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Map showing the location of Hulun Lake (c), within the Inner Mongolia Autonomous Region (b), China (a).
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Lakes at a global level have increasingly experienced algal blooms in recent decades, and it has become a key challenge facing the aquatic ecological environment. Remote sensing technology is considered an effective means of algal bloom detection. This study proposed a novel algal bloom detection index (ABDI) based on Sentinel-2 Multispectral Instr...
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... Lake (48°33′ to 49°20′ N, 116°58′ to 117°48′ E) is located in the cold and semi-arid regions of the Inner Mongolia Autonomous Region, China (Figure 1), and is the largest lake in the Inner Mongolia Autonomous Region, with an area of about 2,063 km 2 in 2019 and an average water depth of 5 to 6 m ( Chuai et al. 2012). The study area falls within a temperate continental monsoon climate zone characterized by warm and semiarid summers and cold and long winters. ...
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... present study selected 1,200 random sample pixels from standard false colour images (RGB: B8A/ B4/B3) for each water type, namely algal blooms, clear water, and turbid water. More specifically, samples for algal blooms, clear water, and turbid water were selected from typical algal blooms, cloudless clean water (in the middle of the lake), and estuarine areas of the Kherlen River, respectively ( Figure 1). Figure 2 shows the mean and standard deviation of spectral reflectance, where it is evident that the spectral curve of algal blooms was similar to that of terrestrial vegetation. ...
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... ABDI was further applied to MSI images for analysis of spatial and temporal distributions of algal blooms in Hulun Lake. As shown in Figure 10, algal blooms in Hulun Lake appeared in summer and autumn (July, August and September) in 2019. Algal blooms developed in Hulun Lake in August, during which time the area of algal blooms increase rapidly of a short period of a few days. ...
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... present study conducted a preliminary analysis of the relationships between areas of algal blooms in Hulun Lake and meteorological factors, including temperature, precipitation, duration of sunshine and wind speed. As shown in Figure 10 (b), the average temperature on days with algal blooms in 2019 ranged between 6.8°C and 25°C, which indicated that algal blooms can adapt to a large temperature range. Although the average temperatures over the 19, 24, 29 and 31 August were similar (17.6°C to 18.6°C), a large difference in algal bloom area was evident (50.1 to 384.6 km 2 ). ...
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... is known to dominate algal blooms in the Chaohu Taihu and Dianchi lakes, whereas green macroalgal blooms frequently occur in the Yellow Sea. Figure 11 shows the standard false colour composite MSI maps, the results of extraction of algal bloom area from visual interpretation, and those from the application of the ABDI and their scatterplots in the Chaohu, Taihu and Dianchi lakes and in the Yellow Sea. Table 5 shows the overall accuracies, K and commission/omission errors from the comparison. ...
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... Obtaining information regarding the actual distribution of aquatic vegetation is a prerequisite for deep learning model training and evaluation. Aquatic vegetation can show different spectral and textural characteristics from water in images, so the visual interpretation of remote sensing images is usually used to determine a more accurate distribution of aquatic vegetation [41][42][43]. As shown in Figure 5a, the emergent vegetation appeared as red and pink on the false-color (N-G-R) images, and dark green and grass green on the real-color (R-G-B) images, as shown in Figure 5b, mainly in the lake shore area in a facultative distribution. ...
Aquatic vegetation is an important component of aquatic ecosystems; therefore, the classification and mapping of aquatic vegetation is an important aspect of lake management. Currently, the decision tree (DT) classification method based on spectral indices has been widely used in the extraction of aquatic vegetation data, but the disadvantage of this method is that it is difficult to fix the threshold value, which, in turn, affects the automatic classification effect. In this study, Sentinel-2 MSI data were used to produce a sample set (about 930 samples) of aquatic vegetation in four inland lakes (Lake Taihu, Lake Caohai, Lake Honghu, and Lake Dongtinghu) using the visual interpretation method, including emergent, floating-leaved, and submerged vegetation. Based on this sample set, a DL model (Res-U-Net) was used to train an automatic aquatic vegetation extraction model. The DL model achieved a higher overall accuracy, relevant error, and kappa coefficient (90%, 8.18%, and 0.86, respectively) compared to the DT method (79%, 23.07%, and 0.77) and random forest (78%,10.62% and 0.77) when utilizing visual interpretation results as the ground truth. When utilizing measured point data as the ground truth, the DL model exhibited accuracies of 59%, 78%, and 91% for submerged, floating-leaved, and emergent vegetation, respectively. In addition, the model still maintains good recognition in the presence of clouds with the influence of water bloom. When applying the model to Lake Honghu from January 2017 to October 2023, the obtained temporal variation patterns in the aquatic vegetation were consistent with other studies. The study in this paper shows that the proposed DL model has good application potential for extracting aquatic vegetation data.
... The process of ABs may last for several days or months, but artificial algal cleaning is another factor that needs to be considered, all of which can lead to missed ABs events in monthly or coarser monitoring by RS surveying or in-situ observation, which is unconducive to academic research (Shi et al., 2019). Even if the revisit period of some satellites can reach two or six times a month, the cloudy weather in summer (from June to August, usually the season with the largest area of ABs) will also reduce the availability of data (Cao et al., 2021;Huang et al., 2014;Jing et al., 2019), leading to an underestimate of the intensity and area of ABs. Although benefiting from its excellent temporal resolution, MODIS data is not perfect, and its spatial resolution makes it difficult to apply in small-scale water bodies. ...
... Sensors on these satellites collect data across multiple spectral bands, including the visible, near-infrared, and shortwave infrared ranges. This capability empowers researchers to conduct comprehensive analyses of water quality, sediment concentrations, and the health of aquatic vegetation, which are crucial aspects of water-related studies (e.g., Rodríguez-Benito 2020; Cao et al. 2021;Abou Samra and Ali 2022;Hu 2022). One of its most notable advantages is the open and freely accessible nature of Sentinel-2 data, making it an affordable and accessible resource for researchers and organizations engaged in remote sensing for water-related studies. ...
... Furthermore, Fang et al. (2018) suggested the Adjusted Floating Algae Index (AFAI) index based on an automatic threshold selection approach to extract HABs from the Landsat and MODIS imagery. More recently, Cao et al. (2021) proposed a novel algae index developed on the basis of the spectral bands of the Sentinel-2 imagery, namely the algal bloom detection index (ABDI), and compared its performance with FAI, AFAI, and NDVI indices in detecting algal blooms observed in Hulun Lake using time series imagery. While the studies mentioned above underlined the effectiveness of index-based approaches in floating algae detection, choosing an appropriate threshold value that provides optimum discrimination between algae pixels and non-algae pixels remains a challenging issue that limits the use of these methods (Garcia et al. 2013;Yan et al. 2022;Colkesen et al. 2023). ...
... Atmospheric correction of remotely sensed imagery over waters has received considerable scholarly attention in the literature. When the spectral characteristics of water pollution caused by algae are analyzed, it can be observed that high-density algae-containing pixels have higher reflectance values, especially in the red-edge and NIR regions, than pixels representing turbid and clear water (Hu 2009;Londe et al. 2016;Cao et al. 2021;Xu et al. 2021;Colkesen et al. 2023). On the other hand, the spectral characteristics of pixels containing low-density algae are similar to those of water. ...
One of the main threats to freshwater resources is pollution from anthropogenic activities such as rapid urbanization and excessive agricultural nutrient runoff. Remote sensing technologies have been effectively used in monitoring and mapping rapid changes in the marine environment and assessing the overall health of freshwater ecosystems. The main goal of this study is to comparatively evaluate the performance of index-based and classification-based approaches in mapping dense floating algal blooms observed in Lake Burdur using Sentinel-2 imagery. For index-based mapping, algae-specific indices, namely the Floating Algae Index (FAI), Adjusted Floating Algae Index, Surface Algal Blooms Index (SABI), and Algal Blooms Detection Index (ABDI), were used. At the same time, pixel- and object-based Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Long Short-Term Memory Network (LSTM) were utilized for classification-based algal mapping. For this purpose, seven Sentinel-2 images, selected through time series analysis performed on the Google Earth Engine platform, were used as the primary dataset in the application. The results show that high-density floating algae formations can be detected over 99% by both indices and classification-based approaches, whereas pixel-based classification is more successful in mapping low-density algal blooms. When two-class thematic maps representing water and floating algae classes were considered, the maps produced by index-based FAI using an appropriate threshold value and the classification-based RF algorithm reached an overall accuracy of over 99%. The highest algae density in the lake was observed on July 13, 2021, and was determined to be effective in ~ 45 km² of the lake’s surface.
... Cao et al. proposed a novel index, i.e., ABDI, using the multispectral instrument of the Sentinel-2. ABDI is found to be outperforming FAI and AFAI and is also less sensitive towards thin clouds and turbidity [46]. ...
Multispectral Satellite Imageries (MSI) are strong enough to delineate features of interest by suppressing others. The water index is a method applied to delineate the water features by combining the multispectral band images. This research proposes a novel index for extracting water features from multi-sensor MSIs named Multi Sensor Water Index (MSWI). The proposed index, along with existing indices, is applied on Landsat-8 OLI imageries, a comparative analysis is done, and it is found that the proposed index is outperforming with 99.77% accuracy. The proposed index was validated on other sensors, such as a series of Landsat (5, 7, 8, and 9), Sentinel-2, Resourcesat, and Modis satellite imagery, by mapping water features successfully. Further, the proposed index maps marine pollution by discriminating phytoplankton and algal bloom from the water feature. It is observed that the increase in phytoplankton or algal bloom is the result of eutrophication. The blooming of phytoplankton and algae is on the surface of the water. Thus, it causes shading at the bottom of the marine ecosystem and negatively impacts the growth of seagrasses, dissolved oxygen levels, fish suffocation, water properties, and other bottom habitats. The three test sites, viz., lake Villarrica, lake Okeechobee and the Atlantic Ocean, have been considered to study increasing phytoplankton and algal bloom. The proposed index successfully discriminates the phytoplankton and algal bloom from the water features.
... The first involves remote sensing retrieval of water quality parameters, such as PC concentration, to describe the development status of CBs through changes in algal pigment concentrations Yuan et al., 2018). The second involves the identification and monitoring of CBs through their unique spectral features at 620 nm, allowing for differentiation of CBs from other signals, which can enable monitoring of CBs in terms of range, frequency, and area (Cao et al., 2021;Chen et al., 2021). ...
With the intensification of global warming, eutrophication in lakes at high latitudes of China has become increasingly severe, with the harm of blue-green algae blooms also on the rise. Therefore, it is urgent to conduct research on water quality of lakes in high latitudes. In this study, taking Lake Hulun as an example, a phyco-cyanin (PC) inversion model applicable to Sentinel-3 OLCI data was constructed and applied to the Sentinel-3 dataset from 2016 to 2022 to analyze the spatiotemporal variation characteristics of PC concentration. The driving mechanism of climate factors on PC concentration was explored, and the correlation between PC concentration and Cyanobacterial blooms (CBs) outbreak was analyzed. Results showed that the PC concentration inversion model based on XGBoost (XGB) has the highest accuracy (R 2 = 0.91, RMSE = 76.76 μg/L, and rRMSE = 0.54). Monthly average PC concentration is higher in July (44.52±64.85 μg/L) and lower in October (5.04 ±1.81 μg/L). From 2016 to 2022, the annual average concentration of PC in Hulun Lake in 2022 (38.82±63.34 μg/L) is higher than that in other years, while the annual average PC concentration in 2020 (4.60±1.76 μg/L) is lower. Temperature is the main impacting factor on PC concentration. The variation of PC concentration in Lake Hulun has high spatiotemporal consistency with the proportion of CBs area. In summary, using Sentinel-3 OLCI imagery for long-term remote sensing monitoring of spatiotemporal pattern changes of PC in Lake Hulun, and analyzing its changing characteristics and patterns, is of great significance for early warning of CBs.
... Many studies have shown that satellite optical observation is affected by cloud (or fog), and macroalgae patches or stripes under thick clouds may be missed from MODIS observations, possibly leading to coverage area underestimation to a certain extent (Cao et al. 2021;Hu 2009;Xing and Hu 2016). To ascertain whether AMC in 2021 is affected by this phenomenon, the temporal evolution of macroalgae coverages and the concurrent cloud cover percentages in 2021 are shown in Figure 6. ...
Green macroalgae bloom (GMB), with the dominant species of Ulva prolifera, has regularly occurred since 2007 along the China coast. Although disaster prevention and control achieved favorable results in 2020, the satellite-observed GMB annual maximum coverage (AMC) rebounded sharply in 2021 to an unprecedented level. The reasons for this rebound and the significant interannual variability over past 15 years are still open questions. Here, by using long-term time-series (2007–2022) optical and Synthetic Aperture Radar satellite observations (1000+ scenes), meteorological data and water quality statistics, the mechanism analysis was performed by exploring effects from natural factors and human activities. Two key determinants for AMC are successfully identified from numerous potential factors which are the macroalgae distribution in a key area (the Subei Shoal) during a critical period (from April to May 20) and the nutrient availability. Furthermore, by using these two parameters, a novel model for AMC prediction (R² = 0.87, p < 0.01) is proposed and independently validated, which can reasonably explain the significant interannual variability (2014–2021) and agree well with the latest observation in 2022 (percentage difference 12%). Finally, suggestions are proposed for future disaster prevention and alleviation. This work may aid future bloom prediction and management measure optimization.
... Advances in the spatial, spectral, and temporal coverage of satellite remote sensing imagery have promoted their use towards providing a synoptic view of changes in optically active surface water quality variables Kutser et al., 2005;. They have also been increasingly used to monitor harmful algal blooms in small inland waters (Cao et al., 2021;Lobo et al., 2021). ...
Anthropogenic eutrophication is a global environmental problem threatening the ecological functions of many inland freshwaters and diminishing their abilities to meet their designated uses. Water authorities worldwide are being pressed to improve their abilities to monitor, predict, and manage the incidence of harmful algal blooms (HABs). While most water quality management decisions are still based on conventional monitoring programs that lack the needed spatio-temporal resolution for effective lake/reservoir management, recent advances in remote sensing are providing new opportunities towards better understanding water quality variability in these important freshwater systems. This study assessed the potential of using the Sentinel 2 Multispectral Instrument to predict and assess the spatio-temporal variability in the water quality of the Qaraoun Reservoir, a poorly monitored Mediterranean hypereutrophic monomictic reservoir that is subject to extensive periods of HABs. The work first evaluated the ability to transfer and recalibrate previously developed reservoir-specific Landsat 7 and 8 water quality models when used with Sentinel 2 data. The results showed poor transferability between Landsat and Sentinel 2, with most models experiencing a significant drop in their predictive skill even after recalibration. Sentinel 2 models were then developed for the reservoir based on 153 water quality samples collected over 2 years. The models explored different functional forms, including multiple linear regressions (MLR), multivariate adaptive regression splines (MARS), random forests (RF), and support vector regressions (SVR). The results showed that the RF models outperformed their MLR, MARS, and SVR counterparts with regard to predicting chlorophyll-a, total suspended solids, Secchi disk depth, and phycocyanin. The coefficient of determination (R²) for the RF models varied between 85% for TSS up to 95% for SDD. Moreover, the study explored the potential of quantifying cyanotoxin concentrations indirectly from the Sentinel 2 MSI imagery by benefiting from the strong relationship between cyanotoxin levels and chlorophyll-a concentrations.
... While multispectral imagers do not capture the full spectrum, they have been found to be useful in algae monitoring applications. [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] With a sufficiently low-cost and robust design, several multispectral imagers could be deployed on bridges, trees, posts, etc. along a river stretch to provide long-term information about the algal growth in the river. Our aim is to develop a viable, long-term monitoring system for narrow rivers that cannot be resolved on satellites using a network of remotely operating, low-cost multispectral imagers that are supplemented with a UAV-based hyperspectral imager when conditions are detected that warrant further investigation. ...
... Researchers have applied adaptive thresholding to high-spatial-resolution data. Cao et al. [23] used Sentinel-2 satellite data to detect floating macroalgae in the Hulun Lake area. The combination of images from high-spatial-resolution satellite data and Otsu's method excels in comparison with satellite data with various spatial resolutions. ...
Extensive floating macroalgae have drifted from the East China Sea to Japan’s offshore area, and field observation cannot sufficiently grasp their extensive spatial and temporal changes. High-spatial-resolution satellite data, which contain multiple spectral bands, have advanced remote sensing analysis. Several indexes for recognizing vegetation in satellite images, namely, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and floating algae index (FAI), are useful for detecting floating macroalgae. Thresholds are defined to separate macroalgae-containing image pixels from other pixels, and adaptive thresholding increases the reliability of image segmentation. This study proposes adaptive thresholding using Sentinel-2 satellite data with a 10 m spatial resolution. We compare the abilities of Otsu’s, exclusion, and standard deviation methods to define the floating macroalgae detection thresholds of NDVI, NDWI, and FAI images. This comparison determines the most advantageous method for the automatic detection of floating macroalgae. Finally, the spatial coverage of floating macroalgae and the reproducible combination needed for the automatic detection of floating macroalgae in Kagoshima, Japan, are examined.
... Preprocessing of optical data, particularly for spaceborne imagery, including atmospheric correction and land adjacency correction, is necessary for improved algal bloom monitoring accuracy (Sagan et al. 2020). Reflectance band ratio and spectral index methods using optical imagery have been used for a number of remote sensing applications, including detecting algal blooms (e.g., Bresciani et al. 2011;Cao et al. 2021;Choe et al. 2021;Lobo et al. 2021). Bresciani et al. (2011) used MERIS data for estimating the lake phytoplankton population in Lake Idro in Italy using band ratios (620 nm/560 nm) and for comparisons with in situ data. ...
... Bresciani et al. (2011) used MERIS data for estimating the lake phytoplankton population in Lake Idro in Italy using band ratios (620 nm/560 nm) and for comparisons with in situ data. Cao et al. (2021) applied a spectral index (algal bloom detection index -ABDI) for algal bloom detection in China using Sentinel-2 MSI imagery; the resultant algal bloom maps were consistent with those identified from visual interpretation maps after applying a suitable threshold to ABDI. ABDI was calculated as: ...
... where R Green , R Red , R RE2 , and R NIRn represent the reflectance at Green, Red, Red Edge-2, and NIRn bands, respectively, and λ Red (665 nm), λ RE2 (740 nm), and λ NIRn (865 nm) represent the central wavelengths of Red, Red Edge-2 and NIRn bands, respectively (Cao et al. 2021). In fact, algorithms using the red and near-infrared (R-NIR) portion of the spectrum perform well in coastal and inland turbid eutrophic waters (Gilerson et al. 2010). ...
A large number of freshwater lakes around the world show recurring harmful algal blooms, particularly cyanobacterial blooms, that affect public health and ecosystem integrity. Prediction, early detection, and monitoring of algal blooms are inevitable for the mitigation and management of their negative impacts on the environment and human beings. Remote sensing provides an effective tool for detecting and spatiotemporal monitoring of these events. Various remote sensing platforms, such as ground-based, spaceborne, airborne, and UAV-based, have been used for mounting sensors for data acquisition and real-time monitoring of algal blooms in a cost-effective manner. This paper presents an updated review of various remote sensing platforms, data types, and algorithms for detecting and monitoring algal blooms in freshwater lakes. Recent studies on remote sensing using sophisticated sensors mounted on UAV platforms have revolutionized the detection and monitoring of water quality. Image processing algorithms based on Artificial Intelligence (AI) have been improved recently and predicting algal blooms based on such methods will have a key role in mitigating the negative impacts of eutrophication in the future.
