Long-Term Spatial and Temporal Monitoring of Cyanobacteria Blooms Using MODIS on Google Earth Engine: A Case Study in Taihu Lake

The decomposition of algae has a greater impact on heavy metal transformation in freshwater lake sediments than that of macrophytes

Heavy metal (HM) pollution is a major concern in freshwater ecosystem management. The different types of endogenous organic matter and the way their decomposition affects HM transformation in freshwater lakes is not well understood. An ex situ mesocosm study was conducted to compare HM transformation in sediments during anaerobic decomposition of cyanobacterial bloom biomass (CBB) and submerged cyanobacterial vegetation in Lake Taihu, known as Potamogeton malaianus (PM). Microbial community structures were examined through Illumina sequencing of 16S rDNA. Results indicate that Zn had a remarkably higher amount of potential mobile fraction than other heavy metals (Cr, Pb, Cu, Ni, and Cd) detected in sediments, especially in sediments collected from CBB-dominated areas (approximately 150 mg kg-1). CBB decomposition has caused a significant increase in exchangeable Zn content in sediments and a decrease in reducible Zn that was three times greater than PM decomposition. Additionally, oxidizable Zn content declined during CBB decomposition but increased during PM decomposition. Furthermore, the relative abundance of the main fermentative bacteria and some sulfate-reducing bacteria genera (e.g., Desulfomicrobium) were significantly associated with the HM content of exchangeable and reducible fractions during CBB decomposition. Overall, the findings indicate that Zn is more susceptible to endogenous organic matter decomposition than other metals in freshwater lakes, and the impacts of CBB decomposition on the transformation of heavy metals in sediment are greater than that of submerged macrophyte decomposition.

Assessing the impact of China's river chief system on enterprise pollution discharge

The River Chief System (RCS), a pivotal environmental governance policy promoted by the Chinese government, has far-reaching implications for public health. This study aims to comprehensively assess the impact of RCS on corporate pollution emissions, emphasizing its role in improving environmental quality and safeguarding public health. Utilizing a database of industrial enterprises and data from heavily polluting enterprises spanning 2003 to 2013. Manual collation of RCS policy implementation across prefecture-level cities during the same period. Application of the DID method to verify the impact of RCS on the extent of corporate pollution emissions. RCS significantly reduces pollution emissions from enterprises. Heterogeneity analysis reveals RCS to be more effective in addressing visible pollutants in rivers, non-provincial capitals, and heavily polluting industries, resulting in a notable reduction in pollution emissions. Mechanism testing underscores the importance of increasing government attention to environmental protection and strengthening environmental regulation as key factors contributing to RCS's success in reducing pollution emissions from enterprises. Additionally, the study finds that improving the business environment of enterprises, measured through the marketization index, enhances the effectiveness of RCS in improving river pollution by enterprises. This study introduces a new perspective on examining the pollution reduction and abatement effects of RCS, addressing a gap in micro-level research. The findings not only contribute to the understanding of RCS's impact on pollution but also offer valuable insights for governments and policymakers in promoting the further development and implementation of RCS policies. The results of this research are of significant importance in strengthening environmental governance and safeguarding public health. By effectively controlling corporate pollution emissions, RCS contributes positively to improving environmental quality and, consequently, enhancing public health outcomes.

MODIS Terra and Aqua images bring non-negligible effects to phytoplankton blooms derived from satellites in eutrophic lakes

Phytoplankton-induced lake eutrophication has drawn ongoing interest on a global scale. One of the most popular remote sensing satellite data for observing long-term dynamic changes in phytoplankton is Moderate-resolution Imaging Spectroradiometer (MODIS). However, it is worth noting that MODIS provides two images with different transit times: Terra (local time, about 10:30 am) and Aqua (local time, about 1:30 pm), which may result in a considerable bias in monitoring phytoplankton bloom areas due to the rapid migration of phytoplankton under wind or hydrodynamic conditions. To analyze this quantitatively, we selected MODIS Terra and Aqua images to generate datasets of phytoplankton bloom areas in Lake Taihu from 2003 to 2022. The results showed that Terra more frequently detected larger ranges of phytoplankton blooms than Aqua, whether on daily, monthly, or annual scales. In addition, long-term trend changes, seasonal characteristics, and abrupt years also varied with different transit times. Terra detected mutation years earlier, while Aqua displayed more pronounced seasonal characteristics. There were also differences in sensitivity to climate factors, with Terra being more responsive to temperature and wind speed on monthly and annual scales, while Aqua was more sensitive to nutrient and meteorological factors. These conclusions have also been further confirmed in Lake Chaohu, Lake Dianchi, and Lake Hulun. In conclusion, our findings strongly advocate for a linear relationship to fit Terra to Aqua results to mitigate long-term monitoring errors of phytoplankton blooms in inland lakes (R2 = 0.70, RMSE = 101.56). It is advised to utilize satellite data with transit times between 10 am and 1 pm to track phytoplankton bloom changes and to consider the diverse applications resulting from the transit times of Terra and Aqua.

Exploring invertebrate indicators of ecosystem health by focusing on the flow transitional zones in a large, shallow eutrophic lake

The river-lake transitional zone provides a unique environment for the biological community and can reduce pollution inputs in lake ecosystems from their catchments. To explore environmental conditions with high purification potential in Lake Taihu and indicator species, we examined the river-to-lake changes in water and sediment quality and benthic invertebrate communities in the transitional zone of four regions. The spatial variations in the environment and invertebrate community observed in this study followed the previously reported patterns in Taihu; the northern and western regions were characterized by higher nutrient concentrations in water, higher heavy metal concentrations in sediment, and higher total invertebrate density and biomass dominated by pollution-tolerant oligochaetes and chironomids. Although nutrient concentrations were low and transparency was high in the eastern region, the taxon richness was the lowest there, which disagreed with the previous findings and might be due to a poor cover of macrophytes in this study. The river-to-lake change was large in the southern region for water quality and the invertebrate community. Water circulation induced by strong wind-wave actions in the lake sites of the southern region is assumed to have promoted photosynthetic and nutrient uptake activities and favored invertebrates that require well-aerated conditions such as polychaetes and burrowing crustaceans. Invertebrates usually adapted to brackish and saline environments are suggested to be indicators of a well-circulated environment with active biogeochemical processes and a less eutrophic state in Taihu, and wind-wave actions are key to maintaining such a community and natural purifying processes.

Ecological mechanisms of sedimental microbial biodiversity shift and the role of antimicrobial resistance genes in modulating microbial turnover

The mechanisms of phylogenetic turnover of microbial communities to environmental perturbations in sediments remain unclear. In this study, the molecular mechanisms of phylogenetic turnover, and impact of antibiotics and antibiotic resistance genes (ARGs) on the modification of microbial assemblages were unravelled. We investigated 306 ARGs, 8 transposases, and 4 integron integrases, bacteria, and eukaryotic diversity through high-throughput quantitative PCR and illumina sequencing, 21 antibiotics and 3 tetracycline byproducts. The freshwater and estuary ecosystems were mainly dominated by genus Sulfurovum and colonised by closely related species compared with the estuary (closeness centrality = 0.42 vs. 0.46), which was dominated by genus Mycobacterium. Eighty-six percent of the ecological process in the bacterial community was driven by stochastic processes, while the rest was driven by deterministic processes. Environmental-related concentrations of antibiotics (0.15-32.53 ng/g) stimulated the proliferation of ARGs which potentially modulated the microbial community assembly. ARG acquisition significantly (P < 0.001) increased eukaryotic diversity through protection mechanisms. ARGs showed complex interrelationships with the microbial communities, and phylum arthropods and Nematea demonstrated the strongest ARG acquisition potential. This study provides key insights for environmental policymakers into understanding the ecological impact of antibiotics and the role of ARGs in modulating the phylogenetic turnover of microbial communities and trophic transfer mechanisms.

Dynamic monitoring and analysis of factors influencing ecological environment quality in northern Anhui, China, based on the Google Earth Engine

Monitoring the ecological environment quality is an important task that is often connected to achieving sustainable development. Timely and accurate monitoring can provide a scientific basis for regional land use planning and environmental protection. Based on the Google Earth Engine platform coupled with the greenness, humidity, heat, and dryness identified in remote sensing imagery, this paper constructed a remote sensing ecological index (RSEI) covering northern Anhui and quantitatively analyzed the characteristics of the spatiotemporal changes in the ecological environment quality from 2001 to 2020. Geodetector software was used to explore the mechanism driving the characteristics of spatial differentiation in the ecological environment quality. The main conclusions were as follows. First, the ecological environment quality in northern Anhui declined rapidly from 2001 to 2005, but the rate of decline slowed from 2005 to 2020 and a trend of improvement gradually emerged. The ecological environment quality of Huainan from 2001 to 2020 was better and more stable compared with other regional cities. Bengbu and Suzhou showed a trend of initially declining and then improving. Huaibei, Fuyang, and Bozhou demonstrated a trend of a fluctuating decline over time. Second, vegetation coverage was the main influencing factor of the RSEI, while rainfall was a secondary factor in northern Anhui from 2001 to 2020. Finally, interactions were observed between the factors, and the explanatory power of these factors increased significantly after the interaction. The most apparent interaction was between vegetation coverage and rainfall (q = 0.404). In addition, we found that vegetation abundance had a positive impact on ecological environment quality, while population density and urbanization had negative impacts, and the ecological environment quality of wetlands was the highest. Our research will provide a theoretical basis for environmental protection and support the high-quality development of northern Anhui.

Laser Remote Sensing of Lake Kinneret by Compact Fluorescence LiDAR

Harmful algal blooms in freshwater reservoirs became a steady phenomenon in recent decades, so instruments for monitoring water quality in real time are of high importance. Modern satellite remote sensing is a powerful technique for mapping large areas but cannot provide depth-resolved data on algal concentrations. As an alternative to satellite techniques, laser remote sensing is a perspective technique for depth-resolved studies of fresh or seawater. Recent progress in lasers and electronics makes it possible to construct compact and lightweight LiDARs (Light Detection and Ranging) that can be installed on small boats or drones. LiDAR sensing is an established technique; however, it is more common in studies of seas rather than freshwater reservoirs. In this study, we present an experimental verification of a compact LiDAR as an instrument for the shipborne depth profiling of chlorophyll concentration across the freshwater Lake Kinneret (Israel). Chlorophyll depth profiles of 3 m with a 1.5 m resolution were measured in situ, under sunlight conditions. A good correlation (R² = 0.89) has been established between LiDAR signals and commercial algae profiler data. A non-monotonic algae depth distribution was observed along the boat route during daytime (Tiberias city-Jordan River mouth-Tiberias city). The impact of high algal concentration on water temperature laser remote sensing has been studied in detail to estimate the LiDAR capability of in situ simultaneous measurements of temperature and chlorophyll concentration.

Is there any difference on cyanobacterial blooms patterns between Lake Chaohu and Lake Taihu over the last 20 years?

Serious cyanobacterial blooms (CBs) caused by lake eutrophication have become a global ecological and environmental problem and have adversely affected the production, life, and health of human beings. Lake Chaohu and Lake Taihu are two large closed shallow eutrophication lakes in the Yangtze River Delta in China with frequent CBs. In this study, the floating algae index (FAI) algorithm was applied to detect a long-time CBs sequence using Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2019. The common characteristics and differences of the CBs patterns were further explored in both lakes over the last 20 years. The results showed that the severity of CBs in Lakes Chaohu and Taihu presented a similar trend of decreasing and then increasing during the period of 2000-2004 and 2005-2007, respectively. Although the severity of CBs in the two lakes was alleviated after 2008, CBs in Lake Taihu has gradually increased since 2011 and severe CBs broke out again in 2017 and 2019. Meanwhile, the CBs in Lake Chaohu have varied significantly in different years, and severe CBs were observed in 2012, 2014-2015, and 2018-2019, while in other years, CBs remained relatively low level. The high-frequency regions of CBs were mainly concentrated in the western part in Lake Chaohu and in Zhushan Bay and Meilian Bay in Lake Taihu in the initial years of 2000. However, since 2005, the CBs in Lake Chaohu gradually expanded to the central and eastern parts, and to the northwestern and western shore in Lake Taihu. Furthermore, the relationship between the monthly mean area of CBs (CBs_mean) and environmental factors based on principal component analysis (PCA) indicated that temperature was the most important driving factor affecting CBs patterns. Compared to the period from 2001 to 2007, TP played a more important role in both lakes from 2008 to 2019. Various management measures have been adopted to reduce CBs in both lakes and these methods can effectively remove cyanobacteria in a short time, but they do not change CBs patterns in the long period.

Novel simulation of aqueous total nitrogen and phosphorus concentrations in Taihu Lake with machine learning

This study demonstrates the utility of internal nutrient loads as an additional parameter to improve the performance of machine learning models in predicting the temporal variations of aqueous TN and TP concentrations in Taihu Lake, a large shallow lake. Internal loads, as a potential input parameter for machine learning models, were estimated using a mass balance calculation. The results showed that between 2011 and 2018 the maximum monthly internal loads of nitrogen and phosphorus in Taihu Lake were 4200 t and 178 t, respectively. Monthly changes in the aqueous TN and TP concentrations of Taihu Lake did not correlate significantly with inflow loads whereas the correlations with estimated internal loads were positive and significant. Long short-term memory (LSTM), random forest (RF), and gradient boosting regression tree (GBRT) models were built, and for all of them the inclusion of internal loads in the input parameters improved their performance. LSTM model III, whose input parameters included both inflow loads and internal loads, had the best performance, based on a testing root mean square error of 0.11 mg TN/L and 0.017 mg TP/L. A 28 % decrease in the annual aqueous TP concentration in Taihu Lake in 2018 simulated by LSTM model III was achieved by lowering the average water level from 3.29 m to 2.99 m, suggesting a possible strategy to control the TP concentration in the lake. In summary, our study showed that aqueous TN and TP concentrations in shallow lakes can be simulated using machine learning, with LSTM models outperforming RF and GBRT models; in these models, internal loads should be included as an input parameter. Additionally, our study identified the water level as an important factor affecting the aqueous TP concentration in Taihu Lake.

Long-Term Temporal and Spatial Monitoring of Cladophora Blooms in Qinghai Lake Based on Multi-Source Remote Sensing Images

With climate warming and intensification of human activities, the eco-environmental problems of lakes in middle and high latitudes become increasingly prominent. Qinghai Lake, located in the northeastern of the Tibetan Plateau, is the largest inland saltwater lake in China. Recently, the problem of Cladophora blooms has been widely concerning. In this study, the area of floating Cladophora blooms (hereafter FCBs) in Qinghai Lake from 1986 to 2021 was extracted using Floating Algal Index (FAI) method based on Landsat TM/ETM+/OLI and Sentinel-2 MSI images, and then the intra- and inter-annual variation characteristics and spatial patterns of FCBs were analyzed. The results show that the general change trend of FCBs in Qinghai Lake featured starting in May, expanding rapidly from June to August, and increasing steadily from September to October. From 1986 to 2021, the area of FCBs in Qinghai Lake showed an overall increasing trend in all months, with the largest increase in July at 0.1 km2/a, followed by October at 0.096 km2/a. Spatially speaking, the FCBs area showed a significant increasing trend in the northern Buha River estuary (BRN) and southern Buha River estuary (BRS) regions, a slight increase in the Shaliu River estuary (SR) region, and a decreasing trend in the Quanji River estuary (QR) region and the Heima River estuary (HR) region. The correlation between the meteorological factors and the changes in FCBs was weak, but the increase in flooded pastures in the BRN region (Bird Island) due to rising water levels was definitely responsible for the large-scale increase in FCBs in this region. However, the QB, northeastern bay of Shaliu River estuary (SRB) and HR regions, which also have extensive inundated grassland, did not have the same increase in FCBs area, suggesting that the growth of Cladophora is caused by multiple factors. The complex relationships need to be verified by further research. The current control measures have a certain inhibitory effect on the Cladophora bloom in Qinghai Lake because the FCBs area was significantly smaller in 2017–2020 (5.22 km2, 3.32 km2, 4.55 km2 and 2.49 km2), when salvage work was performed, than in 2016 and 2021 (8.67 km2 and 9.14 km2), when no salvage work was performed.

Trends and Causes of Raw Water Quality Indicators in the Five Most Famous Lakes of Jiangsu Province, China

Due to pollutants from industrial and agricultural activities, the lakes in China are faced with ecological and environmental problems. The five most famous lakes of Jiangsu Province, Taihu Lake, Gehu Lake, Gaobaoshaobo Lake, Hongze Lake, and Luoma Lake, have long-term fixed monitoring points for water body-related indicators. Over a five-year period, the monitoring showed that Gehu Lake had the highest average total nitrogen (TN) and total phosphorus (TP) concentrations among all lakes which were close to the Grade V critical value of the China’s Environmental Quality Standards for Surface Water (CEQSW). The NH₃-N concentrations in all lakes were Grade IV according to the China’s Water Quality Standard for Drinking Water Sources (CWQSDWS) and Grade II according to the CEQSW. In addition, although TP concentrations in Taihu Lake did not exceed Grade V in the CEQSW, TP removal was the main factor controlling eutrophication. It was also found that the petroleum concentrations in all lakes were lower than the Grade I according to the CEQSW. Despite this relatively low petroleum pollution, the concentration of petroleum was negatively correlated with the phytoplankton densities in all lakes. This indicated that phytoplankton density was very sensitive to petroleum concentration. For heavy metals, the concentrations of Pb, Cu, As, and Cd in all lakes were significantly lower than Grade I (CEQSW) from 2013 to 2017. However, the accumulated heavy metals in sediments will remain an important pollution source affecting water quality and aquatic products in the future. The comprehensive pollution index analysis showed that the five lakes were often moderately polluted, indicating that the protection of lake resources in China should not be relaxed for a long time in the future.

Adaptive Threshold Model in Google Earth Engine: A Case Study of Ulva prolifera Extraction in the South Yellow Sea, China

An outbreak of Ulva prolifera poses a massive threat to coastal ecology in the Southern Yellow Sea, China (SYS). It is a necessity to extract its area and monitor its development accurately. At present, Ulva prolifera monitoring by remote sensing imagery is mostly based on a fixed threshold or artificial visual interpretation for threshold selection, which has large errors. In this paper, an adaptive threshold model based on Google Earth Engine (GEE) is proposed and applied to extract U. prolifera in the SYS. The model first applies the Floating Algae Index (FAI) or Normalized Difference Vegetation Index (NDVI) algorithm on the preprocessed remote sensing images and then uses the Canny Edge Filter and Otsu threshold segmentation algorithm to extract the threshold automatically. The model is applied to Landsat8/OLI and Sentinel-2/MSI images, and the confusion matrix and cross-sensor comparison are used to evaluate the accuracy and applicability of the model. The verification results show that the model extraction of U. prolifera based on the FAI algorithm has higher accuracy (R2 = 0.99, RMSE = 5.64) and better robustness. However, when the average cloud cover is more than 70% in the image (based on the statistical results of multi-year cloud cover information), the model based on the NDVI algorithm has better applicability and can extract the algae distributed at the edge of the cloud. When the model uses the FAI algorithm, it is named FAI-COM (model based on FAI, the Canny Edge Filter, and Otsu thresholding). And when the model uses the NDVI algorithm, it is named NDVI-COM (model based on NDVI, the Canny Edge Filter, and Otsu thresholding). Therefore, the final extraction results are generated by supplementing NDVI-COM results on the basis of FAI-COM extraction results in this paper. The F1-score of U. prolifera extracted results is above 0.85. The spatiotemporal distribution of U. prolifera in the South Yellow Sea from 2016 to 2020 is obtained through the model calculation. Overall, the coverage area of U. prolifera shows a decreasing trend over the five years. It is found that the delay in recovery time of Porphyra yezoensis culture facilities in the Northern Jiangsu Shoal and the manual salvage and cleaning-up of U. prolifera in May are among the reasons for the smaller interannual scale of algae in 2017 and 2018.

AlgaeMAp: Algae Bloom Monitoring Application for Inland Waters in Latin America

Due to increasing algae bloom occurrence and water degradation on a global scale, there is a demand for water quality monitoring systems based on remote sensing imagery. This paper describes the scientific, theoretical, and methodological background for creating a cloud-computing interface on Google Earth Engine (GEE) which allows end-users to access algae bloom related products with high spatial (30 m) and temporal (~5 day) resolution. The proposed methodology uses Sentinel-2 images corrected for atmospheric and sun-glint effects to generate an image collection of the Normalized Difference Chlorophyll-a Index (NDCI) for the entire time-series. NDCI is used to estimate both Chl-a concentration, based on a non-linear fitting model, and Trophic State Index (TSI), based on a tree-decision model classification into five classes. Once the Chl-a and TSI algorithms had been calibrated and validated they were implemented in GEE as an Earth Engine App, entitled Algae Bloom Monitoring Application (AlgaeMAp). AlgaeMAp is the first online platform built within the GEE platform that offers high spatial resolution of water quality parameters. The App benefits from the huge processing capability of GEE that allows any user with internet access to easily extract detailed spatial (30 m) and long temporal Chl-a and TSI information (from August 2015 and with images every 5 days) throughout the most important reservoirs in the State of São Paulo/Brazil. The application will be adapted to extend to other relevant areas in Latin America.

Hot moments and hotspots of cyanobacteria hyperblooms in the Curonian Lagoon (SE Baltic Sea) revealed via remote sensing-based retrospective analysis

A temporally and spatially detailed historical (1985-2018) analysis of cyanobacteria blooms was performed in the Curonian Lagoon (Lithuania, Russia), the largest coastal lagoon in the Baltic Sea. Satellite data allowed the mapping of cyanobacteria surface accumulations, so-called "scums", and of chlorophyll-a concentration. The 34-year time series shows a tendency towards later occurrence (October-November) of the cyanobacteria scum presence, whereas the period of its onset (June-July) remains relatively constant. The periods when scums are present, "hot moments", have been consistently increasing in duration since 2008. The differences in the starting, ending and annual duration of cyanobacteria blooms have been significantly altered by hydro-meteorological conditions (river discharge, water temperature, and wind conditions) and their year-round patterns. The most important environmental factors that determined the temporal changes of the scum presence and area were the standing stock of cyanobacteria and the ambient wind conditions. The "hotspots", the areas where the blooms most likely occur, were distributed in the south-southwestern and central parts of the lagoon. The least affected areas were the northern part, which is connected to the coastal waters of the Baltic Sea, and the Nemunas River delta region. The longstanding, well-established spatial patterns of cyanobacteria blooms were linked to hydrodynamic features, namely water renewal time and current patterns, and to potential nutrient sources that included muddy sediments and the locations of colonies of piscivorous birds. Our findings confirmed that the annual and seasonal variations of cyanobacteria blooms and their regulation are a complex issue due to interactions between multiple factors over spatially and temporally broad scales. Despite great progress in the prevention and control of eutrophication and cyanobacteria blooms, the lagoon is still considered to be in a poor ecological status. This work provides a new and missing understanding on the spatial and temporal extent of cyanobacteria blooms and the factors that govern them. Such an understanding can help in planning management strategies, forecasting the magnitude and severity of blooms under changing nutrient loads and potential climate scenarios.

Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

Monitoring harmful algal blooms (HABs) in freshwater over regional scales has been implemented through mapping chlorophyll-a (Chl-a) concentrations using multi-sensor satellite remote sensing data. Cloud-free satellite measurements and a sufficient number of matched-up ground samples are critical for constructing a predictive model for Chl-a concentration. This paper presents a methodological framework for automatically pairing surface reflectance values from multi-sensor satellite observations with ground water quality samples in time and space to form match-up points, using the Google Earth Engine cloud computing platform. A support vector machine model was then trained using the match-up points, and the prediction accuracy of the model was evaluated and compared with traditional image processing results. This research demonstrates that the integration of multi-sensor satellite observations through Google Earth Engine enables accurate and fast Chl-a prediction at a large regional scale over multiple years. The challenges and limitations of using and calibrating multi-sensor satellite image data and current and potential solutions are discussed.

Water Body Extraction from Sentinel-3 Image with Multiscale Spatiotemporal Super-Resolution Mapping

Water body mapping is significant for water resource management. In the view of 21 spectral bands and a short revisit time of no more than two days, a Sentinel-3 OLCI (Ocean and Land Colour Instrument) image could be the optimum data source in the near-real-time mapping of water bodies. However, the image is often limited by its low spatial resolution in practice. Super-resolution mapping (SRM) is a good solution to generate finer spatial resolution maps than the input data allows. In this paper, a multiscale spatiotemporal super-resolution mapping (MSST_SRM) method for water bodies is proposed, particularly for Sentinel-3 OLCI images. The proposed MSST_SRM method employs the integrated Normalized Difference Water Index (NDWI) images calculated from four near-infrared (NIR) bands and Green Band 6 of the Sentinel-3 OLCI image as input data and combined the spectral, multispatial, and temporal terms into one objective function to generate a fine water body map. Two experiments in the Tibet Plate and Daye lakes were employed to test the effectiveness of the MSST_SRM method. Results revealed that by using multiscale spatial dependence under the framework of spatiotemporal super-resolution Mapping, MSST_SRM could generate finer water body maps than the hard classification method and the other three SRM-based methods. Therefore, the proposed MSST_SRM method shows marked efficiency and potential in water body mapping using Sentinel-3 OLCI images.

Automatic Extraction of Open Water Using Imagery of Landsat Series

Open surface freshwater is an important resource for terrestrial ecosystems. However, climate change, seasonal precipitation cycling, and anthropogenic activities add high variability to its availability. Thus, timely and accurate mapping of open surface water is necessary. In this study, a methodology based on the concept of spatial autocorrelation was developed for automatic water extraction from Landsat series images using Taihu Lake in south-eastern China as an example. The results show that this method has great potential to extract continuous open surface water automatically, even when the water surface is covered by floating vegetation or algal blooms. The results also indicate that the second shortwave-infrared band (SWIR2) band performs best for water extraction when water is turbid or covered by surficial vegetation. Near-infrared band (NIR), first shortwave-infrared band (SWIR1), and SWIR2 have consistent extraction success when the water surface is not covered by vegetation. Low filter image processing greatly overestimated extracted water bodies, and cloud and image salt and pepper issues have a large impact on water extraction using the methods developed in this study.