2.1. Study Areas and Images
The second national survey of lakes in China showed that 85.40% of the 138 lakes
exceeded the eutrophication standard. In addition, 40.10% of the lakes reached to severe eutrophication standard [
25].
As shown in
Figure 1, Taihu Lake (30°56′N–31°33′N, 119°52′E–120°37′E) has a water surface area of 2338 km
2 and an average depth of 1.9 m. Its water quality is closely related to the agricultural irrigation and aquaculture activities in surrounding cities, such as Suzhou, Huzhou, Wuxi, and Yixing [
26,
27]. Chaohu Lake (31°25’N–31°43’N, 117°16’E–117°51’E) is located in the central part of Anhui Province, and has a water surface area of approximately 760 km
2 and a maximum water depth of 7.98 m. Danjiangkou Reservoir (32°36’N–31°33’N, 110°59’E–111°49’E) crosses Henan and Hubei provinces, and consists of Hanjiang Reservoir and Danjiang Reservoir. It has a water surface area of approximately 600 km
2 and a storage capacity of approximately 29.05 billion cubic meters. This reservoir is the water source for the middle route of the South-to-North Water Diversion Project [
28,
29,
30,
31,
32], which is the largest water resources distribution project of China. The project benefits more than 200 million people in 14 cities of four provinces.
In recent years, algal bloom has occurred frequently in Taihu Lake and Chaohu Lake, and seriously affected the quality of the water consumed by the surrounding residents. Rapid and effective remote sensing monitoring of the water environment of these two lakes are urgently required. Although algal bloom has not emerged in Danjiangkou Reservoir, slight eutrophication has occurred in some tributaries. Monitoring the water quality via remote sensing and preventing pollution are the tasks that cannot be ignored. Therefore, this reservoir is also as one of our study areas.
The Sentinel-1A satellite, the Earth observation satellite of the European Space Agency’s Copernicus Initiative, launched on 3 April 2014, and became officially operational in May 2015. The radar sensor works at the C-band. VV polarization and Interferometric Wide-swath mode are chosen in this study. The swath width is 250 km, and the resolution is 5 m × 20 m.
Under the same conditions, there is a great difference in the radar reflected waves of the same ground object from different polarization modes. On a basis of literatures investigating, it can be known that VV polarization is better than that of HH polarization [
33], aimed at the monitoring of water surface. That is the reason why we selected VV polarization for algal bloom monitoring.
The MODIS sensor has been widely used in global environmental monitoring. It consists of 36 spectral bands, and has resolutions of 250 m, 500 m, and 1000 m. The Landsat-8 satellite was launched by NASA in February 2013, and the Operational Land Imager (OLI) includes nine bands with 30 m resolution. In this study, Landsat-8 and MODIS optical images were quasi-synchronized with SAR images, and used to assist the interpretation of SAR images.
The SAR images and optical images used in this study are shown in
Table 1 and
Table 2, respectively.
It can be seen from
Table 1 and
Table 2, that 12 pairs of optical and SAR images were collected on the same day, six pairs were collected at a time interval of one day, and four pairs were collected at a time interval of more than one day. The statistical results are shown in
Figure 2.
2.3. Prior Information of the Dark Region in SAR Image
In order to verify the effectiveness of the proposed method, the prior information of the dark region on SAR image was used to determine the type of dark region.
It is known that the formation of algal bloom is relevant to the position, wind speed, and surroundings conditions. Li et al. [
35] used the long time series MODIS images to monitor algal bloom of Taihu Lake, and found that the northern region is the hardest hit area of algal bloom, and the southern region and center of lake are moderate algal bloom around 2005. The above results are highly consistent with the conclusions of scholars such as Kong et al. [
36,
37]. The study of Yang [
38] showed that algal bloom can drift with the wind. By the flume experiment, Bai et al. [
39] proposed the conception of critical wind speed, which is 3.2 m/s. When the wind is below the critical speed, the algae drifts smoothly on water surface, and when the wind speed is faster than the critical wind speed, algae are mixed in water with the fluctuation of waves. The same conclusion was reached by Kong et al. [
37]. They found that when the wind speed exceeds a certain range (3.1–3.2 m/s), the water surface is free of algal bloom, and the water surface appears with high gray levels on SAR images.
Based on the above conclusions, the field investigations were carried out in 2016 and 2017. The local environmental protection department in Wuxi provided the records of algal bloom in Taihu Lake, which confirmed the results of the literatures. Therefore, the study area of Taihu Lake was considered from four different types of areas, classified by the coverage of algal bloom.
Figure 5 shows four types of areas by different colors.
Severe eutrophication areas: Meiliang Bay is close to the Wuxi city, and there are many tributaries in Zhushan Lake and Western coast. The nutrient-rich agricultural run-off and sewage contribute the aggregation of algal bloom. When the weather condition is suitable, these areas will be the harder-hit areas for the outbreak of algal bloom.
Moderate eutrophication areas: The areas include two regions. The one is the Southern Coast, which is close to the outlet of Taihu Lake. Due to the good fluidity of water, it is the moderate algal bloom area. Additionally, the other one is the center of Taihu Lake, whose terrain benefits the drift and diffusion of water bloom.
Light eutrophication areas: The region is in Gong Lake, located in the northeast of Taihu Lake. Gong Lake is the main water source of Suzhou and Wuxi. Additionally, there are no inflow rivers. Therefore, the water pollution here is relatively light.
Non-algal bloom areas: the regions are in Xu Lake, Donlim Tsui, and East Taihu Lake, located in the east of Taihu Lake. Due to an amount of water plants, water purification capacity is very high in these areas. In addition, the Center of Lake is also the non-algal bloom area, largely due to its position, which is a benefit to the drift and diffusion of algal blooms.
An example is given to illustrate the discrimination process of dark region using the above prior information.
Figure 6 is a pair of optical and SAR image of Taihu Lake (30.55’48’’N–31.33’2.27’’N, 119.52’48’’E–120.38’24’’E). These two images, acquired in 11 September 2015, have a time interval of approximately 7 h. In
Figure 6, five typical regions are marked with rectangles. The zoomed images of each region are shown in
Figure 7.
In
Figure 7a, it is easy to find the aggregation of algal bloom, although influenced by a few clouds. Considering that this region is in the severe eutrophication areas, the long dark region in
Figure 7b is considered as the algal bloom regions.
Comparing
Figure 7d with
Figure 7c, the dark regions are a little closer to the north than the algal bloom regions. According to the meteorological information on 11 September 2015, there is a southeasterly wind at approximately 10:00 a.m. and the real-time wind speed is less than 2 m/s. Additionally, the shape of region ②, a nearly semicircular shore, contributes to the aggregation of algal blooms to some extent. Therefore, the dark regions in
Figure 7d are considered as the algal bloom regions.
Similar to
Figure 7d, the dark regions in
Figure 7f are also regarded as algal bloom.
Large dark regions are visible in
Figure 7h, compared with the same area in
Figure 7g, which covers only a few dark regions in the west. According to the southwesterly wind with a speed of 2 m/s in the center of the lake, it can be concluded that part of the dark regions in
Figure 7h are covered by algal blooms, which flow with the wind to the harbor, and most of the dark regions in
Figure 7h are caused by low winds.
The location of region ⑤ is in the none-algal bloom areas, which is consistent with the status shown in
Figure 7i. Although there are sparse dark regions in
Figure 7j, it can be confirmed that it is not due to algal bloom.
To obtain the prior information of dark regions, several factors including location, meteorology, and optical images, are considered in the study. The prior information provides the basis for manual recognition of algal bloom dark region. The recognition accuracy of the proposed method is assessed by comparing with the results of manual recognition.