Assessing the ability of current and future Landsat missions to monitor cyanobacteria blooms using modeled spectra matching

Ryan Ford

Dr. Anthony Vodacek

Dr. John Schott

7/12/2017

1

Landsat 8 image of Oneida Lake

captured September 19, 2013

IFYGL- the frustration begins

IFYGL 1972-1973: NOAA Citation for mapping turbidity

using color aerial photography

Piech, K.R. and Schott, J.R.,

“Measurement of Lake Eutrophication from

small-scale color imagery during the

IFYGL”, presented to ISP Commission VII

Symposium, October 1974.

Skylab Photos: chlorophyll maps Lake Ontario

Piech, K.R. and Schott, J.R., “Atmospheric corrections

for satellite water quality studies”, Proceedings of the

SPIE, Vol. 57, pp. 84-89, August 1974.

We didn’t have a clue!!!

Skylab Photos: chlorophyll maps Lake Ontario

Piech, K.R. and Schott, J.R., “Atmospheric corrections

for satellite water quality studies”, Proceedings of the

SPIE, Vol. 57, pp. 84-89, August 1974.

We didn’t have a clue!!!

Flash ahead 40 years

Landsat 8 does water!!!!: We’ve been beating the drum

• Pahlevan, N., Schott, J.R., Franz, B.A., Zibordi, G., Markham, B., Bailey, S., Schaaf, C.B., Ondrusek, S.G., Strait, C.M., “Landsat 8 Remote Sensing Reflectance (Rrs) Products: Evaluations, Intercomparisons, and Enhancements”, Remote Sensing of Environment, Vol. 190, pp. 289-301, March 1, 2017.

• Concha, J.A., Schott, J.R., “Retrieval of Color Producing Agents in Case 2 Waters Using Landsat 8”, Remote Sensing of Environment, http://dx.doi.org/10.1016/j.rse.2016.03.018, April 13, 2016.

• Schott, J.R., Gerace, A., Woodcock, C.E., Wang, S., Zhu, Z., Wynne, R.H., Blinn, C.E., “The Impact of Improved Signal-to-Noise Ratios on Algorithm Performance: Case Studies for Landsat Class Instruments”, Remote Sensing of Environment, http://dx.doi.org/10.1016/j.rse.2016.04.015, May 13, 2016.

• Pahlevan, N., Lee, Z., Wei, J., Schaaf, C.B., Berk, A., Schott, J.R., “On Orbit Radiometric Characterization of OLI (Landsat-8) for Applications in Aquatic Remote Sensing”, Remote Sensing of Environment, 154, pp. 272-284, Nov. 2014.

• Gerace A.D, Schott J.R, Nevins R; “Increased potential to monitor water quality in the near-shore environment with Landsat’s next-generation satellite.” Journal of Applied Remote Sensing Vol 7; pp. 073558-073558. May 2013.

• Pahlevan, N.; Schott, J. R.; , "Leveraging EO-1 to Evaluate Capability of New Generation of Landsat Sensors for Coastal/Inland Water Studies," Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of , Vol.PP, no.99, pp.1-15, 2013.

• Pahlevan, N., Schott, J. R., “Characterizing the relative calibration of Landsat-7 (ETM+) visible bands with Terra (MODIS) over clear waters: The implications for monitoring water resources”. Remote Sensing of Environment, Vol. 125, pp167-180, October 2012.

Lake Erie Bloom 2011

Harmful Algal Blooms: a growing problem

A new challenge

'Do not drink, do not boil’ Toledo 2014

Cyanobacteria Blooms in the Finger Lakes

7

New York State Department of Environmental Conservation reported blooms in

the Finger Lakes from 2012 to 2016. Red coloring represents location of bloom

and not spatial extent.

Concentration Retrieval Process

6

2.) Look-Up-Table Generation

1.) Hydrolight Run Inputs:

Chlorophyll, CDOM-yellowing

organics, Suspended Materials,

& Phycocyanin

Retrieved Water Quality Component

Concentrations

4.)Matching Data to Look-Up-Table

Spectral Response

3.) Spectral Sampling

Atmospherically

Compensated

L8 Rrs

Modeling Result Reporting

• Unless otherwise noted, the modeled system being shown have 12 bit quantization and Landsat 8 Requirement SNR

9

L8 Requirement Noise

Based on requirement SNR levels from L8 Design Document

L8 Operational Noise

Based on pre-launch measured L8 SNR levels

Band 1 Band 2 Band 3 Band 4 Band 5

SNR Ltypical

130 130 100 90 90

SNR Lhigh

290 360 390 340 460

Band 1 Band 2 Band 3 Band 4 Band 5

SNR Ltypical

237 355 296 222 199

SNR Lhigh

605 1127 1213 945 1009

Landsat 8 Copycat Model: requirement SNR

13

• Landsat 8 bands with no added spectral coverage

• Landsat 8 quantization level (12 bit)

• Landsat 8 Noise, based on requirement SNR

Modeled Retrieval Error: Noise, Quantization and a Yellow band

12

Incr

easi

ng

Qu

anti

zati

on

Increasing Noise

Operational SNR Requirement SNR

8 Bit

12 Bit

Combined Bands Improvement

15

Less

Improvement

More

Improvement

18

Chlorophyll-a (mg/m^3) Phycocyanin (mg/m^3)

Meas. Ret.

38.3 35.7

33.2 34.0

Field Sample

Chlorophyll

(mg/m^3):

Average

Percent

Error:

4.67%

NRMSE:

0.043

Image results: Honeoye Lake Retrievals

20

Chlorophyll-a (mg/m^3) Phycocyanin (mg/m^3)

Meas. Ret.

3.6 5

4.3 4.3

4.3 1.7

4.0 4.4

Field Sample

Chlorophyll

(mg/m^3):

Average

Percent

Error:

27.8%

NRMSE:

0.43

Image results: Owasco Lake Retrievals

Landsat Science

• 1972-73 Atmospheric compensation of MSS for USAF

• MSS for Gypsy Moth Walker, J.E., Schott, J.R., Gallagher, T.W., “An Investigation of Landsat

data as a base for developing a forest damage assessment system (FORDAS)”, Calspan Report YB-6128-M-1, prepared for USDA Forest Service, March 1978.

• LIDQA 1981-1985 (the Landsat 4&5 science team)

• 1992-3 The High Resolution Multispectral Stereo Imager (HRMSI)

• Landsat 7 Science team 1996-2001

• Resource 21

• Landsat Calibration Team 2001-

• LDCM-L8 Science team 2006-2011

• Landsat 2012-2017 Science team

Landsat: A History of Firsts

• MSS: First moderate resolution Space-based digital Multispectral

• TM: First less moderate resolution multispectral from space

• TM: First many band multispectral from space

• TM: First moderate resolution thermal

• Landsat Archive: First multi-decade open access calibrated human scale record of the planet

• Landsat 8: First very high radiometric resolution,

moderate spatial resolution sensor

16

Landsat 10???????????????

Continuity demands Firsts!

Landsat: A History of Firsts

• MSS: First moderate resolution Space-based digital Multispectral

• TM: First less moderate resolution multispectral from space

• TM: First many band multispectral from space

• TM: First moderate resolution thermal

• Landsat Archive: First multi-decade open access calibrated human scale record of the planet

• Landsat 8: First very high radiometric resolution,

moderate spatial resolution sensor

17

Landsat 10???????????????

Continuity demands Firsts!

18

Conclusion

Results are encouraging, but improvements can be made:

• Improving Model Design

– Increasing number of specific absorption spectra for test runs

– Remove production of non-physical combinations

• Refining Look-Up-Tables

- Determining how to best implement specific absorption variability

- Examining spectral matching interpolation and minimization algorithms

• Collecting Test Data

- Collect more reference data for 2017 Landsat 8 overpasses

21

Outline

Introduction Blooms in the Finger Lakes

Landsat 8

Methods Concentration Retrieval Algorithm

Modeling Retrieval Work

Results Modeled Retrieval Results

Preliminary Imagery Retrievals

Conclusion

20

CONCENTRATION RETRIEVAL ALGORITHM

21

Concentration Retrieval Process

22

1.) Hydrolight Run Generation

Specific

Absorption

Specific

Scattering

Concentration

Phase

Scattering

Phycocyanin (PC)

Chlorophyll – a (Chl)

Suspended Materials

(SM)

CDOM

Pure Water

Waterbody Components Component

Properties

Concentration Retrieval Process

6

2.) Look-Up-Table Generation

1.) Hydrolight Run Generation

Concentration Retrieval Process

6

2.) Look-Up-Table Generation

1.) Hydrolight Run Generation

Chl: 70 ug/L SM: 30 mg/L

CDOM: 1.2 1/m PC: 50 ug/L

Chl: 30 ug/L SM: 10 mg/L

CDOM: 0.5 1/m PC: 21 ug/L

Run 1857

Run 333

Concentration Retrieval Process

6

2.) Look-Up-Table Generation

1.) Hydrolight Run Generation Spectral Response

3.) Spectral Sampling

Concentration Retrieval Process

6

2.) Look-Up-Table Generation

1.) Hydrolight Run Generation Spectral Response

3.) Spectral Sampling

MODELING RETRIEVAL WORK

7

Modeling Process

8

Figure from Gerace et al. 2013

1

2

3 4

Hydrolight Final Concentrations

Modeling Process

8

Figure from Gerace et al. 2013

1

2

3 4

Hydrolight Final Concentrations

Modeling Process

8

Figure from Gerace et al. 2013

1

2

3 4

+

System Noise,

Quantization

Hydrolight Final Concentrations

Modeling Process

8

Figure from Gerace et al. 2013

1

2

3 4

Hydrolight Final Concentrations

Modeling Process

8

Figure from Gerace et al. 2013

1

2

3 4

Hydrolight Final Concentrations

MODELING RETRIEVAL RESULTS

9

Yellow Band Improvement

14

Less

Improvement

More

Improvement

Outlier Point

35

Outlier Point, With Yellow Band

36

Original Spectrum

37

Landsat 8 Spectrum

38

Landsat 8 and Added Bands Spectrum

39

20 nm Imaging Spectrometer Spectrum

40

10 nm Imaging Spectrometer Spectrum

41

Modeled Retrieval Error: Impact of a Yellow band

11

Higher

Error

Lower

Error

Red Edge Band Improvement

15

Less

Improvement

More

Improvement

PRELIMINARY IMAGERY RETRIEVALS

16

Honeoye Lake, October

45

DEC Bloom

Reported in Honeoye

Lake from:

August 21, 2015

To

October 9, 2015

Left image is from

Landsat 8

captured

September 16, 2015

Right image is from

Landsat 8

captured

October 11, 2015

Honeoye Lake Chl-a and PC

24

Long Pond and Cranberry Pond

47

Lake Ontario

Rochester, NY

Long Pond and Cranberry Pond Chl-a and PC

25

Honeoye Lake Bloom, September 2016

17

Landsat 8 image of Honeoye Lake

September 27, 2016

Owasco Lake Bloom, September 2016

19

Landsat 8 image of Owasco Lake

September 20, 2016