# Presentation: 710678

## Satellite Remote Sensing of Ocean Color and Temperature

**Bryan Franz**
- NASA Ocean Biology Processing Group

- University of Queensland, Brisbane, 21 May 2007

## NASA's Goal

- ocean color (and sst) data to the
- broadest user community in the most
- timely and efficient manner possible.

## NASA Ocean Biology Processing Group

- Ocean Color
- Missions to Measurements
- Sensor calibration/characterization
- Product validation (SeaBASS MDB)
- Algorithm development and evaluation (NOMAD)
- User processing and display (SeaDAS)
- User support (Ocean Color Forum)
- Global processing & distribution
- SeaWiFS
- MODIS
- CZCS
- OCTS
- SST processing for MODIS

## My Background

- F-22 Raptor

- Ocean Color

- Aeronautical Engineering
-     aerodynamic design

- Space Science
-     interplanetary dust modeling
-     instrument calibration

- Earth Science
-     atmospheric correction
-     calibration & validation
-     sensor intercomparison

## What is ocean color?

## Marine Spectral Reflectance

- Ocean color is the measurement
- of spectral distribution of radiance
- (or reflectance) upwelling from the
- ocean in the visible regime.

## Quantifying Phytoplankton 

**Quantifying Phytoplankton **
**Processes Remotely**

- CH3

***Chlorophyll-a***

***Marine Spectral Reflectance***

***Chlorophyll Algorithm***

## Phytoplankton

- the chlorophyll concentration that we observe is associated with the distribution of phytoplankton
- phytoplankton are microscopic plants that represent the first link in the marine food chain
- the patterns of distribution are related to both physical and biological processes
- phytoplankton require light, water, nutrients, and carbon dioxide to grow

## Global Carbon Budgets

**Humans**

**Ocean**

**38,000**

**Land**

**2000**

- 760** **(+3.3/yr)

**Atmosphere**

- 7.1 PgC/yr

- Petagrams (Pg) of Carbon

## Why measure phytoplankton from space?

## Tasmania

**Tasmania**

**Australia**

## California 

- California

## The warm heart of the Gulf Stream is readily apparent in the top SST image. As the current flows toward the northeast it begins to meander and pinch off eddies that transport warm water northward and cold water southward. The current also divides the local ocean into a low-biomass region to the south and a higher-biomass region to the north. 

- The warm heart of the Gulf Stream is readily apparent in the top SST image. As the current flows toward the northeast it begins to meander and pinch off eddies that transport warm water northward and cold water southward. The current also divides the local ocean into a low-biomass region to the south and a higher-biomass region to the north.
- The data were collected by MODIS aboard Aqua on April 18, 2005.

## Impact to Human Health

- nodularia spumigena was reported in the  Baltic Sea.  On 24 July 2003, SeaWiFS captured this view of the bloom.

## Impact to Fisheries

- coccolithophore

- Coccolithophore Bloom

**Notes:**
 coccolithophore blooms can be identified from space because of color
 image shows bloom in Bering sea
 reflective carbonate plates are shed in water, giving it opaque, milky quality
 reduced water clarity affects ecology
birds can’t fish - starve
 fish avoid - miss spawning runs
 monitoring allows for better planning

## Impact of Natural Disasters

- Hurricane Floyd
- massive flooding
- rivers carried
- sediment
- sewage
- discharged into coastal areas
- resulted in anoxic conditions in bay

- Sept. 23, 1999

- Pamlico
- Sound

- Albemarle
- Sound

- Cape Fear
- River

**Notes:**
 image of Cape Hatteras region, east coast US
 estuaries appear dark brown or black (usually green or blue)
 caused by contaminatin from pig farms, inundated by flood waters
 can see extent of pollution in SeaWiFS imagery
 helps to identify where long-term remediation is required

## SeaWiFS captures

- El Nino / La Nina
- transition

- January 1998

- July 1998

## Chronology of NASA’s

**CZCS**

**MODIS Terra**

**SeaWiFS**

**NO DATA**

***Chronology of NASA’s***
***Ocean Color Measurements***

**MODIS Aqua**

**NPOESS**
**VIIRS**

- Sea Surface Temperature: AVHRR, MODIS, VIIRS, ...

- Winds: SSMI, Nscat, Quikscat, SeaWinds, ...

- Sea Surface Topography:  TOPEX, Jason, Grace, OSTM, ...

- Other
- Satellite Data

- Salinity:  Aquarius

**An advanced**
**NASA mission ?**

## Operational MODIS Ocean Band Suite

- VIS/NIR
- Ocean Color

## Slide 20

## Light Paths to the SensorScattering and Attenuation of Reflected Solar Bands

- Light Paths to the SensorScattering and Attenuation of Reflected Solar Bands

## Ocean Color from Space

-     1% error in instrument calibration or atmospheric model
- ~10% error in water-leaving radiance

## Effects of the Atmosphere

- Gaseous absorption (ozone, water vapor, oxygen)
- Scattering by air molecules (Rayleigh)
- Scattering and absorption by aerosols (haze, dust, pollution)
- Polarization (MODIS response varies with polarization of signal)

- Rayleigh (80-85% of total signal)
- small molecules compared to nm wavelength, scattering efficiency decreases with wavelength as -4
- reason for blue skies and red sunsets
- can be accurately approximated for a given atmospheric pressure and geometry (using a radiative transfer code)

- Aerosols (0-10% of total signal)
- particles comparable in size to the wavelength of light, scattering is a complex function of particle size
- whitens or yellows the sky
- significantly varies and cannot be easily approximated

## Surface Effects

- Sun Glint

- White Caps

- Corrections based on statistical models (wind & geometry)

## Atmospheric Correction

- td() Lw() = Lt() / tg() / fp() - TLg() - tLf() - Lr() - La()

- TOA

- gas

- pol

- glint

- whitecap

- air

- aerosol

- Lw(=NIR) ≈ 0 and can be estimated (model extrapolation
- from VIS) in waters where *C**a* is the primary driver of Lw().

- But, we need aerosol to get Lw()

## Aerosol Determination in Visible Wavelengths

-  () =

- Given retrieved aerosol reflectance at two 
- and a set of aerosol models fn(,0,).

- a() & a()

- a(NIR)  as(NIR)

- as()

- as()

- model

-  (,)

-  (,)  =

- as()

- as()

## Atmospheric Correction

- td() Lw() = Lt() / tg() / fp() - TLg() - tLf() - Lr() - La()

- TOA

- gas

- pol

- glint

- whitecap

- air

- aerosol

- nLw() = Lw() fb() / td0() 0 f0

- brdf

- Sun

## P.J. Werdell, 2007

## Level-2 Ocean Color Processing

- Determine atmospheric and surface contributions to total radiance at TOA and subtract.
- Normalize to the condition of Sun directly overhead at 1 AU and a non-attenuating atmosphere (nLw or Rrs = nLw/F0).
- Apply empirical or semi-analytical algorithms to relate the spectral distribution of nLw or Rrs to geophysical quantities.
- Assess quality (set flags)

## Calibration

## Temporal Calibration

## MOBY

- MOBY is used to adjust prelaunch calibration for visible bands using satellite-buoy comparisons.

- Vicarious Calibration

**MOBY**

## Are the results valid?

## Available In Situ Match-Ups by Mission

- Available *In Situ *Match-Ups by Mission

- SeaWiFSSept 1997 - Present

- MODIS/AquaJuly 2002 - Present

## Comparison of Water-Leaving Radiances to In Situ

- Comparison of Water-Leaving Radiances to *In Situ*

- SeaWiFS

- MODIS/Aqua

## Comparison of Chlorophyll Retrievals to In Situ

- Comparison of Chlorophyll Retrievals to *In Situ*

- SeaWiFS

- MODIS/Aqua

## Seasonal Chlorophyll Images

**0.01-64 mg m****-3**

- Summer 2004

- Winter 2004

- SeaWiFS

- MODIS/Aqua

- Winter 2004

- Summer 2004

## Definition of Trophic Subsets

- Deep-Water (Depth > 1000m)

- Oligotrophic (Chlorophyll < 0.1)

- Mesotrophic (0.1 < Chlorophyll < 1)

- Eutrophic (1 < Chlorophyll < 10)

## Comparison of Spectral Distribution Trends

- MODIS & SeaWiFS Mean nLw

## Chlorophyll Comparisons

- MODIS / SeaWiFS

- Oligotrophic

- Mesotrophic

- Eutrophic

## Challenges to Remote Sensing of Coastal Waters

- Temporal and spatial variability
- Straylight contamination from land
- Non-maritime aerosols (dust, pollution)
- Region-specific models required
- Absorbing aerosols
- Anthropogenic emissions
- Suspended sediments and CDOM
- Invalid estimation of Lw(NIR), model not fn(Ca)
- Saturation of observed radiances
- Bottom reflectance

## Correction for NO2 Absorption

- Correction for NO2 Absorption

- OMI/Aura Tropospheric NO2

- MODIS/Aqua RGB

## Satellite vs In Situ

**middle**

**upper**

**lower**

- NIR             SWIR

## 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

## RGB Image: 250-meter Resolution

## nLw(645): 250-meter resolution

- -0.1

- 3.0

- mW cm-2 m-1 sr-1

## Data Products and Distribution

## Standard Ocean Products

- Ocean Temperature (MODIS only)
- Long-wave SST (11-12 m), day and night
- Short-wave SST (3.9 - 4.0 m), night only
- SST quality level
- Ocean Color
- Normalized water-leaving radiances, nLw()
- Chlorophyll, *C**a*
- Diffuse attenuation, Kd(490)
- Aerosol type and concentration
- Processing flags
- Data Types
- Level-1: observed radiances (swath-based)
- Level-2: retrieved geophysical parameters (swath-based)
- Level-3: global gridded composites (daily, 8-day, monthly, merged)

## SeaDASData Processing, Analysis, and Display Software

-  free
-  multi-mission
-  display tools
-  analysis tools
-  processing
-  open source

## Examples of Non-standard Ocean Products

- Alternate *C**a* and *K**d* algorithms
- Chlorophyll fluorescence, FLH
- Particulate inorganic carbon, Calcite
- Inherent optical properties (various bio-optical models)
- absorption (total, phaeophytin, dissolved matter)
- backscatter (total, particulate)
- Euphotic depth (Zeu, Zsd)
- Spectrally integrated diffuse attenuation,* K**d*(PAR)

## Data Distribution

- Free and open data distribution policy
- Level-1, Level-2, and Level-3
- ocean color and SST
- CZCS, OCTS, SeaWiFS, MODIS
- Web-based browsing and direct ftp access
- Automated ordering system
- Subscription services
- Geographic and parameter sub-setting

## Remote Sensing of Coral Reefs

- http://oceancolor.gsfc.nasa.gov/cgi/reefs.pl

## Water Depth Classification from SeaWiFS

## http://oceancolor.gsfc.nasa.gov/cgi/reefs.pl

## http://oceancolor.gsfc.nasa.gov/cgi/reefs.pl

## http://oceancolor.gsfc.nasa.gov/_x0003_

- http://oceancolor.gsfc.nasa.gov/_x0003_

## Acknowledgements

- Scarla Weeks and the CMS
- thanks for the invite
- Chuck McClain and Gene Feldman of NASA
- thanks for paying the bill
- The Ocean Biology Processing Group
- thanks for doing all the work

## Slide 58

## Thank You!

## Slide 60

## Thank You!

## Multi-Mission Approach

- Common software for Level-1 through Level-3
- reduces potential for algorithm and implementation differences
- sensor-specific issues consolidated in i/o function and external tables
- Mission-independent, distributed processing system
- controls staging/sequencing of processing jobs for max through-put
- 150x global reprocessing for MODIS, 1600x for SeaWiFS
- Standard procedures for calibration and validation
- temporal calibration via On-Board Calibration system (OBC)
- vicarious calibration to MOBY (instrument + algorithm calibration)
- validation against SeaBASS *in situ* archive
- temporal trending analysis of Level-3 products

## Expanded MODIS Ocean Band Suite

## Slide 64

## Green color of plants, including phytoplankton, is a result of plant pigments, primarily chlorophyll a. 

**Phytoplankton – the principal source **
**of organic matter in the oceans which **
**sustains the marine food chain, a **
**biological pump which sequesters carbon **
**dioxide from the atmosphere into the deep ocean**

## Algal Blooms

- Feb. 25, 1999

**Notes:**
 monitoring harmful blooms from space gives coastal communities ability to take precautionary measures
 fishing bans
 beach closures
 image shows true color (top) and chlorophyll (bottom)  views of bloom in mouth of Mississippi
 similar information was used to monitor a red tide bloom off coast of Texas last fall

## Sea-viewing Wide Field-of-view (SeaWiFS) images of the Galapagos islands

- and surrounding waters from May 9, 1998 (top) and May 24, 1998 (bottom).
- The equatorial current shut down by El Niño reappeared over a period of days,
- indicated by the high concentrations of phytoplankton chlorophyll streaming
- to the west in the later image.

## On November 26, 2002, SeaWiFS captured this relatively clear view of southern Africa and the seas around it. Phytoplankton distributions that are barely discernible in the quasi-true-color image become much clearer in the image of computed chlorophyll concentrations. In the second image, the lower chlorophyll concentrations associated with the Agulhas Current ar visible along the southeastern coast of the continent. When this current meets the Antarctic Circumpolar Current, it gets retroflexed back towards the east and forms the meanders and eddies visible in the lower right quadrant of the image. Higher chlorophyll concentrations along the west coast of Africa result from upwelling associated with the Benguela Current which flows northward along the western edge of the continent. 

- On November 26, 2002, SeaWiFS captured this relatively clear view of southern Africa and the seas around it. Phytoplankton distributions that are barely discernible in the quasi-true-color image become much clearer in the image of computed chlorophyll concentrations. In the second image, the lower chlorophyll concentrations associated with the Agulhas Current ar visible along the southeastern coast of the continent. When this current meets the Antarctic Circumpolar Current, it gets retroflexed back towards the east and forms the meanders and eddies visible in the lower right quadrant of the image. Higher chlorophyll concentrations along the west coast of Africa result from upwelling associated with the Benguela Current which flows northward along the western edge of the continent.

## Level-2 Flags and Masking

- Chlorophyll

- RGB Image

- Glint

- Sediments

- Cloud

**Notes:**
Only masking is for clouds
Physical saturation, no retrieval
Can eliminate or change cloud masking, but can’t change saturation
Chlorophyll looks good, even near in glint (band ratio)What about radiances?

## Level-2 Flags and Masking

- nLw (443)

- RGB Image

- Glint

- Sediments

- Cloud

**Notes:**
Only masking is for clouds
Physical saturation, no retrieval
Can eliminate or change cloud masking, but can’t change saturation
Chlorophyll looks good, even near in glint (band ratio)What about radiances?

## Aerosol Determination in High Chlorophyll

- Atmospheric correction is applied iteratively using NIR reflectance modeling based on consecutive chlorophyll and reflectance retrievals (green & red)
- The modeling assumes
- NIR absorption to be due to water only, and
- NIR backscatter to be a function of particulates, colored dissolved organic matter, and detritus

- High chlorophyll waters (or turbid coastal water) may contain significant Lw contribution in the NIR

- 8.7 mg/m3

## Iterative Correction for Non-zero Lw(NIR)

- Assume Lw(NIR) = 0
- Compute La(NIR)
- Compute La(VIS) from La(NIR)
- Compute Lw(VIS)
- Estimate Lw(NIR) from Lw(VIS) + model
- Repeat until Lw(NIR) stops changing
- Iterating up to 10 times

## 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

- SWIR

## Retrieval Coverage Differences Between SST and OC

- RGB Image

- SST

- Chlorophyll

- Sun glint

- Sun glint

## MODIS/SeaWiFS Ratio Trends

- Oligotrophic

- Mesotrophic

- Eutrophic

- MODIS/SeaWiFS Ratio Trends

*C**a* Ratios

## Change in Chlorophyll Retrieval with Alternate Aerosol Determination Methods

- Change in Chlorophyll Retrieval with Alternate Aerosol Determination Methods

- NIR-based Aerosols

- SWIR-based Aerosols

## Comparison of Relative Temporal Stability in nLw Deep-Water, 8-Day Composites, Common Bins

- MODIS/Aqua

## Direct Comparison of Satellite nLw Retrievals Deep-Water, 8-Day Composites, Common Bins

- MODIS / SeaWiFS

- Direct Comparison of Satellite nLw Retrievals Deep-Water, 8-Day Composites, Common Bins

## 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

- spatial resolution and expanded dynamic range come at the cost of increased digitization error (reduced sensitivity at ocean radiances) and reduced signal to noise

## Chlorophyll: 1000 & 500-meter

- 0.4

- 100

- mg m-3

- OC3 = f(443,488,551)

- OC2 = f(469,555)

## SST Quality Levels

- QL=0

- QL=1

- QL=2

- QL=4

- QL=3

- Shortwave SST

- Shortwave SST QL

## Sea Surface Temperature

## Operational MODIS Ocean Band Suite

- VIS/NIR
- Ocean Color

- Thermal
- SST

## Level-2 SST Processing

- Convert observed radiances to brightness temperatures (BTs)
- Apply empirical algorithm to relate brightness temperature in two wavelengths to SST (regression against in situ buoy data)
-   sst = a0 + a1*BT1 + a2*(BT2-BT1) + a3*(1.0/-1.0)
- Assess quality (0=best, 4=not computed)
- e.g., cloud or residual water vapor contamination
- no specific “cloud mask”

## Nighttime SST Products

- Longwave SST

- Shortwave SST

- Cloud

- Cloud

## SST Validation

- Buoy Measurements

## Slide 87

## http://oceancolor.gsfc.nasa.gov/_x0003_

- http://oceancolor.gsfc.nasa.gov/_x0003_

## Chlorophyll

- Euphotic depth

- Kd(PAR)

- PAR

- SST (11-12m day)

## Slide 90