Accurate correction of the corrupting effects of the atmosphere and the water’s surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi- and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio–Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges.

Sensor capability and atmospheric correction in ocean colour remote sensing

Assessment of estuarine water-quality indicators using MODIS medium-resolution bands: initial results from Tampa Bay, FL

Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean

A spectrum-matching and look-up-table (LUT) methodology has been developed and evaluated to extract environmental information from remotely sensed hyperspectral imagery. The LUT methodology works as follows. First, a database of remote-sensing reflectance (Rrs) spectra corresponding to various water depths, bottom reflectance spectra, and water-column inherent optical properties (IOPs) is constructed using a special version of the HydroLight radiative transfer numerical model. Second, the measured Rrs spectrum for a particular image pixel is compared with each spectrum in the database, and the closest match to the image spectrum is found using a least-squares minimization. The environmental conditions in nature are then assumed to be the same as the input conditions that generated the closest matching HydroLight-generated database spectrum. The LUT methodology has been evaluated by application to an Ocean Portable Hyperspectral Imaging Low-Light Spectrometer image acquired near Lee Stocking Island, Bahamas, on 17 May 2000. The LUT-retrieved bottom depths were on average within 5% and 0.5 m of independently obtained acoustic depths. The LUT-retrieved bottom classification was in qualitative agreement with diver and video spot classification of bottom types, and the LUT-retrieved IOPs were consistent with IOPs measured at nearby times and locations.

/pdf/interpretation-of-hyperspectral-remote-sensing-imagery-by-53mjlgbhvo.pdf

Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables

The retrieval of ocean color radiometry from space-based sensors requires on-orbit vicarious calibration to achieve the level of accuracy desired for quantitative oceanographic applications. The approach developed by the NASA Ocean Biology Processing Group (OBPG) adjusts the integrated instrument and atmospheric correction system to retrieve normalized water-leaving radiances that are in agreement with ground truth measurements. The method is independent of the satellite sensor or the source of the ground truth data, but it is specific to the atmospheric correction algorithm. The OBPG vicarious calibration approach is described in detail, and results are presented for the operational calibration of SeaWiFS using data from the Marine Optical Buoy (MOBY) and observations of clear-water sites in the South Pacific and southern Indian Ocean. It is shown that the vicarious calibration allows SeaWiFS to reproduce the MOBY radiances and achieve good agreement with radiometric and chlorophyll a measurements from independent in situ sources. We also find that the derived vicarious gains show no significant temporal or geometric dependencies, and that the mission-average calibration reaches stability after approximately 20-40 high-quality calibration samples. Finally, we demonstrate that the performance of the vicariously calibrated retrieval system is relatively insensitive to the assumptions inherent in our approach.

/pdf/sensor-independent-approach-to-the-vicarious-calibration-of-2diu5krajq.pdf

Sensor-independent approach to the vicarious calibration of satellite ocean color radiometry

Existing atmospheric correction algorithms for multichannel remote
sensing of ocean color from space were designed for retrieving
water-leaving radiances in the visible over clear deep ocean areas and
cannot easily be modified for retrievals over turbid coastal
waters. We have developed an atmospheric correction algorithm for
hyperspectral remote sensing of ocean color with the near-future
Coastal Ocean Imaging Spectrometer. The algorithm uses lookup
tables generated with a vector radiative transfer code. Aerosol
parameters are determined by a spectrum-matching technique that uses
channels located at wavelengths longer than 0.86 µm. The
aerosol information is extracted back to the visible based on aerosol
models during the retrieval of water-leaving radiances. Quite
reasonable water-leaving radiances have been obtained when our
algorithm was applied to process hyperspectral imaging data acquired
with an airborne imaging spectrometer.

/pdf/atmospheric-correction-algorithm-for-hyperspectral-remote-4i9fdv2gqg.pdf

Atmospheric correction algorithm for hyperspectral remote sensing of ocean color from space

Multi-channel remote sensing of ocean color from space has a rich history -- from the past CZCS, to the present SeaWiFS, and to the near-future MODIS. The atmospheric correction algorithms for processing remotely sensed data from these sensors were mainly developed by Howard Gordon at University of Miami. The algorithms were primarily designed for retrieving water leaving radiances in the visible spectral region over clear deep ocean areas. The information about atmospheric aerosols is derived from channels between 0.66 and 0.87 micrometer, where the water leaving radiances are close to zero. The derived aerosol information is extrapolated back to the visible when retrieving water leaving radiances from remotely sensed data. For the turbid coastal environment, the water leaving radiances for channels between 0.66 and 0.87 micrometer may not be close to zero because of back scattering by suspended materials in the water. Under these conditions, the channels are no longer useful for deriving information on atmospheric aerosols. As a result, the algorithms developed for applications to clear ocean waters cannot be easily modified to retrieve water leaving radiances from remote sensing data acquired over the costal environments. We have recently developed a fast and fully functional atmospheric correction algorithm for hyperspectral remote sensing of ocean color with the Coastal Ocean Imaging Spectrometer (COIS). Our algorithm uses lookup tables generated with a vector radiative transfer code developed by Ahmad and Fraser (1982) and a spectral matching technique for the retrieval of water leaving radiances. The information on atmospheric aerosols is estimated using dark channels beyond 0.86 micron. Quite reasonable results were obtained when applying the algorithm to process spectral imaging data acquired over Chesapeake Bay with the NASA JPL Airborne Visible Infrared Imaging Spectrometer (AVIRIS).© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Ziauddin Ahmad

Papers

Atmospheric correction algorithm for hyperspectral remote sensing of ocean color from space

Atmospheric correction algorithm based on vector radiative transfer modeling for hyperspectral remote sensing of ocean color