Atmospheric correction over case 2 waters with an iterative fitting algorithm

Evolution of Ocean Color Atmospheric Correction: 1970–2005

Retrieval of water properties from satellite-borne imagers viewing oceans and coastal areas in the visible region of the spectrum requires removing the effect of the atmosphere, which contributes approximately 80–90% of the measured radiance over the open ocean in the blue spectral region. The Gordon and Wang algorithm originally developed for SeaWiFS (and used with other NASA sensors, e.g., MODIS) forms the basis for many atmospheric removal (correction) procedures. It was developed for application to imagery obtained over the open ocean (Case 1 waters), where the aerosol is usually non-absorbing, and is used operationally to process global data from SeaWiFS, MODIS and VIIRS. Here, I trace the evolution of this algorithm from early NASA aircraft experiments through the CZCS, OCTS, SeaWiFs, MERIS, and finally the MODIS sensors. Strategies to extend the algorithm to situations where the aerosol is strongly absorbing are examined. Its application to sensors with additional and unique capabilities is sketched. Problems associated with atmospheric correction in coastal waters are described.

Measurement of water colour using AVIRIS imagery to assess the potential for an operational monitoring capability in the Pamlico Sound Estuary, USA

The monitoring of water colour parameters can provide an important diagnostic tool for the assessment of aquatic ecosystem condition. Remote sensing has long been used to effectively monitor chlorophyll concentrations in open ocean systems; however, operational monitoring in coastal and estuarine areas has been limited because of the inherent complexities of coastal systems, and the coarse spectral and spatial resolutions of available satellite systems. Data were collected using the National Aeronautics and Space Administration (NASA) Advanced Visible-Infrared Imaging Spectrometer (AVIRIS) flown at an altitude of approximately 20000 m to provide hyperspectral imagery and simulate both MEdium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectrometer (MODIS) data. AVIRIS data were atmospherically corrected using a radiative transfer modelling approach and analysed using band ratio and linear regression models. Regression analysis was performed with simultaneous field measurements data in the Neuse River Estuary (NRE) and Pamlico Sound on 15 May 2002. Chlorophyll a (Chl a) concentrations were optimally estimated using AVIRIS bands (9.5 nm) centred at 673.6 and 692.7 nm, resulting in a coefficient of determination (R² ) of 0.98. Concentrations of Chromophoric Dissolved Organic Matter (CDOM), Total Suspended Solids (TSS) and Fixed Suspended Solids (FSS) were also estimated, resulting in coefficients of determination of R²=0.90, 0.59 and 0.64, respectively. Ratios of AVIRIS bands centred at or near those corresponding to the MERIS and MODIS sensors indicated that relatively good satellite-based estimates could potentially be derived for water colour constituents at a spatial resolution of 300 and 500 m, respectively.

Remote sensing of phytoplankton chlorophyll-a concentration by use of ridge function fields

A methodology is presented for retrieving phytoplankton chlorophyll-a concentration from space. The data to be inverted, namely, vectors of top-of-atmosphere reflectance in the solar spectrum, are treated as explanatory variables conditioned by angular geometry. This approach leads to a continuum of inverse problems, i.e., a collection of similar inverse problems continuously indexed by the angular variables. The resolution of the continuum of inverse problems is studied from the least-squares viewpoint and yields a solution expressed as a function field over the set of permitted values for the angular variables, i.e., a map defined on that set and valued in a subspace of a function space. The function fields of interest, for reasons of approximation theory, are those valued in nested sequences of subspaces, such as ridge function approximation spaces, the union of which is dense. Ridge function fields constructed on synthetic yet realistic data for case I waters handle well situations of both weakly and strongly absorbing aerosols, and they are robust to noise, showing improvement in accuracy compared with classic inversion techniques. The methodology is applied to actual imagery from the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS); noise in the data are taken into account. The chlorophyll-a concentration obtained with the function field methodology differs from that obtained by use of the standard SeaWiFS algorithm by 15.7% on average. The results empirically validate the underlying hypothesis that the inversion is solved in a least-squares sense. They also show that large levels of noise can be managed if the noise distribution is known or estimated.

Radiometric calibration of SeaWiFS in the near infrared

The radiometric calibration of the Sea-Viewing Wide-Field-of-View Sensor (SeaWiFS) in the near infrared (band 8, centered on 865 nm) is evaluated by use of ground-based radiometer measurements of solar extinction and sky radiance in the Sun's principal plane at two sites, one located 13 km off Venice, Italy, and the other on the west coast of Lanai Island, Hawaii. The aerosol optical thickness determined from solar extinction is used in an iterative scheme to retrieve the pseudo aerosol phase function, i.e., the product of single-scattering albedo and phase function, in which sky radiance is corrected for multiple scattering effects. No assumption about the aerosol model is required. The aerosol parameters are the inputs into a radiation-transfer code used to compute the SeaWiFS radiance. The calibration method has a theoretical inaccuracy of plus or minus 2.0-3.6%, depending on the solar zenith angle and the SeaWiFS geometry. The major source of error is in the calibration of the ground-based radiometer operated in radiance mode, assumed to be accurate to +/- 2%. The establishment of strict criteria for atmospheric stability, angular geometry, and surface conditions resulted in selection of only 26 days for the analysis during 1999-2000 (Venice site) and 1998-2001 (Lanai site). For these days the measured level-1B radiance from the SeaWiFS Project Office was generally lower than the corresponding simulated radiance in band 8 by 7.0% on average, +/- 2.8%.

Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters

The standard SeaWiFS atmospheric correction algorithm, designed for open ocean water, has been extended for use over turbid coastal and inland waters. Failure of the standard algorithm over turbid waters can be attributed to invalid assumptions of zero water-leaving radiance for the near-infrared bands at 765 and 865 nm. In the present study these assumptions are replaced by the assumptions of spatial homogeneity of the 765:865-nm ratios for aerosol reflectance and for water-leaving reflectance. These two ratios are imposed as calibration parameters after inspection of the Rayleigh-corrected reflectance scatterplot. The performance of the new algorithm is demonstrated for imagery of Belgian coastal waters and yields physically realistic water-leaving radiance spectra. A preliminary comparison with in situ radiance spectra for the Dutch Lake Markermeer shows significant improvement over the standard atmospheric correction algorithm. An analysis is made of the sensitivity of results to the choice of calibration parameters, and perspectives for application of the method to other sensors are briefly discussed.

Optical remote sensing of marine constituents in coastal waters: a feasibility study

Optical remote sensing of ocean color is a well-established technique for inferring ocean properties. However, most retrieval algorithms are based on the assumption that the radiance received by satellite instruments is affected only by the phytoplankton pigment concentration and correlated substances. This assumption works well for open ocean water but becomes questionable for coastal waters. To reduce uncertainties associated with this assumption, we developed a new algorithm for the retrieval of marine constituents in a coastal environment. We assumed that ocean color can be adequately described by a three-component model made up of chlorophyll a, suspended matter, and yellow substance. The simultaneous retrieval of these three marine constituents and of the atmospheric aerosol content was accomplished through an inverse-modeling scheme in which the difference between simulated radiances exiting the atmosphere and radiances measured with a satellite sensor was minimized. Simulated radiances were generated with a comprehensive radiative transfer model that is applicable to the coupled atmosphere-ocean system. The method of simulated annealing was used to minimize the difference between measured and simulated radiances. To evaluate the retrieval algorithm, we used simulated (instead of measured) satellite-received radiances that were generated for specified concentrations of aerosols and marine constituents, and we tested the ability of the algorithm to retrieve assumed concentrations. Our results require experimental validation but show that the retrieval of marine constituents in coastal waters is possible.

Atmospheric correction over case 2 waters with an iterative fitting algorithm: relative humidity effects

In algorithms for the atmospheric correction of visible and near-IR satellite observations of the Earth's surface, it is generally assumed that the spectral variation of aerosol optical depth is characterized by an Angström power law or similar dependence. In an iterative fitting algorithm for atmospheric correction of ocean color imagery over case 2 waters, this assumption leads to an inability to retrieve the aerosol type and to the attribution to aerosol spectral variations of spectral effects actually caused by the water contents. An improvement to this algorithm is described in which the spectral variation of optical depth is calculated as a function of aerosol type and relative humidity, and an attempt is made to retrieve the relative humidity in addition to aerosol type. The aerosol is treated as a mixture of aerosol components (e.g., soot), rather than of aerosol types (e.g., urban). We demonstrate the improvement over the previous method by using simulated case 1 and case 2 sea-viewing wide field-of-view sensor data, although the retrieval of relative humidity was not successful.

Remote sensing of ocean color and aerosol properties: resolving the issue of aerosol absorption

Current atmospheric correction and aerosol retrieval algorithms for ocean color sensors use measurements of the top-of-the-atmosphere reflectance in the near infrared, where the contribution from the ocean is known for case 1 waters, to assess the aerosol optical properties. Such measurements are incapable of distinguishing between weakly and strongly absorbing aerosols, and the atmospheric correction and aerosol retrieval algorithms fail if the incorrect absorption properties of the aerosol are assumed. We present an algorithm that appears promising for the retrieval of in-water biophysical properties and aerosol optical properties in atmospheres containing both weakly and strongly absorbing aerosols. By using the entire spectrum available to most ocean color instruments (412-865 nm), we simultaneously recover the ocean's bio-optical properties and a set of aerosol models that best describes the aerosol optical properties. The algorithm is applied to simulated situations that are likely to occur off the U.S. East Coast in summer when the aerosols could be of the locally generated weakly absorbing Maritime type or of the pollution-generated strongly absorbing urban-type transported over the ocean by the winds. The simulations show that the algorithm behaves well in an atmosphere with either weakly or strongly absorbing aerosol. The algorithm successfully identifies absorbing aerosols and provides close values for the aerosol optical thickness. It also provides excellent retrievals of the ocean bio-optical properties. The algorithm uses a bio-optical model of case 1 waters and a set of aerosol models for its operation. The relevant parameters of both the ocean and atmosphere are systematically varied to find the best (in a rms sense) fit to the measured top-of-the-atmosphere spectral reflectance. Examples are provided that show the algorithm's performance in the presence of errors, e.g., error in the contribution from whitecaps and error in radiometric calibration.