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3.1 IUKWC Workshop Freshwater EO - Stefan Simis - Jun17


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Progress and challenges towards a global near real-time inland water quality observation service.
Stefan Simis (PML)

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3.1 IUKWC Workshop Freshwater EO - Stefan Simis - Jun17

  1. 1. Progress and challenges towards a global near real-time inland water quality observation service Stefan Simis Senior Earth Observation Scientist (Inland waters)
  2. 2. Challenges for a global inland water quality observation service • Many geographically sparse locations • Water types vary between and within water bodies • Different water types require different water constituent retrieval algorithms • Third party algorithms: no consistent programming language • Validation data are sparse, not readily available for new sensors • Output formats and interfaces suitable for small and large scale users The Calimnos (‘good lake’) processor: - Designed to address (most of) these challenges efficiently. - Contains algorithms tuned to various water types - Can be extended with other water types, algorithms, sensors - Currently working with ENVISAT MERIS (2002-2012) data - Being readied for Sentinel-3 OLCI (2016 ->) data
  3. 3. Challenge: many geographically sparse locations NERC GloboLakes time-series 2002-2012 1000 lakes ‘linking water quality to environmental change’ Algorithm development and validation Copernicus Global Land Service (CGLOPS) Lake Water 10-day operational public service: reflectance, temperature, turbidity, trophic state index Available 2018
  4. 4. Challenge: many geographically sparse locations Sensor n files / scenes / passes Daily Volume MERIS (300m)* ~60 ~60 GB S3-A OLCI (300m) ~220 ~130 GB S2-A MSI (10-60m) 4204 ~2.1 TB Daily volume of satellite data Processing whole satellite passes (pole to pole) requires large processing (100s PCs) and storage capacity. Solution for 300m resolution data: - Download (nearly) all passes - Subset areas around individual lake - Process in parallel and store output - Aggregate results at desired interval Daily computing <10 PCs for 1000 lakes: • scalable and transferable • Fast archive reprocessing (algorithm testing) However: • all algorithms must accept spatial subsets • Sentinel-2 processing will require global data access, optionally cloud-based approach • S2 and S3 volumes to double when B-satellites become operational *136 TB in 10-yr archive
  5. 5. Challenge: diversity in water types, algorithms Type Model Reference (Semi-)empirical NIR-red BR MERIS 2-Band 708/665 Gilerson et al. 2010 Gurlin et al. 2011 Gons et al. 2005 MERIS 2-Band 753/665 Gilerson et al. 2010 Gitelson et al. 2011 Moses et al. 2009. MERIS 3-Band Gitelson et al. 2008 Gitelson et al. 2011 Gurlin et al. 2011 Moses et al. 2009 MERIS NDCI Mishra et al. 2012 Empirical OC MERIS OC2E MERIS OC3E MERIS OC4E O’Reilly et al. 2000 Neural Network NN_Chl NN_IOP Ioannou et al. 2013 Analytical MERIS QAA [Turbid] Mishra et al. 2013 MERIS GSM Maritorena et al. 2002 MERIS Matrix Inversion Boss & Roesler 2006 Peak Height Method MPH Matthews et al. 2012 Type Model Reference Empirical Binding red Zhang 708 Vantrepotte 665 POWERS 560 Binding et al. 2006 Zhang et al. 2010 Vantrepotte et al. 2011 Eleveld et al. 2008 D’Sa 665/560 Dekker 490,560 Dekker 560,665 D’Sa et al. 2007 Dekker et al. 2002 Loisel 3-Band Loisel et al. 2014 (Semi-) Analytical Binding A Nechad 665 Nechad 681 Nechad 708 Nechad 753 Binding et al. 2010 Nechad et al. 2010 Type Model Reference Empirical Duan 709/620 Duan et al. 2012 Duan 3-Band Song 3-Band Duan et al. 2012 Song et al. 2013 (Semi-) analytical Mishra QAA 13 Mishra QAA 14 Simis NBR Mishra et al. 2013 Mishra et al. 2014 Simis et al. 2005 Chlorophyll-a PhycocyaninTSM
  6. 6. 13 optical water types… Lake Balaton Tonlé Sap Lake …mapped to time series of MERIS imagery (2002-2012) Lake Balaton, Hungary Lough Neagh, Northern Ireland In situ Satellite …each mapped to specific algorithms …to produce time-series without discontinuities
  7. 7. Highest density of observations for inland waters ever.. But still needs careful screening
  8. 8. POLYMER C2R Lakes GloboLakes OWT optical water types [LIMNADES 2016] Reflectance algorithms algorithm mapping algorithm blending chlorophyll-a, suspended matter, phycocyanin passes database S-3 [SAFE] MERIS [FSG/SAFE] ESA catalogue Discover Download Ingest Subset Idepix MPH Masking RGB Quality flags, surface properties, pixel statistics 1-d aggregate 1-w aggregate 1-m aggregate GloboLakes, v1.04 ‘level 1B’ data ‘level 2’ processing ‘level 3’ mapping & aggregation Architecture and performance
  9. 9. POLYMER GloboLakes OWT optical water types [LIMNADES 2016] Reflectance algorithms algorithm mapping Water Reflectance wavebands passes database S-3 [SAFE] MERIS [FSG/SAFE] ESA catalogue Discover Download Ingest Subset Idepix algorithm blending Chlorophyll-a Suspended matter Trophic state classes Turbidity 10-d aggregate Radiometric correction Representative spectrum Copernicus Global Land Service – Lake Water v1.1.0 Architecture and performance
  10. 10. Performance: GloboLakes 10-yr time-series for 1000 lakes 3 – 45 minutes per satellite pass per lake [or: 5 min avg per lake = 3.5 CPU days] Processing grid: 800 CPU cores Processing time: 7y 51d 2h 24m Input data volume (global archive): 136 TB Output data volume: 11.4 TB
  11. 11. Making data visible: GloboLakes portal screenshot of of monthly average chlorophyll-a (300m), July 2011
  12. 12. ESA Ocean Colour CCI Portal
  13. 13. ESA Ocean Colour CCI Portal
  14. 14. Monthly time series over box in northern Tanganyika ESA Ocean Colour CCI Portal
  15. 15. Data at 4-km but 1-km daily also demonstrated ESA Ocean Colour CCI Portal
  16. 16. State-of-the-art • Large and mostly clear open waters observable since 1997 • MERIS sensor (2002-2012) kicked things off for inland waters • Gradually moving from regional success stories to global services: – Lake-specific algorithms often outperform the water-type specific global algorithms.. – but global approach gives us decent picture of remote or under-sampled sites • Time-series data available on request, independent validation encouraged. • We can add additional lakes to our processing chain & visualisation • Community in situ data contributions (LIMNADES) essential for globally valid result for all water types (we are not there yet).
  17. 17. Validation, uncertainties, anomalies, drift In situ validation is a particular challenge in inland waters - Wide optical diversity that changes every day, no climatologies or ‘ocean provinces’ to fall back on - Moving towards: connectivity with high frequency in situ sensors, citizen observatories, supersites, national monitoring databases, LIMNADES, big data analytics
  18. 18. Improvements with Sentinel-3 Ocean Land Colour Instrument • Sentinel 3A launched in 2016, 20-yr mission • Overlapping sensors from 2018, revisit times halved, daily coverage (4 x data volume) • Added wavebands: – Improved atmospheric correction (under development) – Improved substance retrieval in optically complex waters • Sensor similar to MERIS so we can port algorithms over • However, independent validation must continue
  19. 19. Sentinel-2 MultiSpectral Imager Improvements • Very high global resolution: 10, 20, 60 m bands Challenges • Mixed land/water optical configuration – not optimal to retrieve optical water quality parameters • Algorithm development and validation will take time
  20. 20. Conclusions • Global inland water quality remote sensing requires specific data processing • Retrieval of water constituent concentrations relies on continuous broad- scale validation -> community effort • New generation of satellites and related services (Copernicus) can cause a step change in data-driven water quality management… • Inland water remote sensing research needs to speed up to stay ahead of sensor developments, or risk forever working with land/ocean sensors • Algorithm research to continue, but hopefully with better focus on under- sampled water types: healthy inland waters are relatively under-sampled by optical water quality researchers -> focussing of effort needed. Chicken and egg problem: success stories needed at regional level to influence global satellite data uptake, algorithm development, and influence water quality management (through policy). For successful and sustainable regional application, extend support for in situ sampling and integrative research
  21. 21. Thank you