Detecting Polder Water Surface Dynamics Using Multi-Source Remote Sensing Data ^†

Abstract

The flow of water in plain river network areas is significantly influenced by various factors, including human activities, upstream water influx, downstream tidal forces, and local rainfall. This leads to a complex situation where poor drainage and flooding are frequent occurrences. Polders play a crucial role in water management and agriculture in China by facilitating drainage and flood control, as well as supporting irrigation and aquaculture. As agriculture and water resource management continue to modernize, the monitoring and analysis of changes in water bodies and levels within polders become increasingly important. This paper primarily focuses on the detection of open water features in polder regions, mainly employing Sentinel-2 satellite imagery. By analyzing these data, we can effectively monitor the changes in the surface areas of water bodies within the polders. For our study, we have selected the Lixiahe region in China as it frequently experiences both flooding and drought conditions and houses a considerable number of polder zones. This region provides an ideal case study to explore the intricate relationship between water management infrastructure and natural hydrological phenomena. The importance of this research is manifold and significant. It advances the capabilities of remote sensing technologies and provides valuable insights for improved water level management in complex agricultural landscapes. The research introduces new methods and technical support for the remote sensing of water level changes in polders, contributing scientific support for enhanced water management and agricultural water conservation.

1. Introduction

Water resources are considered one of the most important strategic resources in nature and have always garnered widespread public attention [1]. With the growth of the global population and the acceleration of industrialization, water resource management has become increasingly urgent and complex. Exploring the spatial distribution patterns of water resources is crucial not only for understanding and predicting regional water supply and demand but also for the sustainable development of humanity.

With the rapid development of remote sensing technology and the improvement in data acquisition capabilities, the application of remote sensing technology in water resource research has become increasingly widespread and important. Especially in large-scale spatial studies, remote sensing technology is regarded as one of the most efficient methods for effectively acquiring and analyzing water body information. Currently, efficiently and quickly obtaining water body information is a key focus in the field of remote sensing research.

McFeeters et al. constructed the Normalized Difference Water Index (NDWI) based on the principles of the Normalized Difference Vegetation Index (NDVI) using the green and near-infrared bands of Landsat TM data [2]. This index amplifies the strong reflection of water bodies in the green band and the strong absorption characteristics in the near-infrared band, maximizing the distinction between water bodies and background features. In urban water body extraction experiments, Xu et al. replaced the near-infrared band of NDWI with the mid-infrared band, creating an enhanced version called the Modified Normalized Difference Water Index (MNDWI) [3]. MNDWI can be used not only in vegetation-dense areas but also to achieve good results in extracting water bodies within urban areas. Mo et al. proposed the Composite Index for Water Bodies (CIWI), which significantly enhances the separation of water bodies from urban areas and effectively extracts water body information [4]. Yan et al., based on the analysis of spectral characteristics of water bodies and background features in semi-arid regions, introduced the Enhanced Water Index (EWI), which combines GIS technology to effectively suppress noise from soil and vegetation in semi-arid areas [5]. Cao et al. developed the Revised Normalized Difference Water Index (RNDWI) based on the red and short-wave infrared bands of TM images, effectively reducing the impact of mixed pixels and shadows [6]. Shen et al. proposed the GAUSS Normalized Difference Water Index (GNDWI) for extracting river water bodies, aiming to minimize interference from ice, snow, and shadows while ensuring the integrity of river extraction results [7]. Facing complex background features including numerous terrain shadows, Feyisa et al. developed the Automated Water Extraction Index (AWEI), where AWEIsh is used for water body extraction, focusing on shadow interference, while AWEInsh is more suitable for removing dark building interference in urban backgrounds [8]. Zhou et al., addressing the issue of fewer infrared bands in some high-resolution images, significantly improved the separability of urban built-up areas and river water bodies based on modifications to the green band, creating the False Normalized Difference Water Index (FNDWI) without requiring mid-infrared bands [9]. In experiments on mountainous water body extraction, Chen et al. developed the Shadow Water Index (SWI). SWI, combined with decision tree algorithms, effectively eliminates disturbances from snow cover and bare soil [10].

This study utilizes Sentinel-2A satellite imagery data, focusing on the Lixiahe region as the research area. It employs both NDWI and MNDWI methods for water body extraction and compares their advantages and disadvantages based on the conditions for water body extraction.

2. Study Region and Data Set

2.1. Region of Interest

The Lixiahe region (31°55′–34°19′ N, 118°20′–121°12′ E) is located in the central part of Jiangsu Province, China, situated between the Yangtze River and the Huai River, as shown in Figure 1. Covering a total area of approximately 17,000 square kilometers, it is a typical low-lying plain wetland area in eastern China. The basin is bordered by the Yangtze River to the south, the Yellow Sea to the east, the Jiangsu Plain to the west, and the Huai River Basin to the north, giving it a geographically advantageous position. The polders in the Lixiahe region are divided into fifteen regions. The names of each region are as follows (

Table 1. The names of the divided regions in the Lixiahe region.

Number	Name	Number	Name
1	Qin Tong region	9	Kua Tao region
2	Xing Hua region	10	Yun Mian River region
3	San Duo region	11	Li Min River region
4	Jian Hu region	12	Xi Chao River region
5	She Yang Town region	13	Da Feng region
6	Yan Cheng region	14	Chuan Dong Port region
7	Fu Ning region	15	Dong Tai region
8	She Yang Riverbank region	16	Nan Tong region

):

2.2. Sentinel-2

Sentinel-2 is a part of the European Space Agency’s (ESA) Copernicus Programme [11]. The Sentinel-2 mission consists of two satellites, Sentinel-2A and Sentinel-2B, which are designed to provide high-resolution optical imagery. These satellites offer detailed data across 13 spectral bands, with spatial resolutions of 10 m, 20 m, and 60 m, depending on the band. The revisit time is approximately five days at the equator when both satellites are operational, ensuring frequent and reliable coverage. Sentinel-2 data have been continuously collected since 2015 (for Sentinel-2A) and 2017 (for Sentinel-2B), providing valuable long-term Earth monitoring. Sentinel-2A multispectral remote sensing image data were obtained from the European Space Agency’s website (https://scihub.copernicus.eu/; accessed on 5 July 2023).

3. Methodology

To calculate the regulated storage capacity and water area changes over time in the Lixiahe region and to compare the advantages and disadvantages of NDWI and MNDWI methods for water body extraction, the research workflow is presented below, as shown in Figure 2. Firstly, using the latitude and longitude of the Lixiahe region as the selection criteria, Sentinel-2A remote sensing images from January to December 2023 were selected, ensuring that the actual cloud cover was less than 5% as the basis for image selection, resulting in 142 acquired images. The obtained image data underwent uniform preprocessing. Subsequently, NDWI and MNDWI methods were employed separately for water body extraction, followed by a comparative analysis of the water body results.

For the processed band data, NDWI and MNDWI, are, respectively, used to extract water bodies. The calculation formula of MNDWI and NDWI is as follows:

$$\mathrm{M}\mathrm{N}\mathrm{D}\mathrm{W}\mathrm{I}={\displaystyle \frac{\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}-\mathrm{S}\mathrm{W}\mathrm{I}\mathrm{R}}{\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}+\mathrm{S}\mathrm{W}\mathrm{I}\mathrm{R}}}$$

(1)

$$\mathrm{N}\mathrm{D}\mathrm{W}\mathrm{I}={\displaystyle \frac{\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}-\mathrm{N}\mathrm{I}\mathrm{R}}{\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}+\mathrm{N}\mathrm{I}\mathrm{R}}}$$

(2)

The Green in Equations (1) and (2) is the reflection value of the green band, SWIR is the reflection value of the short-wave infrared band, which is band 11 in the Sentinel-2 data, and NIR is the reflection value of the near-infrared band. In MNDWI and NDWI, water bodies are generally positive, while vegetation and land are supposed to show negative. Therefore, water is extracted using 0 as the threshold value [12].

Based on the measured water level data and water area of the hydrology station, the storage capacity of the polder area is calculated. This will help us monitor the hydrological situation in the polder area and avoid the abuse of water resources and damage to the polder area. This research can greatly strengthen the management of water resources in polder areas and provide a simpler and faster way to understand the source and destination of water resources.

4. Results

Water Body Extraction and Analysis

Based on remote sensing data and water level data, combined with the division of the polders, this study ultimately selects the San Duo region and the Yan Cheng region to analyze the temporal changes of water surface and water body in the region. The locations of the two regions are outlined in teal in the Figure 3 below.

According to the water body obtained by the two water extraction methods, the monthly variation of the water area of the two regions is as follows.

Based on the analysis of Figure 4, it can be seen that in large areas, the overall trend of water body area changes is relatively smooth. The extraction results of the water body area generally range from 100 to 120 square kilometers. The values of the water body area are influenced by seasonal factors, with more water bodies extracted from May to September and smaller values in other months. However, during certain periods, the values change significantly from the norm. The following is an analysis of these anomalies:

• Abnormal increase in MNDWI value on 14 November in the San Duo region by studying the remote sensing image of the Lixiahe region on 14 November, it can be observed that the San Duo region is heavily cloud-covered, with more than half of the area covered by clouds. Consequently, the MNDWI index of this area is significantly elevated, and the NDWI index also shows an upward trend.
• Inconsistent trend from June to August. From 23 May to 24 August, the water body area change curve obtained by the MNDWI method shows a significant upward trend, while the curve obtained by the NDWI method shows a significant downward trend. Analyzing the remote sensing images from 15 June and 5 July as examples, as shown in Figure 5, it can be seen that vegetation in these images has noticeably increased compared to the image from 14 November. Analyzing the principles of the two water extraction methods, NDWI extracts water body information by suppressing vegetation and highlighting water, whereas MNDWI enhances the contrast between water and buildings to extract water body information. Further analysis of the water body extraction results during the research process reveals that in Figure 6, in the vicinity of vegetation, the MNDWI index averaged 0.11 and the NDWI index averaged −0.1. This indicates that the suppression effect of the MNDWI method on vegetation is significantly inferior to that of the NDWI method. Therefore, in images with dense vegetation, the MNDWI method may mistakenly extract some vegetation as water bodies, leading to an abnormal upward trend in the water body area change curve.

5. Conclusions

In order to study the water body change and its influencing factors in the Lixiahe region, this research selected two regions within the Lixiahe region—Area 3 (Sanduo) and Area 6 (Yancheng)—to analyze the water body changes from January to December 2023. The findings indicate that water extraction results are influenced by various factors, including season, weather, cloud cover, and extraction methods. Comparing the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI), it was found that MNDWI generally yields slightly higher extraction results than NDWI. However, MNDWI is more affected by cloud cover and rainy weather conditions. Due to the flat terrain and rich vegetation in the Lixiahe region, the NDWI method proves more effective in suppressing vegetation and highlighting water bodies than MNDWI. The study also reveals that the extracted water body area is mainly influenced by season and weather, with higher water body areas observed during rainy seasons or on rainy days. Additionally, the use of optical imagery results in longer imaging intervals from July to September due to cloud interference.

This study uses Sentinel-2A remote sensing imagery and employs NDWI and MNDWI methods to extract water bodies. This method effectively monitors the hydrological conditions within the polder, preventing the misuse of water resources and damage to the polder. The study reveals the dynamic characteristics of the water surface area. This approach is of significant importance for the management of water resources in the polder. By employing this method, the sources and destinations of water resources can be understood more simply and quickly, enhancing water resource management, providing a scientific basis, preventing the misuse of water resources, and ensuring the ecological health of the polder.