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Chapter 7

Chapter 7. Atmospheric correction and ocean color algorithm Remote Sensing of Ocean Color Instructor: Dr. Cheng-Chien Liu Department of Earth Sciences National Cheng-Kung University Last updated: 24 April 2003. 7.1 Introduction.

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Chapter 7

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  1. Chapter 7 Atmospheric correction and ocean color algorithm Remote Sensing of Ocean Color Instructor: Dr. Cheng-Chien Liu Department of Earth Sciences National Cheng-Kung University Last updated: 24 April 2003

  2. 7.1 Introduction • The feasibility of satellite ocean color rests on two observations obtained from CZCS experiences (Gordon 1997) • Atmospheric correction • It is possible to develop algorithms to remove the interfering effects of the atmosphere and the sea surface from the imagery • In-water bio-optical algorithm • There exists a more or less universal relationship between the color of the ocean and the phytoplankton pigment concentration for most open ocean waters

  3. 7.1 Introduction (cont.) • One key element of the CZCS mission • The CZCS Nimbus Experiment Team (NET) • A group of optical physicists and biological oceanographers • The CZCS NET was designated to validate the accuracy of the CZCS radiometric measurements, and to connect the instrument’s measurements to standard measures of oceanic biological productivity and optical seawater clarity • Experiences of in-water calibration, validation, and algorithm development acquired in this program provided an important guideline for designing and planning follow-on sensors • A retrospective on this program was documented by Acker (1994)

  4. 7.2 Atmospheric correction • Definition • The retrieval of the water-leaving radiance from the satellite-observed radiance is called atmospheric correction (Fukushima 1998) • Optical pathways

  5. Fig 2.1.1 Fig. 2.1.1 Illustration of all possible pathways for light rays which eventually reach the sensor; reprint from (http://satori.gso.uri.edu/satlab/phyto/), which is redrawn from (Robinson 1994). Note that the link doesn’t exist anymore.

  6. 7.2 Atmospheric correction (cont.) • Relation: • Ls=L* +TLr+TLw • Ls: The radiance signal that reaches the sensor • L*: The atmosphere path radiance. (h), (i), (j), and (k) all contribute to L* • Lr: The surface reflective radiance within the IFOV. (d) and (e) contribute to Lr • Lw: The water-leaving radiance • T: The direct transmittance of the atmosphere

  7. 7.2 Atmospheric correction (cont.) • Regroup • By simulating typical atmospheres and running large Monte-Carlo modeling programs, Gordon was able to neglect sun glitter and to regroup Ls as • Ls=LR+LA+T'Lw • LR: The atmosphere radiance due to Rayleigh scattering • LA: The atmosphere radiance due to aerosol particle scattering • T': The diffuse transmittance

  8. Fig 7.2.1 Fig. 7.2.1 Typical reflectance spectra for case 1 waters. The way the spectrum changes with increasing chlorophyll concentration is indicated by the arrow and the clear-water spectrum is shown in dashed line. Redrawn from (http://daac.gsfc.nasa.gov/CAMPAIGN_DOCS/OCDST/ocdst_measurement_technique.html). A similar figure can be found as Figure 6.19 in Robinson (1994).

  9. 7.2 Atmospheric correction (cont.) • Reflectance spectra for case 1 waters • Rmin = R(660), regardless of the increase of chlorophyll concentration • Assuming: • Portions of scene are clear water regions • LW(670) = 0

  10. 7.2 Atmospheric correction (cont.) • LR(l) • Apply a linearized approximation to a single-scattering Rayleigh correction  LR(l) • LA(670) LA(l) • LA(670) = LS(670) - LR(670) • Multi-spectral approach: LA(670) LA(l) • LW(l) • LW(l) = [LS(l)-LR(l)-LA(l)] / T'

  11. 7.3 In-water bio-optical algorithm • Definition: • A reliable algorithm that relates LW to the concentrations of various constituents in water • Reflectance spectra for case 1 waters • R(550) does not vary very much with Chl • R(443) and R(520) indeed co-vary with Chl • Hinge point • Therefore, a ‘hinge point’ can be selected near 550nm, and the spectral ratio can be related to Chl

  12. 7.3 In-water bio-optical algorithm (cont.) • Regression analysis • Sea truth • The CZCS NET conducted a series of pre- and post-launch ocean optical survey cruises to collect the sea truth • CZCS algorithm • Based on the regression analysis on a total of 49 data points for both case 1 and case 2 waters

  13. Fig 7.3.1 Fig. 7.3.1 CZCS NET pigment algorithm plots, where R(ij) represents the spectral ratio of CZCS band i and band j. Reprinted from (Gordon 1983).

  14. Fig 7.3.2 Fig. 7.3.2 CZCS Optical ArrangementSource: http://daac.gsfc.nasa.gov/SENSOR_DOCS/CZCS_Sensor.html

  15. 7.4 Improvements of SeaWiFS • Atmospheric correction • Gordon and Wang (1994) • Based on the work with CZCS atmosphere correction, they took a further step to consider (1) pixel-wise aerosol-type variability, and (2) multiple scattering that involves aerosol. • For the pixel-wise atmospheric correction process, a set of ten aerosol models was used in their algorithm to synthesize the aerosol reflectance. • First, they selected a pair of aerosol models that best explained the observed spectral reflectances in the two near infrared bands (670nm, 865nm). • Then an average of the reflectances of the selected pair of aerosol models was taken to estimate the aerosol reflectance in the visible bands. • Ding and Gordon (1995) • They proposed a simple method for the O2 correction. • Because the assumption of zero water-leaving radiance at 670nm might cause significant error, especially in case 2 waters, they suggested an alternative approach to use 765 and 865nm by correcting the oxygen absorption effect in the 765nm band.

  16. 7.4 Improvements of SeaWiFS (cont.) • Atmospheric correction (cont.) • Chomko and Gordon (1998) • They replaced the aerosol models in (Gordon and Wang 1994) with a Junge power-law aerosol size distribution, which enabled the utilisation of a variable refractive index to handle aerosol absorption. • Moore et al. (1999) • To avoid applying the assumption of zero water-leaving radiance at any specific spectral wave bands, they employed a coupled hydrological atmospheric model to solve the water-leaving radiance and atmospheric path radiance in the near-infrared over case 2 turbid waters. • Ruddick et al. (2000) • They replaced the assumption of zero water-leaving radiances for the near-infrared bands with other assumptions of spatial homogeneity of the 765:865nm ratios for aerosol reflectance and for water-leaving reflectance.

  17. 7.4 Improvements of SeaWiFS (cont.) • Atmospheric correction (cont.) • Gao et al. (2000) • They derived the aerosol parameters by a spectrum-matching technique that used channels located at wavelengths longer than 860nm. • Note • It should be noted that all these works were mainly focused on refining the atmospheric correction algorithm to retrieve the information of case 2 waters. There is no change in correcting the atmospheric effect over case 1 waters.

  18. 7.4 Improvements of SeaWiFS (cont.) • In-water bio-optical algorithm • SeaBASS • The SeaWiFS Bio-Optical Archive and Storage System • Provide an interface to the Project’s holdings of bio-optical and laboratory instrument calibration data [Hooker, 1994 #175] • SeaBAM • The Bio-Optical Algorithm Mini-Workshop group • It was developed out of informal meetings conducted during the Halifax Ocean Optics XIII conference (21–25 October 1996). • The main purpose  finalize the operational SeaWiFS chlorophyll-a algorithm • To meet SeaWiFS accuracy goals (35% accuracy over range of 0.05-50 mg/m3; see (Hooker 1992) • Several candidates, including empirical and semi-analytical algorithms, were evaluated by the SeaBAM (O'Reilly, 1998 #239)

  19. 7.4 Improvements of SeaWiFS (cont.) • In-water bio-optical algorithm (cont.) • OC2-v1, OC4-v1 (1997) • Recommendation from SeaBAM (919 observations) • Problems • it overestimated concentrations above concentrations of around 2mg/m3. This was because the derivation of the OC-2 algorithm was based on a large bio-optical data set and the primary limitation of that data set was the number of stations with chlorophyll values above 5mg/m3.

  20. 7.4 Improvements of SeaWiFS (cont.) • In-water bio-optical algorithm (cont.) • OC2-v2, OC4-v2 (1998) • The OC-2 algorithm was revised after about 300 additional stations (1174 in situ observations) were added to the data set  the 2nd data reprocessing • Problems • problems were continuously reported and its applicability was challenged. For example, Kahru and Mitchell (1999) applied the OC-v2 algorithm to their California Cooperative Oceanic Fisheries Investigations (CalCOFI) dataset of 348 bio-optical measurements. They found that the OC-v2 performs better at high chlorophyll (>15 mg m-3) but underestimates at intermediate concentration (1–10 mg m-3). The rms error of OC2-v2 actually increased compared to OC2-v1 when applied to their dataset.

  21. 7.4 Improvements of SeaWiFS (cont.) • In-water bio-optical algorithm (cont.) • OC2-v4, OC4-v4 (2000) • SeaWiFS Project Postlaunch Technical Report Series (http://seawifs.gsfc.nasa.gov/cgi/postlaunch_tech_memo.pl?11) • 2853 in situ dataset (Table 7.4.1) • The relative frequency distribution of Chl concentration in the in situ data (Fig. 7.4.1) • Problems?!!!

  22. Fig7.4.1 Fig. 7.4.1 The data sets and the investigators responsible for the data collection activitySource: http://seawifs.gsfc.nasa.gov/cgi/postlaunch_tech_memo.pl?11

  23. Fig 7.4.2 Fig. 7.4.2 The relative frequency distribution of Chl concentration in the in situ dataSource: http://seawifs.gsfc.nasa.gov/cgi/postlaunch_tech_memo.pl?11

  24. Fig 7.4.3 Fig. 7.4.3 The relationship between band ratios and chlorophyll concentrationSource: http://seawifs.gsfc.nasa.gov/cgi/postlaunch_tech_memo.pl?11

  25. Fig 7.4.4 Fig. 7.4.4 Validation of OC2-v4 modelSource: http://seawifs.gsfc.nasa.gov/cgi/postlaunch_tech_memo.pl?11

  26. Fig 7.4.5 Fig. 7.4.4 Validation of OC4-v4 modelSource: http://seawifs.gsfc.nasa.gov/cgi/postlaunch_tech_memo.pl?11

  27. 7.5 Improvements of MODIS • Latest progresses of this section • Atmospheric correction • A presentation at Remote Sensing of the Earth's Environment from Terra, a Workshop at the International Summer School on Atmospheric and Oceanic Science, L'Aquila, Italy, August 25-30, 2002 http://modis-ocean.gsfc.nasa.gov/refs/Evans.Laquila.8.26.02.ppt • Algorithm Theoretical Basis Documents (ATBDs) 17:Normalized Water Leaving Radiance (Gordon and Voss 1999)http://modis.gsfc.nasa.gov/data/atbd/atbd_mod17.pdf

  28. 7.5 Improvements of MODIS (cont.) • Latest progresses of this section (cont.) • In-water bio-optical algorithm • A presentation at MODIS Ocean Data Workshop, University of New Hampshire, Durham, NH, February 3-4, 2003 http://modis-ocean.gsfc.nasa.gov/refs/UNH.2.03.Carder.ppt • Algorithm Theoretical Basis Documents (ATBDs) 19:Case 2 Chlorophyll_a Algorithm and Case 2 Absorption Coefficient Algorithm (Carder et al 2003)http://modis.gsfc.nasa.gov/data/atbd/atbd_mod19.pdf

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