Accuracy assessment of input parameters for water column correction approach for case 2 waters

Numerous approaches characterising the radiation field of a water column have been developed and correction attempts for remote sensing data have been applied successfully. Various algorithms describe the complex interaction of biophysical parameters with down- and upwelling radiation in a water body and form the basis for water column correction. Parameters such as varying bottom reflectances and bathymetry aggravate an accurate parameterization of water column correction models. Applying these models, special interest lies in their sensitivity to both quality and accuracy of model input parameters. In this paper we discuss the sensitivity of the water column correction model MIP2 to bio-physical parameters, i.e. suspended matters (SM) and chlorophyll (CHL), in case 2 waters. In August 2010, hyperspectral AISAeagle data have been acquired; in-situ measurements were conducted concurrently to the airborne campaign. The study was conducted at the rocky shores of the island Helgoland (North Sea, Germany). The study area is characterised by a heterogeneous water body resulting in varying and spatially uncorrelated concentrations of SM and CHL, which aggravate an accurate water column correction. During analysis, special focus is set on areas with varying water characteristics such as vegetated bedrock, shallow sandy spots and deep water areas. Water column correction is performed using a sub-module of MIP, i.e. WATCOR. Reflectance deviation results show that variations of SM concentrations have a stronger influence than variations of CHL within the water column correction. Whereas, the shallow sandy spots reveal the highest sensitivity at constituent concentration variation followed by the deep water and the vegetated bedrock areas.

[1]  J. Dungan,et al.  The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration , 1991 .

[2]  Thomas Heege,et al.  Mapping Aquatic Systems with a Physically Based Process Chain , 2004 .

[3]  T. Schröder Fernerkundung von Wasserinhaltsstoffen in Küstengewässern mit MERIS unter Anwendung expliziter und impliziter Atmosphärenkorrekturverfahren , 2005 .

[4]  André Morel,et al.  Diffuse reflectance of oceanic shallow waters: influence of water depth and bottom albedo , 1994 .

[5]  Georg Martin,et al.  Feasibility of hyperspectral remote sensing for mapping benthic macroalgal cover in turbid coastal waters—a Baltic Sea case study , 2006 .

[6]  C. Mobley,et al.  An analytical model for subsurface irradiance and remote sensing reflectance in deep and shallow case-2 waters. , 2003, Optics express.

[7]  M. Harvey Development of techniques to classify marine benthic habitats using hyperspectral imagery in oligotrophic, temperate waters , 2009 .

[8]  T. Heege,et al.  Mapping the shallow marine benthic habitats of Rottnest Island, Western Australia , 2007 .

[9]  John M. Melack,et al.  Remote sensing of aquatic vegetation: theory and applications , 2008, Environmental monitoring and assessment.

[10]  L. Prieur,et al.  Analysis of variations in ocean color1 , 1977 .

[11]  Joanna Staneva,et al.  Tidal and wind-driven surface currents in the German Bight: HFR observations versus model simulations , 2011 .

[12]  Inka Bartsch,et al.  The rocky intertidal biotopes of Helgoland: present and past , 2004, Helgoland Marine Research.

[13]  L. Prieur,et al.  Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains1 , 1981 .

[14]  P. Curran,et al.  The effect of sediment type on the relationship between reflectance and suspended sediment concentration , 1989 .

[15]  Nicole Pinnel,et al.  A method for mapping submerged macrophytes inlakes using hyperspectral remote sensing , 2007 .

[16]  T. Heege,et al.  High-resolution satellite remote sensing of littoral vegetation of Lake Sevan (Armenia) as a basis for monitoring and assessment , 2011, Hydrobiologia.

[17]  Wojciech M. Klonowski,et al.  Intercomparison of shallow water bathymetry, hydro‐optics, and benthos mapping techniques in Australian and Caribbean coastal environments , 2011 .

[18]  Antonino Maltese,et al.  The classification of submerged vegetation using hyperspectral MIVIS data , 2006 .

[19]  Thomas Heege,et al.  Bathymetry mapping and sea floor classification using multispectral satellite data and standardized physics-based data processing , 2011, Remote Sensing.

[20]  C. Mobley Light and Water: Radiative Transfer in Natural Waters , 1994 .