A Unified Approach to Remote Estimation of Chlorophyll a Concentration in Complex Inland, Estuarine, and Coastal waters

Transcription

1 A Unified Approach to Remote Estimation of Chlorophyll a Concentration in Complex Inland, Estuarine, and Coastal waters Wesley J. Moses 1, *, Anatoly A. Gitelson 1, Alexander A. Gilerson 2, and Daniela Gurlin 1 1 University of Nebraska Lincoln, NE, USA. 2 The City College of the City University of New York, NY, USA. *Currently at Naval Research Lab, Washington, D.C. 212 NASA Coastal & Inland Water Workshop, 2 Jun 212, Madison, WI

2 Outline 2 NIR red Model Calibration of NIR red Model Validation of NIR red model Comparison Amongst a Few NIR red Algorithms

3 Objective 3 Develop satellite based algorithms for remote estimation of chlorophyll a (chl a) concentration in Inland Estuarine Coastal waters Algorithms should be Accurate over a wide range of chl a concentrations Widely applicable to satellite data

4 Aren t Ocean Color Algorithms Already Available? 4 Reflectance Spectra.8 Absorption Spectra Rrs (Sr -1 ) Abs. Coefficient CDOM Chl-a Water NAP Wavelength (nm) Wavelength (nm) NIR-red models needed!!!

5 Abs. Coefficient λ 12 3 ~ nm Absorption Spectra CDOM Chl-a NAP Total Wavelength (nm) 67 NIR red Model /R 665 Water R 1 1 vs. Chl-a 1/R 665-1/R Chl-a (mg m -3 ) R 1 1 R R Chl-a λ 1 λ 2 λ 3 (1/R 665-1/R 78) x R R 1 1 R 1 2 vs. Chl-a Chl-a (mg m -3 ) 1 1 R R 1 R 2 3 vs. Chl-a Chl-a (mg m -3 ) Stumpf and Tyler s (1988) Model 1 R Rλ Chl-a 1 3 meant for waters without significant NAP and CDOM (Dall Olmo and Gitelson 25)

6 2 Band NIR red Model 6 Abs. Coefficient λ 1 ~ 67 nm Absorption Spectra Chl-a 1.6 NAP Total.8 CDOM Wavelength (nm) 67 Reflectance Spectrum Water λ 2 ~ 7 nm R 1 1 R 2 chl-a Gitelson et al. 1988; Gitelson 1992 Rrs (Sr -1 ) Wavelength (nm)

7 The NIR red models can be implemented using MERIS and MODIS spectral bands 7 Wavelength MODIS MERIS λ nm 665 nm λ 2 78 nm λ nm 753 nm Three-Band MERIS NIR-red Model: Chl - a 1 1 R 665 R78 R753 Two-Band MERIS NIR-red Model: Chl - a R R 78 Two-Band MODIS NIR-red Model: Chl - a R R 748

8 Sensitivity Analysis Gilerson et al. (21) examined the sensitivity of the NIR red models to various optical properties of water 8 Chl-a concentration vs. two-band NIR-Red model with variations in (a) CDOM absorption, <ay(4)<5m -1

9 Sensitivity Analysis 9 (b) CNAP concentration, <CNAP<1 mg/l (c) fluorescence quantum yield, η =.25,.5 and 1.. The two-band NIR-Red model is stable amidst variations of these optical properties!

10 Sensitivity Analysis The Impact of * a ph 1 The slope of the relationship between the NIR red model values and chl a concentrations is affected by * variations in a ph the specific absorption coefficient of phytoplankton (Gilerson et al. 21)

11 Outline 11 NIR red Model Calibration of NIR red Model Validation of NIR red Algorithms Comparison Amongst a Few NIR red Algorithms

12 Calibration & Validation of NIR red Models 12 Chesapeake Bay Azov Sea Nebraska Lakes Lake Kinneret (Sea of Galilee) 12

13 Calibration of the Models: Nebraska, Laboratory Analysis of Water Samples Upwelling Radiance Downwelling Irradiance

14 Calibration of the Models: Nebraska, MERIS 3-Band MERIS 2-Band (1/R 665-1/R 78) x R r 2 = Chl-a (mg m -3 ) R 78/R r 2 = Chl-a (mg m -3 ) Chl-a = (Model) Chl-a = 64.5 (Model) MODIS 2-Band r 2 =.78 R 748/R Chl-a (mg m -3 ) Moses 29; Gitelson et al. 211; Gurlin et al. 211

15 Low to Moderate Chl a Concentration, MERIS 3-Band MERIS 2-Band (1/R 665-1/R 78) x R r 2 = Chl-a (mg m -3 ) R 78/R r 2 = Chl-a (mg m -3 ) MODIS 2-band.5 r 2 =.14 R 748/R Chl-a (mg m -3 ) Moses 29, Gitelson et al. 211; Gurlin et al. 211

16 Outline 16 NIR red Model Calibration of NIR red Model Validation of NIR red Algorithms Comparison Amongst a Few NIR red Algorithms

17 Calibration & Validation of NIR red Models 17 Chesapeake Bay Azov Sea Nebraska Lakes Lake Kinneret (Sea of Galilee) 17

18 Validation of NIR red Algorithms Lake Kinneret, Israel MER 3-Band Model MER 2-Band Model 3 Chl-a (mg m -3 ) From 28 Equation y = x RMSE = 4.75 mg m -3 1:1 Line M easured Chl-a (mg m -3 ) Estimated Chl-a, mg m y =.9528x RMSE = 1.46 mg m -3 1:1 Line Measured Chl-a, mg m -3 Yacobi et al. 211 No need to re-parameterize NIR-red algorithms!

19 Validation of NIR red Algorithms Chesapeake Bay 25 Nebraska Lakes MER 2-Band Model MER 2-Band Model Chl-a (mg m -3 ) From 28 Equation y = 1.2x RMSE = 3.42 mg m -3 1:1 Line Measured Chl-a (mg m -3 ) Chl-a (mg m -3 ) From 28 Equation y =.9456x RMSE = mg m Measured Chl-a (mg m -3 ) 1:1 Line Moses 29, Gitelson et al. 28, Gitelson et al. 211 No need to re-parameterize NIR-red algorithms!

20 Validation of NIR red Algorithms 2 29 Nebraska Lakes MER 3-Band Model MER 2-Band Model Gurlin et al. 211 No need to re parameterize NIR red algorithms!

21 Accurate Results Even with Variations in a * ph 21 (Gurlin 212)

22 Estimation of chl a concentrations in different waters NIR Red 2 band algorithm with MERIS bands calibrated using the 28 Nebraska Lakes data 22 8 Lake Kinneret Estimated Chl-a (mg m -3 ) Chesapeake Bay Azov Sea 28 NE Lakes 29 NE Lakes 1:1 Line In Situ Measured Chl-a (mg m -3 ) Water Body Chl a Chl meas vs. Chl est RMSE mg m 3 mg m 3 NE Lakes x NE Lakes x Chesapeake Bay x Kinneret x Azov Sea x Wide Applicability???

23 Why does the NIR-red two-band model work so much better? Effect of TSS scattering mg m nm 58.1 mg m nm 67 nm 67 nm 75 nm 4.6 mg m mg m -3 Gurlin 212

24 Outline NIR red Model Calibration of NIR red Model Validation of NIR red Algorithms Comparison Amongst a Few NIR red Algorithms 24

25 Advanced NIR red Algorithms (Gilerson et al. 21) 25 Advanced MERIS 3 Band Advanced MERIS 2 Band Chl 1 1 -a R R R Chl-a 35.75R R Chl-a (mg m -3 ) Adv. MER 3-B MER 3-B Chl-a (mg m -3 ) Adv. MER 2-B MER 2-B R 665 R 78 R R665 R78 Advanced models are virtually the same as the models calibrated using data from Nebraska Lakes and gave results of comparable accuracy!

26 Comparison With Gons Algorithm R R.7 b.4 b b. 16 Chl-a b b b 1.61R R Gons Algorithm vs. MERIS 2-Band NIR-red Model (Gons 1999, Gons et al. 22, 25, 28) 7 Gons' Chl-a (mg m -3 ) R665 R78

27 Enhanced 3 Band Index 27 Index R R R R Calibrated for Lake Kasumigaura, Japan (Yang et. al., 21) Chl-a = (Index) Estimated Chl-a (mg m -3 ) :1 Line Measured Chl-a (mg m -3 ) Results for the 29 Nebraska Lakes Data

28 Conclusion 28 NIR red models with MERIS bands are consistently accurate over a wide range of chl-a concentration, including the critical low to moderate range No need for re parameterization for each different water body Two band NIR red model with MERIS bands is very reliable and thus well suited for application to satellite data Two band NIR red model with MODIS bands is not suitable for estimating low to moderate chl a concentrations

29 Acknowledgements 29 Research was supported by NASA/LCLUC and USEPA Field data collection involved assistance from Dr. Yosef Yacobi and the team at the Kinneret Limnological Laboratory, Israel UNL CALMIT staff and students Thank You! Contact:

30 References Dall Olmo, G., Gitelson, A. A., and Rundquist, D. C. (23). Towards a unified approach for remote estimation of chlorophyll a in both terrestrial vegetation and turbid productive waters, Geophysical Research Letters, 3(18), 1938, doi:1.129/23gl1865 Dall'Olmo, G. and Gitelson, A. A. (25). "Effect of bio optical parameter variability on the remote estimation of chlorophyll a concentration in turbid productive waters: experimental results." Applied Optics, 44(3): Gilerson, A., Gitelson, A., Zhou, J., Gurlin, D., Moses, W., Ioannou, I., and Ahmed, S. (21). "Algorithms for remote estimation of chlorophyll a in coastal and inland waters using red and near infrared bands". Optics Express, 18(23): Gitelson, A. (1992). "The Peak near 7 Nm on Radiance Spectra of Algae and Water Relationships of Its Magnitude and Position with Chlorophyll Concentration." International Journal of Remote Sensing, 13(17): Gitelson, A., and K. H. Mittenzwey. (1988). In situ Monitoring of Water Quality on the Basis of Spectral Reflectance, Int. Revue Ges. Hydrobiol., 73(1): Gitelson, A., Szilagyi, F., and Mittenzwey, K. H. (1993). Improving Quantitative Remote Sensing for Monitoring of Inland Water Quality, Water Research, 27(7): Gitelson, A., Dall'Olmo, G., Moses, W., Rundquist, D. C., Barrow, T., Fisher, T. R., Gurlin, D. and Holz, J. (28). "A simple semi analytical model for remote estimation of chlorophyll a in turbid waters: Validation." Remote Sensing of Environment, 112(9): Gitelson, A., Gurlin, D., Moses, W., and Yacobi, Y. (211a). "Remote Estimation of Chlorophyll a Concentration in Inland, Estuarine, and Coastal Waters", Ch 18, pp , In Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications. Weng, Q. (Ed.), CRC Press, Taylor and Francis Group, ISBN: , 61 p. 3

31 References Gitelson, A. A., Gao, B. C., Li, R. R., Berdnikov, S., and Saprygin, V. (211b). "Estimation of chlorophyll a concentration in productive turbid waters using a Hyperspectral Imager for the Coastal Ocean the Azov Sea case study". Environmental Research Letters, 6(2423): 6 pp. Gurlin (212). Near infrared red models for the remote estimation of chlorophyll a concentration in optically complex turbid productive waters: from in situ measurements to aerial imagery". PhD Diss., University of Nebraska Lincoln, Lincoln, NE, USA Gurlin, D., Gitelson, A. A., and Moses, W. J. (211). "Remote estimation of chl a concentration in turbid productive waters Return to a simple two band NIR red model?". Remote Sensing of Environment, 115(12): Moses, W. J. (29). "Satellite based estimation of chlorophyll a concentration in turbid productive waters". PhD Diss., University of Nebraska Lincoln, Lincoln, NE, USA. Moses, W., Gitelson, A., Berdnikov, S. and Povazhnyy, V. (29). "Satellite estimation of chlorophyll a concentration using the red and NIR bands of MERIS the Azov Sea case study." IEEE Geoscience and Remote Sensing Letters, 4(6): Moses, W. J., A.A. Gitelson, R. L. Perk, D. Gurlin, D. C. Rundquist, B. C. Leavitt, T. M. Barrow, and P. Brakhage. (212a). Estimation of chlorophyll a concentration in turbid productive waters using airborne hyperspectral data, Water Research, 46: Moses, W. J., Gitelson, A. A., Berdnikov, S., Saprygin, V., and Povazhnyi, V. (212b). Operational MERIS Based NIR red Algorithms for Estimating Chlorophyll a Concentration in Coastal Waters The Azov Sea Case Study Remote Sensing of Environment, 121: Yacobi, Y. Z., Moses, W. J., Kaganovsky, S., Sulimani, B., Leavitt, B. C., and Gitelson, A. A. (211). "NIR red reflectance based algorithms for chlorophyll a estimation in mesotrophic inland and coastal waters: Lake Kinneret case study". Water Research, 45(7):