sediment concentrations from remote sensing becker · —, 2010, remote sensing of suspended...
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Sediment Concentrations from Remote Sensing
Richard BeckerKevin Czajkowski
University of Toledo
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Overview
How light interacts with water, sedimentSensors UsedAtmosphere correctionsModels for sediment concentrationExamples
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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
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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
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In River assumptionsOptical components consist of:– Suspended Sediment– Phytoplankton– Colored Dissolved Organic Material
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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
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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
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Atmospheric Corrections
Water absorbs very well at long wavelengths
Measurements at long wavelength only measure reflection from the atmosphere
SUN
PhytoplanktonCDOMSPMSPM Water
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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
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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
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Other Atmosphere modelsCOST Model (Chavez, 1996)6S code for SPOT (Vermote, 1997) Bio-optical model of NIR component (Bailey, 2010)
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Reflectance from sediment laden water before and after atmosphere correction
From Wang, et al., 2010
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Reflectance vs sediment
concentration
From Wang, et al., 2010
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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
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Reflectance Band RatioUses Ratio e.g. NIR/GreenEstablishes non-linear fit based on modeled form of relationship
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Simulated relationship between concentration and reflectance ratio
From Doxaran, 2002
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Observed relationship between concentration and reflectance ratio
From Doxaran, 2002
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Calculating from IOPs
)()()()(λλ
λλb
brs
babCR+
×≈
Calculate a, b using existing models
Perform linear fit to backscatter data to estimate suspended matter concentration
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Example – Maumee River
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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
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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
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Used the Lake Erie Center boat to validate the satellite imagery
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Port Clinton: 6/8/2006 Port Clinton: 6/24/2006
(Silt Washout)
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Turbidity Index
0
37
74
111
148
(NTU)
July 15, 1999Images by Todd Kunselman, Clarion University
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Turbidity Index
0
37
74
111
148
(NTU)
Sept. 17, 1999
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Turbidity Index
0
37
74
111
148
(NTU)
Nov. 4, 1999
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Turbidity Index
0
37
74
111
148
(NTU)
March 14, 2001
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Turbidity Index
0
37
74
111
148
(NTU)
July 7, 2002
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Turbidity Index
0
37
74
111
148
(NTU)
August 8, 2002
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ExamplesPavelsky, 2009– Athabasca and Peace River, CA– SPOT– ASTER– MODIS
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Sediment Reflectance Relationship from SPOT and ASTER
From Pavelsky, 2009
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Derived Sediment Concentrations from SPOT total reflectance Correlation
From Pavelsky, 2009
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ExamplesWang et Al 2007, 2009– Yangzee River– Landsat ETM
Wang et Al, 2010– Yangzee River– MODIS 250 m
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Landsat Derived Concentrations from ETM+: Upper Yangtze
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Landsat Derived Concentrations from ETM+: Middle Yangtze
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MODIS derived
sediment concentrations vs observed
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Questions?
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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.