, all of which were unfamiliar to the model.

To provide a detailed assessment of each model’s performance in flood detection, we conducted visual analysis on an image extracted from dataset 1, as depicted in Figure 8. The red boxes in the figure highlight areas where various model structures exhibited higher rates of false detections and misidentifications compared to FloodsNet.

Upon close examination, it becomes evident that the baseline model experienced significant issues with misidentification, such as incorrectly delineating water boundaries in the central section of Figure 8c and inaccurately detecting aquaculture regions in the lower-right part of the image.

The integration of ASPP (Figure 8e) and SC (Figure 8f) structures notably alleviated mis-detection, with ASPP exhibiting particularly significant improvement.

In contrast, our proposed FloodsNet model (Figure 8g) showcased minimal mis-detection, primarily limited to the aquaculture region. This visual assessment underscores the superior performance of FloodsNet in precise flood detection, with substantial reductions in mis-detection compared to the other structures.

3.2. Model Comparison Experiments

In our comparative model evaluation experiments, we conducted a quantitative evaluation of six distinct models using two separate datasets, as outlined in

Table 6. Comparison results of model experiments with two test datasets. The first line displays the results of each model with test dataset 1, while the second line corresponds to dataset 2. Values highlighted in bold indicate the highest numbers for the respective metrics.

Model	OA	Precision	Recall	F1_score	Kappa
FCN-8	0.974	0.943	0.970	0.956	0.937
	0.961	0.881	0.939	0.909	0.884
UNet	0.986	0.980	0.973	0.976	0.966
	0.978	0.951	0.942	0.947	0.933
SegNet	0.983	0.991	0.953	0.971	0.960
	0.975	0.981	0.897	0.937	0.922
DeepLabv3	0.985	0.988	0.961	0.974	0.964
	0.979	0.976	0.918	0.946	0.933
DeepResUNet	0.986	0.985	0.967	0.976	0.966
	0.979	0.970	0.927	0.948	0.935
FloodsNet	0.990	0.994	0.972	0.983	0.976
	0.985	0.987	0.940	0.963	0.954

.

The findings indicate that FCN-8 displayed the weakest performance, possibly attributed to its direct resampling in the decoding layers without additional training. Among the remaining models, UNet, DeepLabv3, and DeepResUNet showcased similar results, with closely aligned F1-score and Kappa values. Particularly, UNet exhibited the highest recall among all six models.

However, SegNet, lacking feature information from the encoding layers, exhibited lower accuracy compared to the other three models. Notably, our proposed FloodsNet model, incorporating multi-scale features and reusing multi-layer features, achieved superior accuracy across all metrics. Similarly, these models demonstrated better performance with dataset 1 compared to dataset 2.

Figure 9 illustrates the inundation detection results generated by the six models using an image from dataset 1. The red boxes in the figure highlight areas where the various models exhibited higher rates of wrong detection and mis-detection compared to FloodsNet.

Notably, FCN-8 (Figure 9c) displayed a considerable mis-detection rate in small inundated areas, indicating limitations in handling small objects. UNet (Figure 9d) exhibited a similar mis-detection rate, alongside a substantial wrong detection rate. The remaining three models (Figure 9e–g) demonstrated higher mis-detection rates than wrong detection rates. Conversely, our proposed FloodsNet model (Figure 9h) showcased a notably low wrong detection rate, with fewer instances of mis-detection. Particularly, the model excelled in detecting large water areas, highlighting its superior performance.

3.3. Band Comparison Experiments

In our band comparison experiments, we aimed to explore the impact of different bands on the classification performance of FloodsNet. We evaluated 14 different band combinations, and the results, presented in

Table 7. Performance comparisons across various band combinations using two specific test datasets. The bands are denoted as 1, 2, 3, 4, and 5, representing VH_PRE, VV_PRE, VH_ORI, VV_ORI, and DEM, respectively. The first line displays the performance metrics for each model when provided with band combinations from test dataset 1, while the second line corresponds to test dataset 2. Values in bold highlight the highest scores for the respective metrics.

Band	OA	Precision	Recall	F1_score	Kappa
1	0.953	0.950	0.890	0.919	0.886
	0.978	0.956	0.909	0.932	0.915
2	0.954	0.933	0.909	0.921	0.888
	0.956	0.907	0.879	0.892	0.865
3	0.990	0.994	0.972	0.983	0.976
	0.985	0.987	0.940	0.963	0.954
4	0.976	0.967	0.951	0.959	0.942
	0.963	0.947	0.871	0.907	0.885
1, 5	0.955	0.943	0.903	0.923	0.891
	0.972	0.921	0.945	0.933	0.915
2, 5	0.952	0.932	0.903	0.917	0.883
	0.955	0.901	0.880	0.890	0.862
3, 5	0.986	0.987	0.963	0.975	0.965
	0.979	0.973	0.923	0.947	0.934
4, 5	0.975	0.962	0.955	0.958	0.941
	0.962	0.937	0.872	0.903	0.879
1, 2	0.958	0.936	0.921	0.928	0.898
	0.963	0.912	0.910	0.911	0.888
3, 4	0.982	0.971	0.963	0.969	0.957
	0.970	0.963	0.886	0.923	0.904
1, 2, 5	0.946	0.946	0.869	0.905	0.868
	0.961	0.912	0.899	0.906	0.881
3, 4, 5	0.981	0.969	0.967	0.968	0.954
	0.970	0.951	0.899	0.924	0.905
1, 2, 3, 4	0.979	0.970	0.960	0.965	0.950
	0.965	0.913	0.916	0.915	0.893
1, 2, 3, 4, 5	0.980	0.969	0.962	0.965	0.951
	0.965	0.911	0.920	0.916	0.894

, offer valuable insights.

The findings clearly indicate that utilizing the VH_ORI band as the sole input leads to the best performance, with VH polarization demonstrating superior accuracy compared to VV polarization. Surprisingly, the preprocessed bands VH_PRE and VV_PRE not only failed to enhance the model’s performance but also decreased its accuracy. Additionally, the incorporation of DEM had minimal effect on flood detection in our study. Furthermore, the introduction of additional inputs into the model did not yield positive contributions to its performance; rather, it seemed to introduce both information and noise, negatively impacting the classification performance. Moreover, the results from this section indicate that directly utilizing Sentinel-1 GRD data for flood mapping offers improved efficiency and accuracy.

Figure 10 illustrates examples of the results from the band combination experiment using an image from dataset 2. The lowest rates of wrong and mis-detected inundation areas were achieved when using only the VH_ORI band as input (Figure 10e). In contrast, introducing the VV polarization (Figure 10d,f,h,j) led to the emergence of numerous mis-detected inundation areas, highlighting the introduction of noise associated with VV polarization.

The inclusion of DEM in the inputs had minimal effect on the results, as demonstrated in Figure 10c,e,g,i. The mis-detected inundation areas were primarily concentrated along the boundaries of the inundated area, where SAR image backscattering coefficients exhibited similarities. These findings underscore the importance of selecting appropriate bands for flood detection to minimize noise and enhance classification accuracy.

3.4. Flood Monitoring Results

After conducting a comprehensive evaluation of our proposed model, which involved experimental comparisons with various popular models and different band combinations as inputs, we have determined that utilizing the VH_ORI band alone as input is the most effective approach for inundation detection and flood mapping in the YRB. The flood monitoring results, spanning from 2016 to 2021, are showcased in Figure 11.

These findings underscore significant flood events occurring within the YRB during the years 2016, 2017, and 2020. Particularly noteworthy are the floods observed in 2016 along the middle reaches of the YRB, in 2017 surrounding the Dongting Lake region, and in 2020 across the middle and lower reaches of the basin. Additionally, minor floods were recorded in some of the YRB’s tributaries during 2018, 2019, and 2021. The susceptibility of the middle and lower reaches of the YRB, as well as the Dongting Lake and Poyang Lake basin, to flooding can be attributed to various factors. These regions are characterized by persistent heavy rainfall from June to August, coupled with relatively flat terrain and the accumulation of sediments from upstream regions. Furthermore, increased anthropogenic activities contribute to their designation as primary flood-prone areas within the YRB.

4. Discussion

4.1. Polarization and DEM

According to [48], the VH polarization of SAR imagery provides a stronger echo signal in volume scattering and a weaker signal in specular reflection. This characteristic proves advantageous for flood monitoring, as it yields smoother and more homogeneous water surface images with reduced noise and variance between classes. Consequently, VH polarization is favored over VV polarization for flood detection [38,39]. Our own research, detailed in Section 3, further substantiates the superiority of VH polarization for inundation detection and flood mapping in the YRB. Inundation detection and flood mapping within the YRB, as depicted in Figure 12, demonstrated that VV band images exhibited significant noise, particularly in areas surrounding ships. This noise is likely attributed to interference caused by certain objects emitting electromagnetic waves at the same frequency. Notably, several studies have successfully used VV polarization for flood extraction [49,50]. Through our comparison with these studies, we found that VV polarization is particularly sensitive to water bodies, but it may result in misclassification in regions with dense vegetation.

Furthermore, SAR images acquired over mountainous terrain often exhibit shadows influenced by the surrounding topography, which can be erroneously interpreted as water due to their similar intensity [51,52]. Some studies have addressed this issue by utilizing the terrain slope to mask flood detection results, particularly in regions with significant topographical variations [9,11,53].

To delve deeper into the influence of DEM data in our investigation, we conducted statistical analyses on the DEM of both the training and test datasets, as depicted in Figure 13. Our analysis, depicted in Figure 13a, indicates that the average slope of the majority of images in the training dataset is less than 10 degrees. This observation corresponds with the distribution of floods depicted in Figure 4 and Figure 11, primarily within the middle and lower reaches of the YRB. Consequently, we infer that DEM has minimal impact on flood monitoring in these areas. Meanwhile, we added the mountain image in the training dataset, which can also avoid the model recognizing the mountain shadow as water.

It is important to note that our dataset comprises limited instances of flash floods detected in the YRB using Sentinel-1 between 2016 and 2021. For future endeavors, the inclusion of additional flash flood cases will be imperative to ensure the generalizability of the dataset.

4.2. Dynamic Monitoring of Floods in Typical Areas

To evaluate the efficacy and feasibility of our proposed methodology, we performed dynamic inundation detection and flood mapping for the Dongting Lake region in 2017 and the Poyang Lake region in 2020. The results are presented in Figure 14, Figure 15 and Figure 16.

In 2017, the flood season at Dongting Lake commenced in June, covering an initial area of approximately 630 km². By July, most of the lake was inundated, reaching its peak around 5 July, with the flooded area nearly doubling in size. Subsequently, from 29 July to 10 August, the floodwaters gradually receded, resulting in a decrease in flood coverage. However, on 22 August, a new round of flooding was observed in the Dongting Lake region.

The results of flood monitoring for the Poyang Lake region in 2020 are illustrated in Figure 15. In June, the flood situation remained relatively stable. However, starting from 2 July, the inundated area began to escalate, reaching its peak on 14 July. Although the flood coverage started to decrease in August, it remained more extensive compared to June. It is noteworthy that both monitoring periods coincided with the flood season of the YRB, indicating that these events were attributed to flooding rather than variations in water levels between dry and wet periods.

By converting the aforementioned monitoring results into shapefile format and calculating their areas, we obtained the quantitative areal data presented in Figure 16. The dynamic flooding processes at Dongting Lake in 2017 and at Poyang Lake region in 2020 are statistically exhibited in the left and right of Figure 16 in terms of flooding area variations with time, respectively.

4.3. Potential and Limitations

In this research, we apply a deep learning model to conduct large-scale flood detection and mapping in the Yangtze River Basin (YRB). The all-weather, all-day imaging capabilities of Sentinel-1 SAR data provide crucial support for flood detection. The FloodsNet model proposed in this paper is applicable for future flood detection in the Yangtze River Basin, as demonstrated in the flood monitoring of Dongting Lake and Poyang Lake. Meanwhile, in small and medium-sized watersheds, the model also demonstrates good performance, as evidenced by the 2021 flood event in Figure 11d and the 2019 flood event in Figure 11f. It must be emphasized that the dataset and model in this study do not include any flash flood cases, which represents an area for improvement in future work. Although we use a semi-automated method to generate datasets more efficiently, the volume of the datasets remains limited. Looking ahead, we aim to make minor corrections to the flood detection results from our model and directly apply them to the production of flood datasets, making this work more meaningful.

While SAR satellites have an advantage in operational time compared to optical satellites, they are less accurate, especially when detecting specific features like wetlands and submerged vegetation. Recent studies [54] have attempted to augment flood mapping accuracy by integrating optical satellite data. In future work, it may be necessary to consider additional data sources and expand the scope of the current research in order to obtain multi-source remote sensing data that can support flood detection. Furthermore, integrating SAR and optical data to harness the advantages of both will be a challenge in future studies.

5. Conclusions

YRB stands out as one of China’s most flood-prone regions. Throughout this study, we have delineated a comprehensive approach for flood mapping, integrating both dataset production and algorithmic advancements, all rooted in deep learning methodologies.

Initially, we introduced a semi-automatic methodology tailored for generating flood datasets optimized for deep learning applications. This approach has substantially reduced both time and labor investments. Subsequently, we proposed a CNN model, FloodsNet, engineered for large-scale flood detection and mapping within the YRB. Leveraging multi-scale feature extraction and reuse, FloodsNet demonstrated its remarkable efficacy in our experiments. Our findings have underscored the dominance of VH polarization in flood detection, while also illustrating the minimal impact of DEM data on flood monitoring within the YRB. Importantly, FloodsNet emerged as the foremost model in our comparative analysis. Furthermore, the successful deployment of FloodsNet in flood detection and mapping within the YRB serves as a testament to its robust generalization capabilities.

In summary, this study not only reaffirms the effectiveness and superiority of deep learning methodologies in large-scale flood mapping but also emphasizes their vast potential in the field of flood monitoring.