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2.2 IUKWC Workshop Freshwater EO - Andrew Tyler - Jun17

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• State of Art in EO of Inland Waters: a UK perspective

Andrew Tyler (University of Stirling)

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2.2 IUKWC Workshop Freshwater EO - Andrew Tyler - Jun17

  1. 1. State of the Art in the EO of Inland Waters: A UK perspective Andrew Tyler IUKWC Wokshop June l 2017 Enhancing Freshwater Monitoring Through Earth Observation
  2. 2. Inspired by opportunity and need… Lake Balaton, Hungary Landsat 7 Need to monitor for management, protection and resilience Recognition of the spatial and temporal heterogeneity Tendency for reactive monitoring Scale of the problem Algorithm stability Challenges of optically complex waters Growing capacity and capability of satellite platforms Tyler et al., 2006, IJRS
  3. 3. Airborne Hyperspectral: PC retrieval IUKWC June 2017: Enhancing Freshwater Monitoring Through Earth Observation Hunter, et al. (2008). RSE Hunter, et al. (2008). Limnol. Ocean Hunter, et al. (2009). Envi. Sci. Tech. NERC ARSF: AISA Eagle and Hawk
  4. 4. MERIS in inland waters IUKWC June 2017: Enhancing Freshwater Monitoring Through Earth Observation Envisat: MERIS
  5. 5. Inner basin 48.4 mg Chla m-3 Middle basin 13.1 mg Chla m-3 Outer basin 1.00 Chla mg m-3 Elterwater Lat: 54.4273 / Long: -3.0230 Loughrigg Tarn STIR: 27.8 mg m-3 EA: 27.2 mg m-3 World View-2: Water optical type classification Turbid NIR-red ratio R754/R659 Clear green-blue ratio R546/R478 In-water algorithm Mapped Level-2 Chla Hunter and Tyler (2012) EA Report
  6. 6. Inner basin 48.4 mg Chla m-3 Middle basin 13.1 mg Chla m-3 Outer basin 1.00 Chla mg m-3 Loughrigg Tarn STIR: 27.8 mg m-3 EA: 27.2 mg m-3 IS R(608) > 0.04 YES: Chla ~ R(754)/R(659) NO: Chla ~ R(546)/R(478) Hunter and Tyler et al. (2012) EA Report Report With Citizen Science Based Validation
  7. 7. EO Challenges – Global Scale IUKWC June 2017: Enhancing Freshwater Monitoring Through Earth Observation EO key challenges:  Diversity in retrieval algorithms & validation approaches  Inland water remote sensing community appears fragmented Search Keys: remote sensing, water quality, lakes (2015) Filter: use of in-situ data for development/validation Number of lakes per publication:
  8. 8. Our approach Lake ecology & modelling Global lakes observatory for 1000 study lakes Environmental statistics EO lake water quality EO + modelled catchment drivers EO lake water temperature Change over time (within / between lakes) Attributing drivers of environmental change Time-series data & web visualization IUKWC June 2017: Enhancing Freshwater Monitoring Through Earth Observation
  9. 9. A Global Partnership: LIMNADES www.limnades.org o data from almost 1500 inland systems o radiometric data ~4000 stations >250 lakes o at least 40 peer-reviewed papers IUKWC June 2017: Enhancing Freshwater Monitoring Through Earth Observation
  10. 10. A Global Partnership: LIMNADES Parameter Units Range Median Lakes Chla mg m-3 0.03-13296.70 12.34 208 PC mg m-3 0 – 24677 28.79 60 TSM mg L-1 0.09-2533.30 10.54 81 ISM mg L-1 0.01-359.42 13.35 10 aCDOM(442) m-1 0.004-42.467 0.8206 83 aph(442) m-1 0.036-454.976 0.5148 19 aph(442)/[Chla] m2 mg-1 0.002-0.257 0.0156 19 aNAP(442) m-1 0.004-12.540 1.3875 23 aNAP(442)/[TSM] m2 g-1 0.001-0.410 0.0573 19 bp(532) m-1 0.524-17.788 2.2442 6 bp(532)/)/[TSM] m2 g-1 0.136-1.838 0.5872 6 bbp(532) m-1 0.004-0.292 0.0676 9 bbp(532)/)/[TSM] m2 g-1 0.0003-0.1003 0.0118 9 IUKWC June 2017: Enhancing Freshwater Monitoring Through Earth Observation
  11. 11. OWT classification • ~4,000 Rrs spectra • K-means clustering • Optimum number of clusters determined statistically Spyrakos et al., submitted …. IUKWC June 2017: Enhancing Freshwater Monitoring Through Earth Observation
  12. 12. OWT classification OWT Dominant characteristics OWT1 Hyper-eutrophic waters with cyanobacteria scum and vegetation-like reflectance OWT2 Common case waters with diverse reflectance shape and marginal dominance of pigments and CDOM over inorganic suspended particles OWT3 Clear waters OWT4 Turbid waters with high organic content OWT5 Sediment-laden waters OWT6 Balanced effects of optically active constituents at shorter wavelength OWT7 Highly productive waters with reflectance peak with elevated reflectance at red/near-infrared spectral region OWT8 Productive waters with reflectance peak close to 700 nm OWT9 Optically neighbouring to OWT2 waters but with higher reflectance at shorter wavelengths OWT10 CDOM-rich waters OWT11 High in CDOM waters with cyanobacteria presence and high absorption efficiency by NAP OWT12 Turbid, moderately productive waters with cyanobacteria presence OWT13 Very clear blue waters IUKWC June 2017: Enhancing Freshwater Monitoring Through Earth Observation Spyrakos et al., submitted ….
  13. 13. OWT classification Lake Balaton, HungaryTONLÉ SAP Lake, Cambodia IUKWC June 2017: Enhancing Freshwater Monitoring Through Earth Observation Spyrakos et al., submitted ….
  14. 14. Algorithm validation Step 1: Validation of original algorithms Step 2: OWT cluster-wise algorithm tuning Step 3: Algorithm selection per cluster or cluster family LIMNADES in situ MERIS matchups Optical Water Types IUKWC June 2017: Enhancing Freshwater Monitoring Through Earth Observation
  15. 15. Algorithm validation IUKWC June 2017: Enhancing Freshwater Monitoring Through Earth Observation
  16. 16. In situ algorithm validation Y=0.7692x + 0.4175 R2=0.698 MAPE=164.5 Y=0.7863x + 0.311 R2=0.7891 MAPE=84.67 Y=0.8313x + 0.2197 R2=0.7969 MAPE=92.96 • Retuning per cluster reduced uncertainty compare to original model • Further improvements evident when using the best performing algorithm per cluster. Gurlin 2011 original output Gurlin retuned per cluster Dynamic algorithm selection per cluster IUKWC June 2017: Enhancing Freshwater Monitoring Through Earth Observation Neil et al., in prep….
  17. 17. MERIS Calimnos match-ups Atmospheric correction Polymer, Scape-M, CoastColour, Fub, Boreal Lakes, Megs Constituent Retrieval FLH, C2R, BL, EUL etc. Constituent Retrieval (Chla, TSM, CDOM, PC) cloud OR cloud shadow OR snow_ice OR Glint (Idepix) Input from initial in-situ validation to screen out poorly performing algo. Standardisation of Rrs and OWT-membership function 3x3 sigma-filter Ground data Chla (>50000 spec or HPLC from ~2000 inland water systems), TSM (8760 from ~500 water systems), CDOM (2000 from 78 inland water systems), PC (532 from 48), in-situ Rrs (3000 from 250) Scores Quality flags MERIS Algorithm validation IUKWC June 2017: Enhancing Freshwater Monitoring Through Earth Observation
  18. 18. Algorithm performance Slope Normalised score [1] Correlation r Normalised score [2] IUKWC June 2017: Enhancing Freshwater Monitoring Through Earth Observation Spyrakos et al., in prep ….
  19. 19. Water type family Optical water types Suggested algorithm 1 3; 9; 10; 13 OC2-like 2 2; 8; 11; 12 Rrs708/Rrs665 3 1; 4; 5; 6 Gons, 2005 4 7 QAA (Mishra et al., 2013) Dynamic Alogorithm Selection ⎯ a dynamic processing chain for remote sensing of inland water quality IUKWC June 2017: Enhancing Freshwater Monitoring Through Earth Observation
  20. 20. Operationalisation IUKWC June 2017: Enhancing Freshwater Monitoring Through Earth Observation
  21. 21. Some reflections The Razelm lagoon system, Romania • Monitoring ~ 50 % of world’s freshwater – ESA Sentinel 3 (OLCI) and Sentinel 2 (MSI) • Dynamic algorithm selection based on OWTs provides more accurate products • Processing chain operational for a continuum of optically complex River-Sea Systems (DANUBIUS-RI) • Data now being used to interpret drivers controlling lake status and change • Continuing need for high quality in situ matchup data for EO, currently are limited • biogeochemical >> radiometry • Chla > TSM >> CDOM > PC > Kd • GloboLakes simply not possible without community- wide collaboration IUKWC June 2017: Enhancing Freshwater Monitoring Through Earth Observation
  22. 22. Thank you! Andrew Tyler Professor of Environmental Monitoring Biological and Environmental Sciences University of Stirling t +44 1786 467838 e a.n.tyler@stir.ac.uk w www.stir.ac.uk w www.globolakes.ac.uk w www.limnades.org follow @globolakes Acknowledgements Natural Environment Research Council U ESA Diversity II Over 30 data contributors around the w

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