1. Pick, F. R. Blooming algae: a Canadian perspective on the rise of toxic cyanobacteria. Can. J. Fish. Aquat. Sci. 73, 1149–1158 (2016).

2. Ndlela, L. L., Oberholster, P. J., Van Wyk, J. H. & Cheng, P. H. An overview of cyanobacterial bloom occurrences and research in Africa over the last decade. Harmful Algae 60, 11–26 (2016).

3. Kudela, R. M. et al. Harmful Algal Blooms. A Scientific Summary For Policy Makers (IOC/UNESCO, 2015).

4. Hampton, S. E. et al. Sixty years of environmental change in the world’s largest freshwater lake – Lake Baikal, Siberia. Glob. Change Biol. 14, 1947–1958 (2008).

5. Duan, H. et al. Two-decade reconstruction of algal blooms in China’s Lake Taihu. Environ. Sci. Technol. 43, 3522–3528 (2009).

6. Taranu, Z. E. et al. Acceleration of cyanobacterial dominance in north temperate–subarctic lakes during the Anthropocene. Ecol. Lett. 18, 375–384 (2015).

7. Carvalho, L. et al. Sustaining recreational quality of European lakes: minimizing the health risks from algal blooms through phosphorus control. J. Appl. Ecol. 50, 315–323 (2013).

8. Beaulieu, M., Pick, F. & Gregory-Eaves, I. Nutrients and water temperature are significant predictors of cyanobacterial biomass in a 1147 lakes data set. Limnol. Oceanogr. 58, 1736–1746 (2013).

9. Kosten, S. et al. Warmer climates boost cyanobacterial dominance in shallow lakes. Glob. Change Biol. 18, 118–126 (2012).

10. Posch, T., Köster, O., Salcher, M. M. & Pernthaler, J. Harmful filamentous cyanobacteria favoured by reduced water turnover with lake warming. Nat. Clim. Change 2, 809–813 (2012).

11. Paerl, H. W. et al. Mitigating cyanobacterial harmful algal blooms in aquatic ecosystems impacted by climate change and anthropogenic nutrients. Harmful Algae 54, 213–222 (2016).

12. Winder, M. Lake warming mimics fertilization. Nat. Clim. Change 2, 771–772 (2012).

13. Paerl, H. W. & Huisman, J. Blooms like it hot. Science 320, 57–58 (2008).

14. Carmichael, W. in Cyanobacterial Harmful Algal Blooms: State of the Science and Research Needs (ed. Hudnell, H. K.) 105–125 (Springer-Verlag, 2008).

15. Schindler, D. W., Carpenter, S. R., Chapra, S. C., Hecky, R. E. & Orihel, D. M. Reducing phosphorus to curb lake eutrophication is a success. Environ. Sci. Technol. 50, 8923–8929 (2016).

16. Winter, J. G., Young, J. D., Landre, A., Stainsby, E. & Jarjanazi, H. Changes in phytoplankton community composition of Lake Simcoe from 1980 to 2007 and relationships with multiple stressors. J. Great Lakes Res. 37, 63–71 (2011).

17. Huisman, J. et al. Cyanobacterial blooms. Nat. Rev. Microbiol. 16, 471–483 (2018).

18. McCrackin, M. L., Jones, H. P., Jones, P. C. & Moreno-Mateos, D. Recovery of lakes and coastal marine ecosystems from eutrophication: a global meta-analysis. Limnol. Oceanogr. 62, 507–518 (2017).

19. Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

20. Zhu, Z. et al. Benefits of the free and open Landsat data policy. Remote Sens. Environ. 224, 382–385 (2019).

21. Ho, J. C., Stumpf, R. P., Bridgeman, T. B. & Michalak, A. M. Using Landsat to extend the historical record of lacustrine phytoplankton blooms: a Lake Erie case study. Remote Sens. Environ. 191, 273–285 (2017).

22. Schneider, P. & Hook, S. J. Space observations of inland water bodies show rapid surface warming since 1985. Geophys. Res. Lett. 37, L22405 (2010).

23. Sharma, S. et al. A global database of lake surface temperatures collected by in situ and satellite methods from 1985–2009. Sci. Data 2, 150008 (2015).

24. Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 13603 (2016).

25. Hampton, S. E. Understanding lakes near and far. Science 342, 815–816 (2013).

26. Spyrakos, E. et al. Optical types of inland and coastal waters. Limnol. Oceanogr. 63, 846–870 (2018).

27. O’Reilly, C. M. et al. Rapid and highly variable warming of lake surface waters around the globe. Geophys. Res. Lett. 42, 10,773–10,781 (2015).

28. Downing, J. in Global Environmental Change (ed. Freedman, B.) 221–229 (Springer, 2014).

29. Kraemer, B. M. et al. Morphometry and average temperature affect lake stratification responses to climate change. Geophys. Res. Lett. 42, 4,981–4,988 (2015).

30. Fristachi, A. et al. in Cyanobacterial Harmful Algal Blooms: State of the Science and Research Needs (ed. Hudnell, H. K.) 45–103 (Springer-Verlag, 2008).

31. Adrian, R. et al. Lakes as sentinels of climate change. Limnol. Oceanogr. 54, 2,283–2,297 (2009).

32. Rigosi, A., Carey, C. C., Ibelings, B. W. & Brookes, J. D. The interaction between climate warming and eutrophication to promote cyanobacteria is dependent on trophic state and varies among taxa. Limnol. Oceanogr. 59, 99–114 (2014).

33. Wessel, P., Smith, W. H. F., Scharroo, R., Luis, J. & Wobbe, F. Generic Mapping Tools: improved version released. Eos 94, 409–410 (2013).

34. Tebbs, E. J., Remedios, J. J. & Harper, D. M. Remote sensing of chlorophyll-a as a measure of cyanobacterial biomass in Lake Bogoria, a hypertrophic, saline–alkaline, flamingo lake, using Landsat ETM+. Remote Sens. Environ. 135, 92–106 (2013).

37. Chander, G., Markham, B. L. & Helder, D. L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 113, 893–903 (2009).

38. USGS. Landsat Surface Reflectance Level-2 Data Products. https://landsat.usgs.gov/landsat-surface-reflectance-data-products (2017).

39. Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22 (2004).

40. Zhu, Z. & Woodcock, C. E. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 118, 83–94 (2012).

41. Zhu, Z., Wang, S. & Woodcock, C. E. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images. Remote Sens. Environ. 159, 269–277 (2015).

42. Erickson, T. A. Earth Engine Data Catalog: USGS Landsat 5 TOA Reflectance (Orthorectified) with Fmask. https://code.earthengine.google.com/dataset/LANDSAT/LT5_L1T_TOA_FMASK (2016).

43. Irish, R. R. Landsat 7 automatic cloud cover assessment. In Proc. SPIE 4049, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI 348–355 (2000).

44. Moore, T. S. et al. Bio-optical properties of cyanobacteria blooms in western Lake Erie. Front. Mar. Sci. 4, 300 (2017).

45. Gower, J., King, S., Borstad, G. & Brown, L. Use of the 709 nm band of MERIS to detect intense plankton blooms and other conditions in coastal waters. ESA J. 1161, 365–368 (2005).

46. Goward, S. et al. Historical record of Landsat global coverage: mission operations, NSLRSDA, and international cooperator stations. Photogramm. Eng. Remote Sensing 72, 1155–1169 (2006).

47. Palmer, S. C. J. et al. Validation of Envisat MERIS algorithms for chlorophyll retrieval in a large, turbid and optically-complex shallow lake. Remote Sens. Environ. 157, 158–169 (2015).

48. Pálffy, K., Présing, M. & Vörös, L. Diversity patterns of trait-based phytoplankton functional groups in two basins of a large, shallow lake (Lake Balaton, Hungary) with different trophic state. Aquat. Ecol. 47, 195–210 (2013).

49. Hajnal, É. & Padisák, J. Analysis of long-term ecological status of Lake Balaton based on the ALMOBAL phytoplankton database. Hydrobiologia 599, 227–237 (2008).

50. Chesoh, S., Lim, A. & Tongkumchum, P. Trend of water quality and model for forecasting eutrophication occurrence in Songkhla Lake, Thailand. in Proc. Taal2007: The 12th World Lake Conference 834–839 (2008).

51. Suwanidcharoen, S. & Liengcharernsit, W. Development of phytoplankton model with application to Songkhla Lake, Thailand. Lowl. Technol. Int. 14, 50–59 (2012).

52. Stumpf, R. P., Wynne, T. T., Baker, D. B. & Fahnenstiel, G. L. Interannual variability of cyanobacterial blooms in Lake Erie. PLoS ONE 7, e42444 (2012).

53. Pahlevan, N., Balasubramanian, S. V., Sarkar, S. & Franz, B. A. Toward long-term aquatic science products from heritage Landsat missions. Remote Sens. 10, 1337 (2018).

54. Palmer, S. C. J. et al. Satellite remote sensing of phytoplankton phenology in Lake Balaton using 10 years of MERIS observations. Remote Sens. Environ. 158, 441–452 (2015).

55. Mann, H. B. Nonparametric tests against trend. Econometrica 13, 245–259 (1945).

56. Sen, P. K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968).

57. Rousseeuw, P. J. & Leroy, A. M. Robust Regression and Outlier Detection (John Wiley & Sons, 1987).

58. Izmest’eva, L. R. et al. Lake-wide physical and biological trends associated with warming in Lake Baikal. J. Great Lakes Res. 42, 6–17 (2016).

59. Gobler, C. J. et al. Ocean warming since 1982 has expanded the niche of toxic algal blooms in the North Atlantic and North Pacific oceans. Proc. Natl Acad. Sci. USA 114, 4975–4980 (2017).

60. Padisák, J. & Koncsos, L. Trend and noise: long-term changes of phytoplankton in the Keszthely Basin of Lake Balaton, Hungary. Int. Assoc. Theor. Appl. Limnol. 28, 194–203 (2002).

61. Tátrai, I., Istvánovics, V., Tóth, L.-G. & Kóbor, I. Management measures and long-term water quality changes in Lake Balaton (Hungary). Fundam. Appl. Limnol. 172, 1–11 (2008).

62. Mioni, C., Kudela, R., Baxa, D. & Sullivan, M. Harmful Cyanobacteria Blooms and their Toxins in Clear Lake and the Sacramento-San Joaquin Delta (California) (Central Valley Regional Water Quality Control Board, 2011).

63. Winder, M., Reuter, J. & Schladow, G. Clear Lake Historical Data Analysis (Univ. California, Davis, 2010).

64. North, R. L. et al. The state of Lake Simcoe (Ontario, Canada): the effects of multiple stressors on phosphorus and oxygen dynamics. Inland Waters 3, 51–74 (2013).

65. Evans, D. O., Skinner, A. J., Allen, R. & McMurtry, M. J. Invasion of zebra mussel, Dreissena polymorpha, in Lake Simcoe. J. Great Lakes Res. 37, 36–45 (2011).

66. Baranowska, K. A., North, R. L., Winter, J. G. & Dillon, P. J. Long-term seasonal effects of dreissenid mussels on phytoplankton in Lake Simcoe, Ontario, Canada. Inland Waters 3, 285–296 (2013).

67. Schindler, D. W., Hecky, R. E. & McCullough, G. K. The rapid eutrophication of Lake Winnipeg: greening under global change. J. Great Lakes Res. 38, 6–13 (2012).

68. Allinger, L. & Reavie, E. The ecological history of Lake Erie as recorded by the phytoplankton community. J. Great Lakes Res. 39, 365–382 (2013).

69. Nilson, E. Investigating Potential Agricultural-related Causes of Eutrophication in the Tsimlyansk Reservoir through GIS and Remote Sensing. MSc thesis, Central European Univ. (2014).

70. Pozzer, A. et al. AOD trends during 2001–2010 from observations and model simulations. Atmos. Chem. Phys. 15, 5521–5535 (2015).

71. Guan, X., Li, J. & Booty, W. G. Monitoring Lake Simcoe water clarity using Landsat-5 TM images. Water Resour. Manage. 25, 2015–2033 (2011).

72. Belovsky, G. E. et al. The Great Salt Lake Ecosystem (Utah, USA): long term data and a structural equation approach. Ecosphere 2, art33 (2011).

73. Havens, K. et al. Extreme weather events and climate variability provide a lens to how shallow lakes may respond toclimate change. Water 8, 229 (2016).

74. Chin, M. et al. Multi-decadal aerosol variations from 1980 to 2009: a perspective from observations and a global model. Atmos. Chem. Phys. 14, 3657–3690 (2014).

75. Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C. & Zhao, M. Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS deep blue aerosol products. Rev. Geophys. 50, RG3005 (2012).

76. Wang, J., Dai, A. & Mears, C. Global water vapor trend from 1988 to 2011 and its diurnal asymmetry based on GPS, radiosonde, and microwave satellite measurements. J. Clim. 29, 5205–5222 (2016).

77. Tiffany, M. A., Ustin, S. L. & Hurlbert, S. H. Sulfide irruptions and gypsum blooms in the Salton Sea as detected by satellite imagery, 1979–2006. Lake Reserv. Manage. 23, 637–652 (2007).

78. Chang, N.-B., Bai, K. & Chen, C.-F. Smart information reconstruction via time-space-spectrum continuum for cloud removal in satellite images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8, 1898–1912 (2015).

79. Wei, Y. et al. NACP MsTMIP: Global and North American Driver Data for Multi-Model Intercomparison. https://doi.org/10.3334/ORNLDAAC/1220 (ORNL DAAC, 2014).