Consistent Multi-Mission Measures of Inland Water Algal Bloom Spatial Extent Using MERIS, MODIS and OLCI
Abstract
:1. Introduction
2. Methods
2.1. Satellite Imagery and Processing Steps
2.2. Training and Validation Datasets
2.3. Neural Network Parameterization and Workflow
3. Results & Discussion
3.1. NN14B Performance
3.2. Pixel Saturation and NN9B Performance
3.3. Alternate Chlorophyll-Retrieval Algorithm Solutions
3.4. Performance of Seasonal Bloom Extent Products
3.5. NNMCI Regional Transferability
3.6. Consistent Long-Term Time-Series of Bloom Spatial Extent
4. Summary and Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Mouw, C.B.; Greb, S.; Aurin, D.; DiGiacomo, P.M.; Lee, Z.; Twardowski, M.; Binding, C.; Hu, C.; Ma, R.; Moore, T.; et al. Aquatic color radiometry remote sensing of coastal and inland waters: Challenges and recommendations for future satellite missions. Remote Sens. Environ. 2015, 160, 15–30. [Google Scholar] [CrossRef]
- Binding, C.E.; Pizzolato, L.; Zeng, C. EOLakeWatch; delivering a comprehensive suite of remote sensing algal bloom indices for enhanced monitoring of Canadian eutrophic lakes. Ecol. Indic. 2021, 121, 106999. [Google Scholar] [CrossRef]
- Mishra, D.R.; Kumar, A.; Ramaswamy, L.; Boddula, V.K.; Das, M.C.; Page, B.P.; Weber, S.J. CyanoTRACKER: A cloud-based integrated multi-platform architecture for global observation of cyanobacterial harmful algal blooms. Harmful Algae 2020, 96, 101828. [Google Scholar] [CrossRef] [PubMed]
- Schaeffer, B.A.; Bailey, S.W.; Conmy, R.N.; Galvin, M.; Ignatius, A.R.; Johnston, J.M.; Keith, D.J.; Lunetta, R.S.; Parmar, R.; Stumpf, R.P.; et al. Mobile device application for monitoring cyanobacteria harmful algal blooms using Sentinel-3 satellite Ocean and Land Colour Instruments. Environ. Model. Softw. 2018, 109, 93–103. [Google Scholar] [CrossRef]
- Hallegraeff, G.M. A review of harmful algal blooms and their apparent global increase. Phycologia 1993, 32, 79–99. [Google Scholar] [CrossRef] [Green Version]
- Heisler, J.; Glibert, P.; Burkholder, J.; Anderson, D.; Cochlan, W.; Dennison, W.; Gobler, C.; Dortch, Q.; Heil, C.; Humphries, E.; et al. Eutrophication and Harmful Algal Blooms: A Scientific Consensus. Harmful Algae 2008, 8, 3–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Paerl, H.W.; Huisman, J. Blooms Like It Hot. Science 2008, 320, 57. [Google Scholar] [CrossRef] [Green Version]
- Glibert, P.; Seitzinger, S.; Heil, C.; Burkholder, J.; Parrow, M.; Codispoti, L.; Kelly, C. The role of in the global proliferation of harmful algal blooms: New perspectives and approaches. Oceanography 2005, 18, 196–207. [Google Scholar] [CrossRef] [Green Version]
- Stumpf, R.P.; Wynne, T.T.; Baker, D.B.; Fahnenstiel, G.L. Interannual variability of cyanobacterial blooms in Lake Erie. PLoS ONE 2012, 7, e42444. [Google Scholar] [CrossRef]
- Jetoo, S.; Grover, V.; Krantzberg, G. The Toledo Drinking Water Advisory: Suggested Application of the Water Safety Planning Approach. Sustainability 2015, 7, 9787–9808. [Google Scholar] [CrossRef] [Green Version]
- Smith, R.B.; Bass, B.; Sawyer, D.; Depew, D.; Watson, S.B. Estimating the economic costs of algal blooms in the Canadian Lake Erie Basin. Harmful Algae 2019, 87, 101624. [Google Scholar] [CrossRef]
- Watson, S.B.; Miller, C.; Arhonditsis, G.; Boyer, G.L.; Carmichael, W.; Charlton, M.N.; Confesor, R.; Depew, D.C.; Höök, T.O.; Ludsin, S.A.; et al. The re-eutrophication of Lake Erie: Harmful algal blooms and hypoxia. Harmful Algae 2016, 56, 44–66. [Google Scholar] [CrossRef]
- Stumpf, R.P.; Johnson, L.T.; Wynne, T.T.; Baker, D.B. Forecasting annual cyanobacterial bloom biomass to inform management decisions in Lake Erie. J. Great Lakes Res. 2016, 42, 1174–1183. [Google Scholar] [CrossRef]
- Sayers, M.J.; Grimm, A.G.; Shuchman, R.A.; Bosse, K.R.; Fahnenstiel, G.L.; Ruberg, S.A.; Leshkevich, G.A. Satellite monitoring of harmful algal blooms in the Western Basin of Lake Erie: A 20-year time-series. J. Great Lakes Res. 2019, 45, 508–521. [Google Scholar] [CrossRef]
- EOLakeWatch. EOLakeWatch: Satellite Earth Observations for Lake Monitoring. Available online: https://www.canada.ca/en/environment-climate-change/services/water-overview/satellite-earth-observations-lake-monitoring.html (accessed on 3 January 2021).
- Loucks, D.P.; van Beek, E. Water Resources Planning and Management: An Overview. In Water Resource Systems Planning and Management: An Introduction to Methods, Models, and Applications; Springer International Publishing: Cham, Switzerland, 2017; pp. 1–49. [Google Scholar]
- Groom, S.; Sathyendranath, S.; Ban, Y.; Bernard, S.; Brewin, R.; Brotas, V.; Brockmann, C.; Chauhan, P.; Choi, J.-K.; Chuprin, A.; et al. Satellite Ocean Colour: Current Status and Future Perspective. Front. Mar. Sci. 2019, 6, 485. [Google Scholar] [CrossRef] [Green Version]
- Mélin, F.; Vantrepotte, V.; Chuprin, A.; Grant, M.; Jackson, T.; Sathyendranath, S. Assessing the fitness-for-purpose of satellite multi-mission ocean color climate data records: A protocol applied to OC-CCI chlorophyll-a data. Remote Sens. Environ. 2017, 203, 139–151. [Google Scholar] [CrossRef]
- Binding, C.E.; Greenberg, T.A.; McCullough, G.; Watson, S.B.; Page, E. An analysis of satellite-derived chlorophyll and algal bloom indices on Lake Winnipeg. J. Great Lakes Res. 2018, 44, 436–446. [Google Scholar] [CrossRef]
- Matthews, M.W.; Bernard, S.; Robertson, L. An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters. Remote Sens. Environ. 2012, 124, 637–652. [Google Scholar] [CrossRef]
- Simis, S.G.H.; Peters, S.W.M.; Gons, H.J. Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water. Limnol. Oceanogr. 2005, 50, 237–245. [Google Scholar] [CrossRef]
- Wynne, T.T.; Stumpf, R.P.; Tomlinson, M.C.; Warner, R.A.; Tester, P.A.; Dyble, J.; Fahnenstiel, G.L. Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes. Int. J. Remote Sens. 2008, 29, 3665–3672. [Google Scholar] [CrossRef]
- Carder, K.L.; Chen, F.R.; Lee, Z.P.; Hawes, S.K.; Kamykowski, D. Semianalytic Moderate-Resolution Imaging Spectrometer algorithms for chlorophyll α and absorption with bio-optical domains based on nitrate-depletion temperatures. J. Geophys. Res. Ocean. 1999, 104, 5403–5421. [Google Scholar] [CrossRef]
- O’Reilly, J.E.; Maritorena, S.; Mitchell, B.G.; Siegel, D.A.; Carder, K.L.; Garver, S.A.; Kahru, M.; McClain, C. Ocean color chlorophyll algorithms for SeaWiFS. J. Geophys. Res. Ocean. 1998, 103, 24937–24953. [Google Scholar] [CrossRef] [Green Version]
- Antoine, D.; Sentinel-3 Optical Products and Algorithm Definition. OLCI Level 2 Algorithm Theoretical Basis Document: Ocean Color Products in Case 1 Waters. Available online: https://sentinel.esa.int/documents/247904/349589/OLCI_L2_ATBD_Ocean_Colour_Products_Case-1_Waters.pdf (accessed on 3 January 2021).
- Gilerson, A.A.; Gitelson, A.A.; Zhou, J.; Gurlin, D.; Moses, W.; Ioannou, I.; Ahmed, S.A. Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands. Opt. Express 2010, 18, 24109–24125. [Google Scholar] [CrossRef] [Green Version]
- Gower, J.; King, S.; Borstad, G.; Brown, L. Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer. Int. J. Remote Sens. 2005, 26, 2005–2012. [Google Scholar] [CrossRef]
- Lunetta, R.S.; Schaeffer, B.A.; Stumpf, R.P.; Keith, D.; Jacobs, S.A.; Murphy, M.S. Evaluation of cyanobacteria cell count detection derived from MERIS imagery across the eastern USA. Remote Sens. Environ. 2015, 157, 24–34. [Google Scholar] [CrossRef]
- Shuchman, R.A.; Leshkevich, G.; Sayers, M.J.; Johengen, T.H.; Brooks, C.N.; Pozdnyakov, D. An algorithm to retrieve chlorophyll, dissolved organic carbon, and suspended minerals from Great Lakes satellite data. J. Great Lakes Res. 2013, 39, 14–33. [Google Scholar] [CrossRef]
- Pahlevan, N.; Smith, B.; Schalles, J.; Binding, C.; Cao, Z.; Ma, R.; Alikas, K.; Kangro, K.; Gurlin, D.; Hà, N.; et al. Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. Remote Sens. Environ. 2020, 240, 111604. [Google Scholar] [CrossRef]
- Donlon, C.; Berruti, B.; Buongiorno, A.; Ferreira, M.H.; Féménias, P.; Frerick, J.; Goryl, P.; Klein, U.; Laur, H.; Mavrocordatos, C.; et al. The Global Monitoring for Environment and Security (GMES) Sentinel-3 mission. Remote Sens. Environ. 2012, 120, 37–57. [Google Scholar] [CrossRef]
- Land, P.E.; Shutler, J.D.; Smyth, T.J. Correction of Sensor Saturation Effects in MODIS Oceanic Particulate Inorganic Carbon. IEEE Trans Geosci. Remote Sens. 2018, 56, 1466–1474. [Google Scholar] [CrossRef]
- Wynne, T.T.; Meredith, A.; Briggs, T.; Litaker, W.; Stumpf, R.P. Harmful Algal Bloom Forecasting Branch Ocean Color Satellite Imagery Processing Guidelines; NOAA Technical Memorandum NOS NCCOS: Silver Spring, MD, USA, 2018. [Google Scholar] [CrossRef]
- Binding, C.E.; Stumpf, R.P.; Shuchman, R.A.; Sayers, M.J. Advances in Remote Sensing of Great Lakes Algal Blooms. In Contaminants of the Great Lakes. The Handbook of Environmental Chemistry; Crossman, J., Weisener, C., Eds.; Springer: Berlin/Heidelberg, Germany, 2021. [Google Scholar]
- Wynne, T.T.; Stumpf, R.P.; Briggs, T.O. Comparing MODIS and MERIS spectral shapes for cyanobacterial bloom detection. Int. J. Remote Sens. 2013, 34, 6668–6678. [Google Scholar] [CrossRef]
- IOCCG. Ocean-Colour Data Merging; Reports of the International Ocean-Colour Coordinating Group, IOCCG: Dartmouth, NS, Canada, 2007. [Google Scholar]
- Kwiatkowska, E.; Fargion, G. Application of machine-learning techniques toward the creation of a consistent and calibrated global chlorophyll concentration baseline dataset using remotely sensed ocean color data. Geosci. Remote Sens. IEEE Trans. 2004, 41, 2844–2860. [Google Scholar] [CrossRef]
- Maritorena, S.; Fanton Dandon, O.; Mangin, A.; Siegel, D. Merged satellite ocean color data products using a bio-optical model: Characteristics, benefits and issues. Remote Sens. Environ. 2010, 114, 1791–1804. [Google Scholar] [CrossRef]
- Mélin, F.; Zibordi, G.; Berthon, J.-F. Assessment of satellite ocean color products at a coastal site. Remote Sens. Environ. 2007, 110, 192–215. [Google Scholar] [CrossRef]
- Mélin, F.; Zibordi, G.; Berthon, J.-F.; Bailey, S.; Franz, B.; Voss, K.; Flora, S.; Grant, M. Assessment of MERIS reflectance data as processed with SeaDAS over the European seas. Opt. Express 2011, 19, 25657–25671. [Google Scholar] [CrossRef] [Green Version]
- Pottier, C.; Garcon, V.; Gilles, L.; Sudre, J.; Schaeffer, P.; Traon, P.-Y. Merging SeaWiFS and MODIS/Aqua Ocean Color Data in North and Equatorial Atlantic Using Weighted Averaging and Objective Analysis. Geosci. Remote Sens. IEEE Trans. 2006, 44, 3436–3451. [Google Scholar] [CrossRef] [Green Version]
- Hollmann, R.; Merchant, C.J.; Saunders, R.; Downy, C.; Buchwitz, M.; Cazenave, A.; Chuvieco, E.; Defourny, P.; de Leeuw, G.; Forsberg, R.; et al. The ESA Climate Change Initiative: Satellite Data Records for Essential Climate Variables. Bull. Am. Meteorol. Soc. 2013, 94, 1541–1552. [Google Scholar] [CrossRef] [Green Version]
- Mélin, F.; Sclep, G. Band shifting for ocean color multi-spectral reflectance data. Opt. Express 2015, 23, 2262–2279. [Google Scholar] [CrossRef] [PubMed]
- Simis, S.; Selmes, N.; Calmettes, B.; Duguay, C.; Merchant, C.; Norce, E.M.; Yésou, H.; Blanco, P. ESA Lakes Climate Change Initiative (Lakes_cci): Lake Products; Version 1.0; University of Waterloo: Waterloo, ON, Canada, 2020. [Google Scholar] [CrossRef]
- Mobley, C.D.; Sundman, L.K.; Davis, C.O.; Bowles, J.H.; Downes, T.V.; Leathers, R.A.; Montes, M.J.; Bissett, W.P.; Kohler, D.D.R.; Reid, R.P.; et al. Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables. Appl. Opt. 2005, 44, 3576–3592. [Google Scholar] [CrossRef] [PubMed]
- Lee, Z.P.; Carder, K.L.; Arnone, R.A. Deriving inherent optical properties from water color: A multiband quasi-analytical algorithm for optically deep waters. Appl. Opt. 2002, 41, 5755–5772. [Google Scholar] [CrossRef] [PubMed]
- Pahlevan, N.; Smith, B.; Binding, C.; O’Donnell, D.M. Spectral band adjustments for remote sensing reflectance spectra in coastal/inland waters. Opt. Express 2017, 25, 28650–28667. [Google Scholar] [CrossRef]
- NASA. NASA Oceancolor Web. Available online: https://oceancolor.gsfc.nasa.gov/cgi/browse.pl (accessed on 3 January 2021).
- MERIS. ESA Earth Observation. Available online: https://coda.eumetsat.int/#/home (accessed on 1 April 2019).
- Pahlevan, N.; Mangin, A.; Balasubramanian, S.V.; Smith, B.; Alikas, K.; Arai, K.; Barbosa, C.; Bélanger, S.; Binding, C.; Bresciani, M.; et al. ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters. Remote Sens. Environ. 2021, 258, 112366. [Google Scholar] [CrossRef]
- Binding, C.E.; Greenberg, T.A.; Jerome, J.H.; Bukata, R.P.; Letourneau, G. An assessment of MERIS algal products during an intense bloom in Lake of the Woods. J. Plankton Res. 2011, 33, 793–806. [Google Scholar] [CrossRef] [Green Version]
- Zeng, C.; Binding, C. The effect of mineral sediments on satellite chlorophyll-a retrievals from line-height algorithms using red and near-infrared bands. Remote Sens. 2019, 11, 2306. [Google Scholar] [CrossRef] [Green Version]
- Falconer, I.; Bartram, J.; Chorus, I.; Kuiper-Goodman, T.; Utkilen, H.; Burch, M.; Codd, G.A. Toxic Cyanobacteria in Water: A Guide to their Public Health Consequences, Monitoring and Management. In Safe Levels and Safe Practices; E&FN Spon: London, UK; New York, NY, USA, 1999. [Google Scholar]
- O’Reilly, J.E.; Werdell, P.J. Chlorophyll algorithms for ocean color sensors—OC4, OC5 & OC6. Remote Sens. Environ. 2019, 229, 32–47. [Google Scholar] [CrossRef] [PubMed]
- Binding, C.E.; Zastepa, A.; Zeng, C. The impact of phytoplankton community composition on optical properties and satellite observations of the 2017 western Lake Erie algal bloom. J. Great Lakes Res. 2019, 45, 573–586. [Google Scholar] [CrossRef]
- Philpot, W.D. The derivative ratio algorithm: Avoiding atmospheric effects in remote sensing. IEEE Trans Geosci. Remote Sens. 1991, 29, 350–357. [Google Scholar] [CrossRef]
- Mimuro, M.; Fujita, Y. Estimation of chlorophyll a distribution in the photosynthetic pigment systems I and II of the blue-green alga Anabaena variabilis. Biochim. Biophys. Acta Bioenerg. 1977, 459, 376–389. [Google Scholar] [CrossRef]
- Gons, H.J.; Auer, M.T.; Effler, S.W. MERIS satellite chlorophyll mapping of oligotrophic and eutrophic waters in the Laurentian Great Lakes. Remote Sens. Environ. 2008, 112, 4098–4106. [Google Scholar] [CrossRef]
- Schindler, D.W.; Hecky, R.E.; McCullough, G.K. The rapid eutrophication of Lake Winnipeg: Greening under global change. J. Great Lakes Res. 2012, 38, 6–13. [Google Scholar] [CrossRef]
- Environment and Climate Change Canada; Manitoba Agriculture and Resource Development. Chapter 8.0—Phytoplankton. In State of Lake Winnipeg Report, 2nd ed.; Government of Manitoba: Winnipeg, MB, Canada, 2020; p. 175. [Google Scholar]
- Ho, J.C.; Michalak, A.M. Phytoplankton blooms in Lake Erie impacted by both long-term and springtime phosphorus loading. J. Great Lakes Res. 2017, 43, 221–228. [Google Scholar] [CrossRef]
- Paterson, A.M.; Rühland, K.M.; Anstey, C.V.; Smol, J.P. Climate as a driver of increasing algal production in Lake of the Woods, Ontario, Canada. Lake Reserv. Manag. 2017, 33, 403–414. [Google Scholar] [CrossRef]
- Reavie, E.D.; Edlund, M.B.; Andresen, N.A.; Engstrom, D.R.; Leavitt, P.R.; Schottler, S.; Cai, M. Paleolimnology of the Lake of the Woods southern basin: Continued water quality degradation despite lower nutrient influx. Lake Reserv. Manag. 2017, 33, 369–385. [Google Scholar] [CrossRef]
- Hammond, M.L.; Beaulieu, C.; Henson, S.A.; Sahu, S.K. Assessing the Presence of Discontinuities in the Ocean Color Satellite Record and Their Effects on Chlorophyll Trends and Their Uncertainties. Geophys. Res. Lett. 2018, 45, 7654–7662. [Google Scholar] [CrossRef] [Green Version]
Parameter | Value/Description |
---|---|
Input/hidden/output nodes | 14(9)/10/3 |
Pre/post-processing | Normalization |
Activation Functions | Tanh |
Optimization approach | Levenberg–Marquardt |
Performance metric | Mean Square Error |
Computing environment | Micro-workstation (CPU: 56core @2GHz; Memory: 32 G + 16 G swap; storage: 80Tb) running Linux 4.15.0-64 Ubuntu 16.04 |
MODIS Predicted | Bloom (NN14B) | Non-Bloom (NN14B) | Bloom (NN9B) | Non-Bloom (NN9B) | |
---|---|---|---|---|---|
MERIS/OLCI | |||||
Bloom | 18.4% | 2.8% | 20.9% | 2.9% | |
Non-bloom | 3.8% | 75.0% | 5.0% | 71.2% |
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Zeng, C.; Binding, C.E. Consistent Multi-Mission Measures of Inland Water Algal Bloom Spatial Extent Using MERIS, MODIS and OLCI. Remote Sens. 2021, 13, 3349. https://doi.org/10.3390/rs13173349
Zeng C, Binding CE. Consistent Multi-Mission Measures of Inland Water Algal Bloom Spatial Extent Using MERIS, MODIS and OLCI. Remote Sensing. 2021; 13(17):3349. https://doi.org/10.3390/rs13173349
Chicago/Turabian StyleZeng, Chuiqing, and Caren E. Binding. 2021. "Consistent Multi-Mission Measures of Inland Water Algal Bloom Spatial Extent Using MERIS, MODIS and OLCI" Remote Sensing 13, no. 17: 3349. https://doi.org/10.3390/rs13173349