MODIS Land Bands for Ocean Remote Sensing: Application to Chesapeake Bay Bryan Franz NASA Ocean Biology Processing Group MODIS Science Team Meeting, October.

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Presentation transcript:

MODIS Land Bands for Ocean Remote Sensing: Application to Chesapeake Bay Bryan Franz NASA Ocean Biology Processing Group MODIS Science Team Meeting, October 2006, College Park, MD

Contents Why the land/cloud bands? Implementation & Sensor Characterization Results for Chesapeake Bay Future Plans

Gao, B.-C., M.J. Montes, Z. Ahmad, and C. O. Davis (2000). Atmospheric correction algorithm for hyperspectral remote sensing of ocean color from space, Applied Optics, 39, Arnone, R.A, Z.P. Lee, P. Martinolich, B. Casey, and S.D. Ladner (2002). Characterizing the optical properties of coastal waters by coupling 1 km and 250 m channels on MODIS – Terra, Proc. Ocean Optics XVI, Santa Fe, New Mexico, November. Li, R.-R., Y.J. Kaufman, B.-C. Gao, and C.O. Davis (2003). Remote Sensing of Suspended Sediments and Shallow Coastal Waters, IEEE Trans. on Geoscience and Remote Sensing, Vol. 41, No. 3 pp Miller, R.L. and B.A. McKee (2004). Using MODIS Terra 250 m imagery to map concentrations of total suspended matter in coastal waters, Remote Sensing of Environment, 93, Hu, C., Z. Chen, T.D. Clayton, P. Swarzenski, J.C. Brock, and F.E. Müller-Karger (2004). Assessment of estuarine water-quality indicators using MODIS medium-resolution bands: Initial results from Tampa Bay, FL, Remote Sensing of Environment, 93, Kahru, M., B.G. Mitchell, A. Diaz, M. Miura (2004). MODIS Detects Devastating Algal Bloom in Paracas Bay, Peru, EOS Trans. AGU, 85 (45), Some History Wang, M. and W. Shi (2005). Estimation of ocean contribution at the MODIS near-infrared wavelengths along the east coast of the U.S.: Two case studies, Geophys. Res. Lett., 32, L13606.

MODIS Land/Cloud Bands of Interest Band Wavelength Resolution Potential Use 1645 nm250 msediments, turbidity, IOPs aerosols C a, IOPs, CaCO C a, IOPs, CaCO aerosols aerosols aerosols

SWIR

Expanded MODIS Ocean Band Suite

Characterization & Calibration Relative spectral response functions: Rayleigh & aerosol tables Polarization sensitivities (reanalysis of pre-launch testing)

Meister, G., E.J. Kwiatkowska, and C.R. McClain (2006). Analysis of image striping due to polarization correction artifacts in remotely sensed ocean scenes. Proc. SPIE Earth Observing Systems XI, Polarization Sensitivity Polarization Angle pcpc Wavelength

Characterization & Calibration Relative spectral response functions: Rayleigh & aerosol tables Polarization sensitivities (reanalysis of pre-launch testing) Relative detector and sub-sampling corrections (striping)

Detector and Sub-sample Striping TOA Radiance 469 nmRatio of Adjacent Samples Along Scan, 469 nm

Characterization & Calibration Relative spectral response functions: Rayleigh & aerosol tables Polarization sensitivities (reanalysis of pre-launch testing) Relative detector and sub-sampling corrections (striping) Vicarious calibration to MOBY (preliminary)

Multi-Resolution Implementation from Gumley, et al. AggregationInterpolation Observed (TOA) radiances, geolocation, radiant path geometries interpolated or aggregated to a com mon resolution at start.

0.4100mg m -3 Chlorophyll: 1000-meter resolution OC3 = f(443,488,551)OC2 = f(469,555)

0.4100mg m -3 Chlorophyll: 1000 & 500-meter OC3 = f(443,488,551) OC2 = f(469,555)

RGB Image: 250-meter Resolution Potomac Rappahannock Patuxent Nanticoke Wicomico Pocomoke York James

RGB Image: 250-meter Resolution

nLw(645): 250-meter resolution mW cm -2  m -1 sr -1

In Situ Chlorophyll Data ~ 20 year record SIMBIOS/Harding 3,000 stations CBP 15,000 stations ( fluorometrically derived )

Spatial Stratification from Magnuson et al middle upper lower

Satellite vs In Situ middle upper lower NIR SWIR

Median Percent Difference from In Situ Chlorophyll SWIR-based aerosol determination significantly reduces bias in C a retrievals relative to historical record for all seasons. Best improvement in Spring-Summer, where aerosol optical thickness (SWIR signal) is highest.

Match-up with AERONET AOT Comparison Development of regional aerosol models See poster by E. Kwiatkowska

New AERONET CIMEL Site on Smith Island

See poster by Z. Ahmad Correction for NO 2 Absorption OMI/Aura Tropospheric NO 2 MODIS/Aqua RGB 20% increase in nLw(412)

Summary Developed processing capabilities to include higher resolution land/cloud bands in ocean remote sensing applications. Demonstrated some potential ocean products (500-meter chlorophyll, 250-meter nLw), and SWIR atmospheric correction. SWIR-based aerosol determination significantly reduced bias between retrieved and in situ chlorophyll. Software and tools distributed through SeaDAS, to encourage further evaluation and development by research community. More info:

Future Plans Develop more applicable aerosol models based on local AERONET observations Incorporate MODIS-derived water-vapor concentrations for improved water-vapor correction (significant in SWIR) Assist NOAA Coast Watch to implement an operational Chesapeake Bay monitoring system using MODIS Develop “high-resolution” Level-3 products (binned/mapped) –Rolling 3-day, merged sensors for increased coverage –Pilot project in Great Barrier Reef, University of Queensland

Thank You !

Expanded MODIS Ocean Band Suite

0.4100mg m -3 Chlorophyll: 500-meter Resolution OC2 = f(469,555)OC3 = f(443,488,551) Interpolated

Aerosols from SWIR Evaluate standard and alternate aerosol determination 1aerosol determined via NIR at 748 and 869 nm 2aerosol determined via SWIR at 1240 and 2130 nm Processed 150 MODIS/Aqua scenes over Chesapeake Bay to retrieve OC3 Chlorophyll at 1km resolution. Compared with historical record of in situ C a

NIR SWIR Monthly Mean C a Time-Series Comparison Mid Bay MODIS

Chesapeake Bay Collaboration Chesapeake Bay Program (MD, VA, PA, DC, Federal EPA), University of Maryland, Old Dominion, NOAA Coast Watch, and NASA OBPG. CBP is an on-going program of in situ monitoring with a large historical data set spanning ~ 20 years. OBPG is assisting with use of remote sensing data to augment field campaign, and supporting operational implementation within NOAA Coast Watch. Utilizing local expertise and in situ measurements (in-water and atmospheric) to evaluate and improve performance of satellite retrievals on a regional scale (regional algorithms & atmospheric models).

Thank You !