Validation of ocean colour products in inland and coastal waters of Northern Europe

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Charlotte Gregory
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1 Validation of ocean colour products in inland and coastal waters of Northern Europe

2 Tiit Kutser 1, Susanne Kratzer 2, Krista Alikas 3, Jenni Attila 4, Kari Kallio 4, Evelin Kangro 3, Kersti Kangro 3, Birgot Paavel 1, Petra Philipson 5, Anu Reinart 3, Laura Siitam 6, Liis Sipelgas 6, Tuuli Soomets 1, Kaire Toming 1, Rivo Uiboupin 6 1. Estonian Marine Institute, University of Tartu, Estonia 2. Department of Ecology, Environment and Plant Sciences, Stockholm University, Sweden 3. Tartu Observatory, Estonia 4. Finnish Environment Institute, Finland 5. Brockmann Geomatics, Sweden 6. Marine Systems Institute, Tallinn University of Technology, Estonia

3 Optical measurements and validation

6 The challanges at high latitudes Atmospheric correction Optically complex waters Both Nordic lakes and the Baltic Sea are CDOM-rich = low reflectance Standard algorithms do not perform well

7 The challanges at high latitudes a CDOM (400)=6.59 m -1 a CDOM (400)=55.6 m -1

8 The challanges at high latitudes Both carbon and iron affect water colour Their decreasing rate through time is different Köhler et al PlosOne

9 Some algorithms manage better with variable carbon-iron ratio Kutser et al Remote Sensing of Environment

11 Test of BEAM processors in Finnish lakes Processors (e.g. FUB, C2R) are typically trained with low CDOM values and are not suitable for the majority of lakes in Finland Boreal lakes prosessor, trained with typical concentrations in Finland, estimated TSM reasonably, but overestimated Chl-a and overestimated CDOM. Kallio, K., Koponen, S., Ylöstalo, P., Kervinen, M., Pyhälahti, T. & Attila, J Validation of MERIS spectral inversion processors using reflectance, IOP and water quality measurements in boreal lakes. Remote Sensing of Environment 157:

12 HELCOM EutroOper project: for improving eutrophication assessment. Examples of comparison between ICES station data and MERIS/FUB chl-a. On different parts of Baltic Sea, correspondence between MERIS/FUB and ICES MS data was good. 12

13 TSM validation in industrial coastal region: port dredging FUB C2R C2R adjusted -Scatter plots of in situ TSM and different MERIS TSM concentrations estimated with FUB (a), C2R (b), and C2R adjusted (c) in Pakri Bay. - MERIS Level 1 radiance data was provided by CoastColor project ( a443 validation: MERIS C2R algorithm Chl a validation: MERIS C2R algorithm MERIS C2R absorption at 443 nm, (1/m) y = x R² = In situ absorption at 443nm, (1/m) MERIS C2R Chl a (mg/ m 3 ) y = x R² = Laboratory Chl a (mg/m 3 )

14 Statistics between different satellite SPM products and in situ measurements of studies carried out in the Baltic Sea Study Region Processor RMSE Raag et al Raag et al Raag et al Vaičiūtė et al Vaičiūtė et al Kratzer et al Kratzer et al Kratzer et al Attila et al Attila et al Estonia (C) Estonia (C) Estonia (C) Lithuania (C) Lithuania (C) Sweeden (O) Sweeden (C) Sweeden (O) Finland (C) Finland (C) SD (mg/l) Root mean square error (RMSE), standard deviation (SD), mean bias, mean absolute bias, and correlation coefficient (r 2 ), using linear function between satellite products and in situ measurements retrieved from different studies that have been carried out in the Baltic Sea. (C) indicates the coastal region and (O) indicates the open sea. Mean bias Mean absolute bias r 2 (lin.) Over/under estimation (%) C2R 0,82 0,77(-) -0,28 2,65 0,61-12,2 C2R adjust. 0,77 0,75 (-) -0,15 2,53 0,62-11,9 FUB 1,36 0,85 (-) -1,06 3,39 0,51-45,8 C2R 5,43 1,89 (-) - 4,05 0,53 - FUB 4,25 4,04 (-) - 3,93 0,87 - MERIS stand. - - (43%) FUB - - (6%) FUB - - (23%) /-60 C2R C2R adjust. 0,86 (59%) 0,73 (50%) - 0,39-0,76 32, ,83 - Raag et al. 2014

15 Results of MERIS validation in NW Baltic Sea and Lake Vänern v We can correct for adjacency effects from land (e.g. with ICOL or SIMEC); Kratzer and Vinterhav, 2010; Beltrán-Abaunza et al., 2014 (ICOL); Sterckxs et al (SIMEC paper) v For chlorophyll-a the FUB algorithm works best; overestimation about 27% (Beltran et al., 2014); less error for monthly averages (about 10%, Harvey et al., 2015); MEGS: about 60% bias v For SPM the MERIS MEGS works best: mean normalized bias in the range of 8-16% (Beltran et al., 2014) v K d 490: only 3% bias (Alikas et al., 2015) v Standard errors for in-water measurements are 7-10% for chl-a, and about 10-13% for SPM (Kratzer, 2000); turbidity about 6% error (Kari et al., manuscript); CDOM about 6% error (Harvey et al b).

16 Baltic Sea problems Advanced Land Imager on EO-1 30 m resolution

17 Sentinel 2

18 Cyanobacterial blooms MERIS July 25, 2008

19 HYPERION 30 m Kutser 2004, L&O

20 Sentinel 2

23 Change in benthic habitat is an indicator of the state of coastal waters Hiiumaa Hiiumaa Hiiumaa Kaevatsi Kaevatsi Kaevatsi QuickBird 2,4 m spatial resolution imagery

24 Sentinel 2 10 m HySpex 40 cm

25 Thank you!

DEVELOPMENT OF MERIS LAKE WATER ALGORITHMS: VALIDATION RESULTS FROM EUROPE

DEVELOPMENT OF MERIS LAKE WATER ALGORITHMS: VALIDATION RESULTS FROM EUROPE Antonio Ruiz-Verdú (1) *, Sampsa Koponen (2), Thomas Heege (3), Roland Doerffer (4), Carsten Brockmann (5), Kari Kallio (6), Timo