1 Satellite Remote Sensing for Monitoring and Assessment Group on Earth Observations June 12, 2014 Blake Schaeffer, Ph.D. Environmental Protection Agency Office of Research and Development National Exposure Research Laboratory

22 Outline Barriers Numeric criteria Cyanobacteria Assessment Network (CyAN) Future directions

33 Barriers Cost –Satellite imagery only obtained with financial commitment –Personnel and resources required to process the data Product Accuracy –Concern of accuracy and error estimates –Less concern regarding the error with standard in situ measurements Data Continuity –Mission continuity was relevant to investment Programmatic support –Buy-in and support from organizational management was important. Source: Schaeffer et al. IJRS :

44 Numeric criteria EPA Administrator determined that “new or revised numeric standards are necessary” to address nutrients Related consent decree calls for two-phase rulemaking –Inland waters rule –Estuarine/Coastal and South Florida Rule Final rulemaking - November 2012

55 Numeric criteria Best available data –Compiled data for Florida waters Scientific rationale –Use the latest scientific knowledge General water body classifications –Estuaries –Coastal Waters –Southern Inland Flowing Waters and Marine Waters

66 Reference condition –Water body currently minimally impacted by nutrients and supporting natural populations Coastal chlorophyll –No trend in chlorophyll from –Slope not significantly different from zero (p>0.05) 6 FP (p=0.42) WFS (p=0.34) AC (p=0.76) Numeric criteria

7 FDEP’s Water Body Identification Numbers (WBIDs) –Start at land –Extended seaward 4 NM –Typically centered at estuary pass –Coastal segments Source: Schaeffer et al. ES&T :

8 Numeric criteria Annual geometric means of Chl RS - a in each segment were calculated using only SeaWiFS Criteria values were calculated from the 90 th percentile, by segment, of all annual geometric means during the reference period Source: Schaeffer et al. ES&T :

99 Numeric criteria Mission continuity –“…the future availability of ChlRS-a data is in doubt, particularly given the loss of the SeaWiFS sensor.” –“Of course there is always the issue as to whether or not satellites go off- line in the future….If satellites would go off-line…and there is no funding to put satellites in orbit to do this work than that would definitely cause some concern.” Source: EPA-SAB Source: Schaeffer et al. IJRS :

10 Numeric criteria SeaWiFS, MODIS, MERIS, VIIRS?, Sentinel-3?, PACE?, GEOCAPE? Field data used within ±3 hr satellite overpass time Source: Schaeffer et al. JARS :

11 Numeric criteria “…recurrent topic in panel discussions was the ‘missing kilometer’ at the coast...” NASA PACE SDT Report (2012) Source: EPA SAB Draft Report Source: Schaeffer et al. ES&T :

12 Numeric criteria Source: Schaeffer et al. JARS :

13 Numeric criteria Coastal Florida Rule Proposed rulemaking: Nov. 30, 2012 Final rulemaking: Sept Source: Schaeffer et al. JARS :

14 CyAN Introduction HABs = $2.2 billion annual freshwater degradation (Dodds et al. 2009). HAB and Hypoxia Research and Control Amendments Act of 2013 Cyanobacteria and their toxins are addressed differently by each state (Graham et al. 2009) Timely and useful delivery of satellite data is needed (Schaeffer et al. 2013). Toxic algae in Hoover Reservoir cost city Choking on growth, part III Polluted farm runoff linked to toxic green algae

15 CyAN Partners and Stakeholders Previous support –EPA - Office of Research and Development, Pathfinder Innovation Project II –EPA - NASA Applied Science Program announcement NNH08ZDA001N –NOAA - NASA Applied Science Program announcement NNH08ZDA001N EPA Office of Water –Office of Wetlands, Oceans, and Watersheds –Office of Wastewater Management –Office of Science and Technology States –Ohio EPA –St. Johns River WMD GLNPO

16 CyAN Technical Approach

17 CyAN Methods 378,153 lakes in U.S. ~153,000 lakes considered in 2007 National Lake Assessment MERIS and Sentinel-3 –9,347 lakes w/ 3x3 300 m pixel array –6 % of National Lakes Assessment Landsat –~153,000 lakes w/ 5x5 30 m pixel array –100% of National Lake Assessment w/ 5x5 array

18 CyAN Methods Spatial scale –300 x 300 m Temporal scales –≤ 1-day –± 1, 3, 5 and 7 days

19 CyAN Methods

20 CyAN Results Lunetta et al. (In Review) RSE

21 CyAN Results Cyanobacteria cell count ranges –Low (10,000–109,999) WHO Low-Moderate –Medium (110,000–299,999) –High (300,000–1,000,000) –Very high (>1,000,000) Lunetta et al. (In Review) RSE

22 CyAN Results Correspondence % (+/- 7 days) –Low = 90% –Medium = 17% –High = 35% –Very High = 83% Lunetta et al. (In Review) RSE

23 CyAN Results Source: NOAA Coastal Ocean Science Grand Lake St. Mary, Ohio: MERIS Cyano index 2010 Low [conc] Very High [conc]

24 Crowd Source CyAN Mobile Application Android OS Google Play Schaeffer et al. (In Prep) PNAS

25 Crowd Source CyAN Mobile Application Schaeffer et al. (In Prep) PNAS

26 Crowd Source CyAN Mobile Application Schaeffer et al. (In Prep) PNAS

27 Crowd Source CyAN Mobile Application Schaeffer et al. (In Prep) PNAS

28 Crowd Source Prediction Algorithm Now through June 28 th ! 2006 National Land Cover Database Water Quality Portal National Climatic Data Center MERIS/Sentinel-3 cyanobacteria counts

29 Crowd Source Prediction Algorithm

30 Crowd Source Prediction Algorithm

31 Future Approach Remote Sensing –Uniform and systematic approach for identifying cyanobacteria blooms. –Strategy for evaluation and refinement of algorithms across platforms. Environment –Identify landscape linkages causes of chlorophyll-a and cyanobacteria. Health –Exposure and human health effects in drinking and recreational waters. Economics –Behavioral responses and economic value of the early warning system. Notifications –Bring the technology to EPA, states and tribal partners.

32 Impacts Inform and advise under the Clean Water Act and Safe Drinking Water Act. Complement National Aquatic Resource Surveys Applied novel sophisticated tool to assist in management of events that may involve significant risk to the public. Increased use of remotely sensed water quality data to improve decision support in EPA and state agencies Decrease costs of monitoring, improve resource allocations, and reduce exposures