sediment concentrations from remote sensing becker · —, 2010, remote sensing of suspended...

Sediment Concentrations from Remote Sensing

Richard BeckerKevin Czajkowski

University of Toledo

Overview

How light interacts with water, sedimentSensors UsedAtmosphere correctionsModels for sediment concentrationExamples

What does a satellite see when What does a satellite see when looking at water?looking at water?

Upward scattering from Upward scattering from phytoplankton phytoplankton water moleculeswater moleculesinorganic suspended matterinorganic suspended matter

Absorption by Absorption by CDOMCDOMby pigmentsby pigmentswaterwatersuspended mattersuspended matter

SUN

PhytoplanktonCDOMSPMSPM Water

Sensor Spatial and Spectral Properties and Repeat Times

MODISDaily, 250m

ASTER>16 days, 15-30m

Landsat TM/ETM7-16 Day repeat, 30m

SPOT26 day repeat, 10m

In River assumptionsOptical components consist of:– Suspended Sediment– Phytoplankton– Colored Dissolved Organic Material

ApproachesReflectance Based Correlation– Assumes correlation between light reflected in

certain bands and suspended particulate concentration

Inherent Optical Properties (IOP) based– Uses linear relationship between particle

abundance and absorption and backscatter

1. Calculate atmospheric effects

2. Calculate at water reflectance (ρw)

3. Use relationship between ρw or band ratios and suspended particulates to calculate concentration

-OR-

3. Calculate IOPs from ρw4. Use relationship between IOPs and

suspended particulates to calculate concentration

Atmospheric Corrections

Water absorbs very well at long wavelengths

Measurements at long wavelength only measure reflection from the atmosphere

SUN

PhytoplanktonCDOMSPMSPM Water

Based on reflectances at longer wavelengths, atmospheric contribution is calculated

1.Compute Rayleigh scattering from air and remove

2.Use aerosol models for a variety of conditions• Fit iteratively using

multiple NIR bands

Problem: – In sediment rich water,

NIR light may not be entirely absorbed by water, leading to over-correction

Solution: – Use longer wavelength

(SWIR) bands where water absorption is even higher

Using Look Up Tables to Identify Model

Lookup tables contain values generated by different aerosol models and varying solar & viewing geometries for multiple spectral bands.

SEADAS software uses 12 aerosol models generated using three aerosol types (maritime, coastal, tropospheric)

412

443

490555

765

865

Other Atmosphere modelsCOST Model (Chavez, 1996)6S code for SPOT (Vermote, 1997) Bio-optical model of NIR component (Bailey, 2010)

Reflectance from sediment laden water before and after atmosphere correction

From Wang, et al., 2010

Reflectance vs sediment

concentration

From Wang, et al., 2010

Results based on MODIS Band 2 (Red),Band 5 (NIR)

From Wang, et al., 2010

Calculates fit based on linear fit of concentration or ln concentration vs Reflectance from single or multiple bands

Reflectance Band RatioUses Ratio e.g. NIR/GreenEstablishes non-linear fit based on modeled form of relationship

Simulated relationship between concentration and reflectance ratio

From Doxaran, 2002

Observed relationship between concentration and reflectance ratio

From Doxaran, 2002

Calculating from IOPs

)()()()(λλ

λλb

brs

babCR+

×≈

Calculate a, b using existing models

Perform linear fit to backscatter data to estimate suspended matter concentration

Example – Maumee River

W

16 August 1999 (22)

W

1 September 1999 (19)

W

17 September 1999(14)

W

4 November 1999 (8)

Scale (Km)

20 0

Landsat Satellite Imagery to Estimate Sediment in the Maumee River

Ohio State University

Scale (Km)

20 0

27 March 2000 (56)

W

14 May 2000 (62)

1 July 2000 (45)

W

19 September 2000 (81)

WW

Stream Water Quality – Maumee River Basin, Ohio

Used the Lake Erie Center boat to validate the satellite imagery

Port Clinton: 6/8/2006 Port Clinton: 6/24/2006

(Silt Washout)

Turbidity Index

0

37

74

111

148

(NTU)

July 15, 1999Images by Todd Kunselman, Clarion University

Turbidity Index

0

37

74

111

148

(NTU)

Sept. 17, 1999

Turbidity Index

0

37

74

111

148

(NTU)

Nov. 4, 1999

Turbidity Index

0

37

74

111

148

(NTU)

March 14, 2001

Turbidity Index

0

37

74

111

148

(NTU)

July 7, 2002

Turbidity Index

0

37

74

111

148

(NTU)

August 8, 2002

ExamplesPavelsky, 2009– Athabasca and Peace River, CA– SPOT– ASTER– MODIS

Sediment Reflectance Relationship from SPOT and ASTER

From Pavelsky, 2009

Derived Sediment Concentrations from SPOT total reflectance Correlation

From Pavelsky, 2009

ExamplesWang et Al 2007, 2009– Yangzee River– Landsat ETM

Wang et Al, 2010– Yangzee River– MODIS 250 m

Landsat Derived Concentrations from ETM+: Upper Yangtze

Landsat Derived Concentrations from ETM+: Middle Yangtze

MODIS derived

sediment concentrations vs observed

Questions?

References Cited:Chavez, P.S., 1996, Image-based atmospheric corrections revisited and improved: Photogrammetric Engineering and Remote Sensing, v. 62, p. 1025-1036.Doxaran, D., Froidefond, J.M., and Castaing, P., 2003, Remote-sensing reflectance of turbid sediment-dominated waters. Reduction of sediment type variations and changing illumination conditions effects by use of reflectance ratios: Applied Optics, v. 42, p. 2623-2634.Nechad, B., Ruddick, K.G., and Park, Y., 2010, Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters: Remote Sensing of Environment, v. 114, p. 854-866.Ouillon, S., Douillet, P., Petrenko, A., Neveux, J., Dupouy, C., Froidefond, J.M., Andrefouet, S., and Munoz-Caravaca, A., 2008, Optical algorithms at satellite wavelengths for Total Suspended Matter in tropical coastal waters: Sensors, v. 8, p. 4165-4185.Pavelsky, T.M., and Smith, L.C., 2009, Remote sensing of suspended sediment concentration, flow velocity, and lake recharge in the Peace-Athabasca Delta, Canada: Water Resources Research, v. 45, p. 16.Tassan, S., 1997, A numerical model for the detection of sediment concentration in stratified river plumes using Thematic Mapper data: International Journal of Remote Sensing, v. 18, p. 2699-2705.Vermote, E., Vermeulen, A., Ouaidrari, H., and Roger, J.C., 1997, Atmospheric correction for shortwave sensors (MODIS, ASTER, MISR, POLDER, SeaWiFs, MERIS, VEGETATION): Physical Measurements and Signatures in Remote Sensing, Vols 1 and 2, p. 3-8.Wang, J.J., Lu, X.X., Liew, S.C., and Zhou, Y., 2009, Retrieval of suspended sediment concentrations in large turbid rivers using Landsat ETM plus : an example from the Yangtze River, China: Earth Surface Processes and Landforms, v. 34, p. 1082-1092.—, 2010, Remote sensing of suspended sediment concentrations of large rivers using multi-temporal MODIS images: an example in the Middle and Lower Yangtze River, China: International Journal of Remote Sensing, v. 31, p. 1103-1111.