NASA Ocean Color Image Gallery http://oceancolor.gsfc.nasa.gov

Optical Observations and Visible Remote Sensing

of Lake Superior

Colleen Mouw1

Audrey Barnett1

John Trochta2

Brice Grunert1

Angela Yu1

1Dept. Geological & Mining Engineering & Sciences,

Michigan Tech

2School of Aquatic & Fishery Sciences, Univ. Washington

IOCCG, Report 3

Non-algal particles (NAP)

Rrs(λ)

What a Satellite Radiometer Sees

Atmosphere Emergent flux from

below the surface

Constituents - Water

Dutkiewicz et al., 2014

Constituents – NAP & CDOM

Exponential decay:

Colored Dissolved Organic Matter (CDOM)

Non-Algal Particles

Inorganic (Minerals)

Organic (Detritus)

Dutkiewicz et al., 2014

Constituents - Phytoplankton

•  Pigment composition •  Taxonomic composition •  Physiological status •  Cell size

Dutkiewicz et al., 2014

MISSION CAPABILITY

ALGORITHMS !!

IN SITU OBSERVATIONS

OPERATIONAL CAPACITY

Empirical Semi-analytical

AOPs IOPs SIOPs Biogeochemical Parameters

Spectral

Spatial Temporal Signal:noise Atmospheric Correction

Satellite Instruments

In situ Instruments Observational Platforms

Protocols Training

Calibration

Product Availability

Validation Uncertainty

Processing Software

Fundamental Elements of Satellite Remote Sensing

Mouw et al., 2015

Components of Aquatic Color Remote Sensing

NAPCDOM

Aquatic Applications

Lake Superior – Optics

Mouw et al., 2013 Effler et al. 2010

•  High CDOM absorption (>75%) •  Oligotrophic, small chlorophyll dynamic range in

the open lake (0.4 – 0.8 mg m-3)

Superior

aCDOM is 10x greater

Non-algal particles

wavelength (nm)

Retrieval of chlorophyll concentration from an inversion algorithm approach was unsuccessful. The very large contribution of absorption due to CDOM to total absorption and the error in derived CDOM absorption being greater than phytoplankton absorption values make the deconvolution of absorption due to phytoplankton and consequently chlorophyll concentration from Rrs(λ) difficult.

CDOM Absorption Imagery

Buffer zones based on the distance from shore: 25 km

Mouw et al., 2013

Spatial / temporal understanding of aCDOM lends insight into carbon inputs and cycling within the lake as well as optical limitations for satellite retrievals of other biogeochemical parameters.

Imagery Example CDOM corrected [Chl] OC4 [Chl]

SeaWiFS 8/31/2006

CDOM Absorption

Mouw et al., 2013 Mouw et al., in prep

CDOM Corrected [Chl]

SeaWiFS, August 31, 2006 Mouw et al., in prep

aCDOM & [Chl] Time Series

Mouw et al., 2013; Mouw et al., in prep

0

0.2

0.4

0.6

0.8

1

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Year

[Chl

] (m

g m−3

)

< 10 km from shore10−25 km from shore> 25 km from shore

aCDOM (m-1)

[Chl] (mg m-3)

•  aCDOM bimodal annual distribution: Greatest peak in fall smaller peak in spring •  Mixing deep CDOM

reservoirs back into the surface

•  Summer: Photochemical degradation and microbial utilization.

0

0.2

0.4

0.6

0.8

1

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Year

[Chl

] (m

g m−3

)

< 10 km from shore10−25 km from shore> 25 km from shore

•  [Chl] bimodal annual distribution: Greatest peak in spring smaller peak in fall •  Looking into drivers of interannual bloom variability.

Trochta, Mouw & Moore, submitted

Optical Water Types

Frequency of classification – how often pixels had high membership in each class

Class 4

Highest to lowest constituent composition

HEAVIEST HEAVIER

MODERATE CLEARER CLEAREST

Trochta, Mouw & Moore, submitted

Bluest with lowest CDOM & particles (CLEAREST)

Greener class with high CDOM & moderate

particles (HEAVIER)

Similar to 2, but with high particles (HEAVIEST)

Bluer class with moderate CDOM & particles

(MODERATE)

Similar to 4, but with lower particles (CLEARER)

aCDOM(488)

b bp(

488)

Temporal Evolution

Trochta, Mouw & Moore, submitted

Highest to lowest constituent composition

HEAVIEST HEAVIER

MODERATE CLEARER

CLEAREST

SS

T (°

C)

Ch

l (m

g m

3)

& %

Ice

(x1

00

)

10

20

00

0.5

1

20

15

10

5

0

SS

T (°

C)

Mouw et al., in prep

Ice, Temperature and [Chl]

0 0.5 1 1.5 2 2.5 3 3.5

0

10

20

30

40

50

60

70

80

90

100

Chlorophyll concentration (mg/m3)

Dept

h (m

)

MayJun.Jul.Aug.Sep.Oct.

Comparison of [Chl] profiles between a very cold high ice year (1979, dotted line; Fahnenstiel and Glime, 1983) and a warm year (2013, solid line)

Deep Chlorophyll Layer

Optical Observations

•  Ed(λ), Lu(λ) à Rrs(λ) •  a(λ), ag(λ), c(λ) •  bb(λ) •  Chl, CDOM, PC fluor. •  CTD

Full characterization of optical properties needed for algorithm development and validation

Optical Sampling Locations

2013 2014

solid symbols - stations sampled more than once in a given year open symbols - stations sampled once in a given year

5 m

2013 2014 Optical Observations

Par

ticu

late

Abs

orpt

ion

CD

OM

Abs

orpt

ion

Photo credit: Chris Strait

Acknowledgements •  Gary Fahnenstiel (Michigan Tech U.) •  Tim Moore (U. New Hampshire) •  Mike Twardowski, Jim Sullivan (WET Labs) •  Galen McKinley, Haidi Chen (U. Wisconsin-Madison) •  Steve Effler, David O’Donnell, MaryGail Perkins, Chris Strait

(Upstate Freshwater Inst.) •  SeaWiFS, MODIS: NASA Ocean Biology Processing Group •  Ice: National Snow and Ice Data Center

Mouw Optics and Remote Sensing Laboratory

cbmouw@mtu.edu http://www.geo.mtu.edu/~cbmouw

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