MONITORING WATER QUALITY OF THE PERIALPINE ITALIAN LAKE GARDA THROUGH MULTI-TEMPORAL MERIS DATA

1. MONITORING WATER QUALITY OF THE PERIALPINE ITALIAN LAKE GARDA THROUGH MULTI-TEMPORAL MERIS DATA Gabriele Candiani(1), Dana Floricioiu(2), Claudia Giardino(1), Helmut Rott(2) (1)Optical Remote Sensing Group-IREA, National Research Council, via Bassini 15, I-20133 Milan, Italy (2)Institute of Meteorology and Geophysics, University of Innsbruck, Innrain 52, A-6020 Innsbruck, Austria

2. Objective This work represents ongoing research efforts aimed at developing remote sensing strategies which address problems of water quality in Lake Garda Parameterisation of a bio-optical model Time-series of FR MERIS data L1P-derived chl-a concentrations vs L2P-Algal2 vs in situ data

3. The distribution of water resources (Mosello & Salmaso, 2005)

4. Study area Lake Garda is the largest Italian lake and one of the most important lake of the European region and needs accurate care for its natural relevance and its importance due tourism, drinking water, water supply, irrigation and recreation. The lake was chosen because the EO-related activity has a pretty long tradition at this lake and optical properties are well-studied. The lake dimension is in agreement with the pixel size of FR MERIS data. Oligo-mesotrophic state (OECD)

5. The MERIS FR dataset (1/2) Chlorophyll-a is the only parameter that is routinely measured by the 3 local environmental protection agencies in charge for Laka Garda monitoring. AO553 & AO164 projects ESA PI projects

6. The MERIS FR dataset (2/2) L1P TOA radiances 6S code (with AOT550 measured in situ or estimated from imagery using the Dark Dense Vegetation approach (Floricioiu & Rott, poster) RrsMERIS R6S/ Atmospheric correction of L1P FR data

7. The bio-optical model (1/5) N=22 Phytoplankton absorption

8. The bio-optical model (2/5) Non-algal particles absorption

9. The bio-optical model (3/5) CDOM absorption

10. The bio-optical model (4/5) Backscattering coefficients

11. The bio-optical model (5/5) Lee Z, Carder K. L., Mobley C. D., Steward R. G. and Patch J. S., Hyperspectral remote sensing for shallow waters. I. A semianalytical model, Applied Optics, 37, 6329-6338, 1998. Parameterisation using HYDROLIGHT

12. MERIS & bio-optical modelling (1/2) Forward bio-optical modelling

13. Band ratio (br) Optimization (opt) MERIS & bio-optical modelling (2/2) Inversion of MERIS Rrs data

14. RMSE [mgm-3] = 1.20 0.87 1.60 Results (1/2) In situ chl-a are the average values from measurements provided by the 3 agencies In situ data only in the northern part Evaluation of L1P-derived chl-a products Anabaena bloom at the surface

15. >12 12 9 chl-a [mgm-3] 6 3 0 Results (2/2) L1P-derived Algal2 L1P chl-a & Algal2 products show

16. Conclusions and future work Preliminary results obtained from 6S corrected L1P FR MERIS data are promising to implement a in situ-independent method to assess chl-a concentration in Lake Garda (RMSE < 1 mgm-3 with the optimisation method using B4 to B9). On the average L2P data give also good results but the spatial information is minor due to the presence of masked pixels. More images, acquired as close as possible to field data, are necessary to verify the method to invert Rrs spectrum (using br algorithms, opt techniques, others?) or the accuracyof Algal2 products (and of L1P irradiance-reflectance products). The effect of SIOPs on chl-a assessment had to be better understood. New data of Lake Garda waters are going to be collected to increase the knowledge on optical properties and to verify the optical closure of the bio-optical modelwith in situ measured Rrs values.

17. Acknowledgements MERIS data were supplied by ESA (AO553 and AO164 PI projects) In situ data were provided by APPA Trento, ARPAV Veneto and ASL-Brescia We are very grateful to A. G. Dekker, V. E. Brando & N. Strömbeck for the continuous support on our researches on Lake Garda This work was co-funded by Agenzia Spaziale Italiana Thank you very much for you attention