# Presentation: 592580

## 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

## Some History

- 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, 18-22 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. 559.
- 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, 259-266.
- 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, 423-441.
- Kahru, M., B.G. Mitchell, A. Diaz, M. Miura (2004). MODIS Detects Devastating Algal Bloom in Paracas Bay, Peru, *EOS Trans. AGU*, 85 (45), 465-472.

- 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
- 645 nm	250 m		sediments, turbidity, IOPs
- 859		250		aerosols
- 469		500		*C**a*, IOPs, CaCO3
- 555		500		*C**a*, IOPs, CaCO3
- 1240 		500		aerosols
- 1640		500		aerosols
- 2130		500		aerosols

## Slide 5

**Notes:**
Straylight
PAR
Calcite

## Slide 6

## Expanded MODIS Ocean Band Suite

## Characterization & Calibration

- Relative spectral response functions: Rayleigh & aerosol tables

- Polarization sensitivities (reanalysis of pre-launch testing)

## Polarization Sensitivity

## 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 nm

- Ratio 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.

- Aggregation

- Interpolation

- Observed (TOA) radiances, geolocation, radiant path geometries interpolated or aggregated to a com  mon resolution at start.

## Chlorophyll: 1000-meter resolution

- 0.4

- 100

- mg m-3

- OC3 = f(443,488,551)

- OC2 = f(469,555)

## Chlorophyll: 1000 & 500-meter

- 0.4

- 100

- mg m-3

- OC3 = f(443,488,551)

- OC2 = f(469,555)

## RGB Image: 250-meter Resolution

## RGB Image: 250-meter Resolution

## nLw(645): 250-meter resolution

- -0.1

- 3.0

- mW cm-2 m-1 sr-1

## In Situ Chlorophyll Data

*In Situ* Chlorophyll Data
- ~ 20 year record

- SIMBIOS/Harding
- 3,000 stations
- CBP
- 15,000 stations
- ( fluorometrically derived )

## Spatial Stratification

- from *Magnuson et al. 2004*

**middle**

**upper**

**lower**

## Satellite vs In Situ

**middle**

**upper**

**lower**

- NIR             SWIR

## SWIR-based aerosol determination significantly reduces bias in Ca retrievals relative to historical record for all seasons. 

- 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 

- Match-up with AERONET

- AOT Comparison

- Development of regional aerosol models
- See poster by E. Kwiatkowska

## New AERONET CIMEL Site on Smith Island

## Correction for NO2 Absorption

- Correction for NO2 Absorption

- OMI/Aura Tropospheric NO2

- MODIS/Aqua RGB

## 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: http://oceancolor.gsfc.nasa.gov/DOCS/modis_hires/

## 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 !

**Thank You !**

## Expanded MODIS Ocean Band Suite

## Expanded MODIS Ocean Band Suite

## Slide 31

## Chlorophyll: 500-meter Resolution

- 0.4

- 100

- mg m-3

- OC2 = f(469,555)

- OC3 = f(443,488,551)

## Aerosols from SWIR

- Evaluate standard and alternate aerosol determination
- aerosol determined via NIR at 748 and 869 nm
- aerosol 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*

## Monthly Mean Ca Time-Series ComparisonMid Bay

- NIR_x0016_

- SWIR_x0016_

- Monthly Mean *C**a* Time-Series ComparisonMid 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).

## Slide 36

**Notes:**
Completed chlorophyll algorithm evaluation, including both standard global algorithms and regionally tuned variants (J. Werdell).

Provided processing software (SeaDAS) and support to NOAA for implementation of an operational Bay monitoring capability using SeaWiFS LAC (with MODIS to follow).

## Thank You !

**Thank You !**