Atmospheric Correction for Coastal and Inland Waters Current Capabilities and Challenges
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1 Atmospheric Correction for Coastal and Inland Waters Current Capabilities and Challenges Nima Pahlevan Research Scientist NASA Goddard Space Flight Center Science Systems and Applications Inc.
2 Outline In situ validations Challenges and issues Aerosols Absorbing waters Extremely turbid waters Calibration errors Trace gases Adjacency effects Sunglint Cloud shadows & wave facets
![Validations using AERONET-OC data Landsat-8 (OLI) Sentinel-2A (MSI) RMSD (1/sr) 0.0009 0.0008 0.0007 0.0004 R rs (Landsat-8) [1/sr] Pahlevan, N., Schott, J.R., Franz, B.](/docs-images/74/69869888/images/3-1.jpg "A., Zibordi, G., Markham, B., Bailey, S., Schaaf, C.B., Ondrusek, M., Greb, S., & Strait, C.M. (2017).")
3 Validations using AERONET-OC data Landsat-8 (OLI) Sentinel-2A (MSI) RMSD (1/sr) R rs (Landsat-8) [1/sr] Pahlevan, N., Schott, J.R., Franz, B.A., Zibordi, G., Markham, B., Bailey, S., Schaaf, C.B., Ondrusek, M., Greb, S., & Strait, C.M. (2017). Landsat 8 remote sensing reflectance (R rs) products: Evaluations, intercomparisons, and enhancements. Remote Sensing of Environment, 190, Pahlevan, N., Sarkar, S., Franz, B. A., He, J. Sentinel-2 MultiSpectral Instrument (MSI) data processing for aquatic science applications: Demonstrations and preliminary validations. Submitted to Remote Sensing of Environment
4 Validations using AERONET-OC data By adopting heritage A/C method, on average, we are doing great!
5 Validations using AERONET-OC data By adopting heritage A/C method, on average, we are doing great! But How well do we do retrievals per-pixel? (maximize number of valid retrievals)
6 Validations using AERONET-OC data Landsat-8 (OLI) Sentinel-2A (MSI) RMSD (1/sr) R rs (Landsat-8) [1/sr] Pahlevan, N., Schott, J.R., Franz, B.A., Zibordi, G., Markham, B., Bailey, S., Schaaf, C.B., Ondrusek, M., Greb, S., & Strait, C.M. (2017). Landsat 8 remote sensing reflectance (R rs) products: Evaluations, intercomparisons, and enhancements. Remote Sensing of Environment, 190, Pahlevan, N., Sarkar, S., Franz, B. A., He, J. Sentinel-2 MultiSpectral Instrument (MSI) data processing for aquatic science applications: Demonstrations and preliminary validations. Submitted to Remote Sensing of Environment
7 Validations using AERONET-OC: Necessary but NOT sufficient By adopting heritage A/C method, on average, we are doing great! But How well do we do over areas NOT represented within AERONET-OC network?
8 Issues with aerosol removal (absorbing waters): representativeness Landsat-8 derived R rs (443) over Wachusett Reservoir in Massachusetts Automated removal of aerosols using existing aerosol LUTs
9 Issues with aerosol removal (absorbing waters): representativeness Landsat-8 derived R rs (443) over Wachusett Reservoir in Massachusetts Automated removal of aerosols using existing aerosol LUTs Manual removal of aerosols using observed AOT spectra
10 Issues with aerosol removal: representativeness Single Scattering Albedo SSA M1 M2 M3 M4 M5 M6 M7 M11 M12 M13 M14 M15 M16 Standard aerosol models (Ahmad et al. 2010) Observed/retrieved aerosol properties (coastal AERONET) Wavelength (nm) Pahlevan, N., Roger, J.-C., & Ahmad, Z. (2017). Revisiting short-wave-infrared (SWIR) bands for atmospheric correction in coastal waters. Optics express, 25,
11 The standard aerosol models do NOT represent aerosols over inland and nearshore coastal areas
12 extremely turbid pixel Belgian coastal zone / Zeebrugge Landsat-8/OLI TOA Rayleigh-corrected water significant NIR signal Good A/C with SWIR bands Credit: Quinten Vanhellemont
13 Calibration errors Use VIIRS NIR/SWIR band combinations to simulate the sensitivity of aerosol removal to calibration errors. The values shown are averaged for > 250 VIIRS observations. Absolute percentage difference in R rs (443) Pahlevan, N., Roger, J.-C., & Ahmad, Z. (2017). Revisiting short-wave-infrared (SWIR) bands for atmospheric correction in coastal waters. Optics express, 25,
14 Monitor calibration performances radiances) more frequently to identify any short-term changes in responses within NIR/SWIR
15 Issues with removal of trace gases: representativeness 1 DU error in NO 2 results in large errors in UV and blue % error Rrs NO2: 0-2 km NO2: 0-3 km SZA (a) Wavelength (nm) (b) Wavelength (nm) Tzortziou, M., Herman, J.R., Ahmad, Z., Loughner, C.P., Abuhassan, N., & Cede, A. (2014). Atmospheric NO2 dynamics and impact on ocean color retrievals in urban nearshore regions. Journal of Geophysical Research: Oceans, 119,
16 Issues with removal of trace gases: representativeness Busan, 18 May 2016 NO2 (DU) DU Ground based BUSAN Shipboard Series Local time Currently, we use climatology or coarseresolution ancillary data to correct for the effects of trace gases Credit: Maria Tzortziou (KORUS OC field campaign)
17 Adjacency effects (ice) Southwest Greenland (June 2 nd 2014) Landsat-8 (OLI) with ~ 30m spatial sampling
18 Adjacency effects (ice) Southwest Greenland (June 2 nd 2014) 8 km In collaboration with Clémence Goyens & Simon Bélanger
19 Adjacency effects (ice) Mackenzie River Delta (June 24 th 2016) Landsat-8 (OLI)
20 Adjacency effects (ice)
21 Adjacency effects (land)
22 Adjacency effects (land) Landsat example(s) NDVI calculated using Rayleigh-corrected radiance Warmer colors indicate impacts of adjacency effects km
23 Note how adjacency effects vary from time to time. Dependency on environmental conditions & solar angles is clearly observed.
24 Adjacency effects (man-made structures) Landsat 8/OLI image of CPower wind farm & OTS platform (AERONET-OC station) Vanhellemont Q. & Ruddick K. (2015b) Assessment of Sentinel-3/OLCI sub-pixel variability and platform impact using Landsat-8/OLI Submitted for the proceedings of the Sentinel-3 for Science Workshop, ESA Special Publication SP-734
25 Adjacency effects (man-made structures) CPower Windfarm Belgian Coastal Zone OLCI TOA radiance 865nm S2A/MSI Ship Anchorage S2A/MSI (presented by Héloïse Lavigne at S3VT)
26 Sunglint & impact of instrument design Examples from Landsat-8/Sentinel-2 Track direction Landsat-8 (OLI) focal plane Line-of-sight odd: forward looking) Track direction Line-of-sight (even: backward looking) Optical axis ( nadir viewing ) ~6400 detectors (~ 185km) less glint Optical axis (nadir) more glint
27 Sunglint: Near-simultaneous Landsat-8/Sentinel-2 Images Landsat-8 Sentinel-2A
28 Southern Italy & Malta Sentinel-2A (MSI) SZA ~ 22 o Time ~ 10:00 View zenith angle ~ 11 o
29 Southern Italy & Malta Sentinel-2A (MSI) SZA ~ 22 o Time ~ 10:00 View zenith angle ~ 8 o Landsat-8 (OLI) SZA ~ 26.2 o Time ~ 9:35 GMT
30 Chesapeake Bay Sentinel-2A (MSI) SZA ~ 22 Time ~ 16:00 GMT
31 Chesapeake Bay Sentinel-2A (MSI) SZA ~ 22 Time ~ 16:00 GMT Landsat-8 (OLI) SZA ~ 25 o Time ~ 15:35 GMT
32 Cloud shadows & wave facets
33 Belgian coastal zone / Zeebrugge Sentinel-2A/MSI Wave facets, breaking waves resolved -> SPM product cloud shadow breaking waves cloud wave facets! breaking waves «SPM» Credit: Q. Vanhellemont
34 Sentinel-2A/MSI Bright/dark wave facets: 10-30% difference in ρw (+ timing/view differences across bands) (spring bloom) Credit: Q. Vanhellemont
35 Let s discuss all these issues
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