Sentinel-2 and Landsat-8 Observations for Harmful Algae Blooms in a Small Eutrophic Lake

Widespread harmful cyanobacterial bloom is one of the most pressing concerns in lakes and reservoirs, resulting in a lot of negative ecological consequences and threatening public health. Ocean color instruments with low spatial resolution have been used to monitor cyanobacterial bloom in large lakes; however, they cannot be applied to small water bodies well. Here, the Multi-Spectral Instrument (MSI) onboard Sentinel-2A and -2B and the Operational Landsat Imager (OLI) onboard Landsat-8 were employed to assemble the virtual constellation and to track spatial and seasonal variations in floating algae blooms from 2016 to 2020 in a small eutrophic plateau lake: Lake Xingyun in China. The floating algae index (FAI) was calculated using Rayleigh-corrected reflectance in the red, near-infrared, and short-wave infrared bands. The MSI-derived FAI had a similar pattern to the OLI-derived FAI, with a mean absolute percentage error of 19.98% and unbiased percentage difference of 17.05%. Then, an FAI threshold, 0.0693, was determined using bimodal histograms of FAI images for floating algae extraction. The floating algae had a higher occurrence in the northern region than the southern region in this lake, whilst the occurrence of floating algae in summer and autumn was higher than that in spring and winter. Such a spatial and seasonal pattern was related to the variability in air temperature, wind speed and direction, and nutrients. The climatological annual mean occurrence of floating algae from 2016 to 2020 in Lake Xingyun exhibited a significant decrease, which was related to decreases in nutrients, resulting from efficient ecological restoration by the local government. This research highlighted the application of OLI-MSI virtual constellation on monitoring floating algae in a small lake, providing a practical and theoretical reference to monitor aquatic environments in small water bodies.

[1]  Minqi Hu,et al.  MODIS-Satellite-Based Analysis of Long-Term Temporal-Spatial Dynamics and Drivers of Algal Blooms in a Plateau Lake Dianchi, China , 2019, Remote. Sens..

[2]  Ronghua Ma,et al.  Climate- and human-induced changes in suspended particulate matter over Lake Hongze on short and long timescales , 2017 .

[3]  J. Olesen,et al.  Watershed land use effects on lake water quality in Denmark. , 2012, Ecological applications : a publication of the Ecological Society of America.

[4]  Bryan A. Franz,et al.  Spatially Resolving Ocean Color and Sediment Dispersion in River Plumes, Coastal Systems, and Continental Shelf Waters , 2013 .

[5]  Y. Sheng,et al.  An automated scheme for glacial lake dynamics mapping using Landsat imagery and digital elevation models: a case study in the Himalayas , 2012 .

[6]  C. Justice,et al.  The Harmonized Landsat and Sentinel-2 surface reflectance data set , 2018, Remote Sensing of Environment.

[7]  Chuanmin Hu,et al.  To what extent can Ulva and Sargassum be detected and separated in satellite imagery? , 2021, Harmful algae.

[8]  Bing Zhang,et al.  Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index , 2018, Remote Sensing of Environment.

[9]  Xiaohan Liu,et al.  Long-term MODIS observations of cyanobacterial dynamics in Lake Taihu: Responses to nutrient enrichment and meteorological factors , 2017, Scientific Reports.

[10]  J. Downing,et al.  The global abundance and size distribution of lakes, ponds, and impoundments , 2006 .

[11]  Abhishek Kumar,et al.  A novel cross-satellite based assessment of the spatio-temporal development of a cyanobacterial harmful algal bloom , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[12]  Menghua Wang,et al.  In search of floating algae and other organisms in global oceans and lakes , 2020 .

[13]  E. Jeppesen,et al.  The relative importance of weather and nutrients determining phytoplankton assemblages differs between seasons in large Lake Taihu, China , 2019, Aquatic Sciences.

[14]  A. Michalak,et al.  Using Landsat to extend the historical record of lacustrine phytoplankton blooms: A Lake Erie case study , 2017 .

[15]  Steven A. Loiselle,et al.  Wind Effects for Floating Algae Dynamics in Eutrophic Lakes , 2021, Remote. Sens..

[16]  Junsheng Li,et al.  Landsat-satellite-based analysis of spatial–temporal dynamics and drivers of CyanoHABs in the plateau Lake Dianchi , 2018, International Journal of Remote Sensing.

[17]  Ronghua Ma,et al.  Diurnal changes of cyanobacteria blooms in Taihu Lake as derived from GOCI observations , 2018 .

[18]  H. Paerl,et al.  Mitigating eutrophication and toxic cyanobacterial blooms in large lakes: The evolution of a dual nutrient (N and P) reduction paradigm , 2019, Hydrobiologia.

[19]  Chuanqing Wu,et al.  Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake , 2019, Environmental Science and Pollution Research.

[20]  R. Ma,et al.  Effects of broad bandwidth on the remote sensing of inland waters: Implications for high spatial resolution satellite data applications , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[21]  Chuanmin Hu,et al.  Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data: Application of a virtual baseline reflectance height technique , 2016 .

[22]  Min Zhang,et al.  Fourteen-Year Record (2000-2013) of the Spatial and Temporal Dynamics of Floating Algae Blooms in Lake Chaohu, Observed from Time Series of MODIS Images , 2015, Remote. Sens..

[23]  M. Scheffer,et al.  Warmer climates boost cyanobacterial dominance in shallow lakes , 2012 .

[24]  S. Fawcett,et al.  Potential for High Fidelity Global Mapping of Common Inland Water Quality Products at High Spatial and Temporal Resolutions Based on a Synthetic Data and Machine Learning Approach , 2021, Frontiers in Environmental Science.

[25]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[26]  D. Mishra,et al.  A harmonized image processing workflow using Sentinel-2/MSI and Landsat-8/OLI for mapping water clarity in optically variable lake systems , 2019, Remote Sensing of Environment.

[27]  R. Bukata,et al.  The MERIS Maximum Chlorophyll Index; its merits and limitations for inland water algal bloom monitoring , 2013 .

[28]  P. Leavitt,et al.  Effects of lake warming on the seasonal risk of toxic cyanobacteria exposure , 2020, Limnology and Oceanography Letters.

[29]  J. Brock,et al.  Assessment of estuarine water-quality indicators using MODIS medium-resolution bands: initial results from Tampa Bay, FL , 2004 .

[30]  Lucie Guo,et al.  Doing Battle With the Green Monster of Taihu Lake , 2007, Science.

[31]  H. Paerl,et al.  Cyanobacterial blooms , 2018, Nature Reviews Microbiology.

[32]  Ian W. Housman,et al.  Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States , 2019, Remote Sensing of Environment.

[33]  J C Ho,et al.  Widespread global increase in intense lake phytoplankton blooms since the 1980s , 2019, Nature.

[34]  Quinten Vanhellemont,et al.  Atmospheric correction of metre-scale optical satellite data for inland and coastal water applications , 2018, Remote Sensing of Environment.

[35]  H. Paerl,et al.  Blooms Like It Hot , 2008, Science.

[36]  E. Vermote,et al.  Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity , 2019, Remote Sensing of Environment.

[37]  Chuanmin Hu A novel ocean color index to detect floating algae in the global oceans , 2009 .

[38]  Bryan A. Franz,et al.  Sentinel-2 MultiSpectral Instrument (MSI) data processing for aquatic science applications: Demonstrations and validations , 2017 .

[39]  Ronghua Ma,et al.  Two-decade reconstruction of algal blooms in China's Lake Taihu. , 2009, Environmental science & technology.

[40]  Giuseppe Zibordi,et al.  On the detectability of adjacency effects in ocean color remote sensing of mid-latitude coastal environments by SeaWiFS, MODIS-A, MERIS, OLCI, OLI and MSI , 2018, Remote sensing of environment.

[41]  David P. Roy,et al.  A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring , 2017, Remote. Sens..

[42]  C. Woodcock,et al.  Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images , 2015 .

[43]  Lian Feng,et al.  Concerns about phytoplankton bloom trends in global lakes , 2021, Nature.

[44]  Tiit Kutser,et al.  Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing , 2004 .

[45]  Quinten Vanhellemont,et al.  Challenges and opportunities for geostationary ocean colour remote sensing of regional seas: A review of recent results , 2014 .

[46]  K. Rose,et al.  Wind and trophic status explain within and among‐lake variability of algal biomass , 2018, Limnology and Oceanography Letters.

[47]  P. Jacinthe,et al.  Climatic versus Anthropogenic Controls of Decadal Trends (1983-2017) in Algal Blooms in Lakes and Reservoirs across China. , 2021, Environmental science & technology.

[48]  J. Melack,et al.  A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes , 2020, Remote Sensing of Environment.

[49]  Nima Pahlevan,et al.  Sentinel-2/Landsat-8 product consistency and implications for monitoring aquatic systems , 2019, Remote Sensing of Environment.

[50]  Lian Feng,et al.  Cloud adjacency effects on top-of-atmosphere radiance and ocean color data products: A statistical assessment , 2016 .

[51]  Qinglong Wu,et al.  Environmental issues of Lake Taihu, China , 2007, Hydrobiologia.