Test of the Semi-Analytical Case 1 and Gelbstoff Case 2 SeaWiFS Algorithm with a Global Data Set

The algorithm-development activities at USF during the second half of 1997 have concentrated on data collection and theoretical modeling. Six abstracts were submitted for presentation at the AGU conference in San Diego, California during February 9-13, 1998. Four papers were submitted to JGR and Applied Optics for publication

Algorithm-development activities

The task of algorithm-development activities at USF continues. The algorithm for determining chlorophyll alpha concentration, (Chl alpha) and gelbstoff absorption coefficient for SeaWiFS and MODIS-N radiance data is our current priority

Tests of a Semi-Analytical Case 1 and Gelbstoff Case 2 SeaWiFS Algorithm with a Global Data Set

A semi-analytical algorithm was tested with a total of 733 points of either unpackaged or packaged-pigment data, with corresponding algorithm parameters for each data type. The 'unpackaged' type consisted of data sets that were generally consistent with the Case 1 CZCS algorithm and other well calibrated data sets. The 'packaged' type consisted of data sets apparently containing somewhat more packaged pigments, requiring modification of the absorption parameters of the model consistent with the CalCOFI study area. This resulted in two equally divided data sets. A more thorough scrutiny of these and other data sets using a semianalytical model requires improved knowledge of the phytoplankton and gelbstoff of the specific environment studied. Since the semi-analytical algorithm is dependent upon 4 spectral channels including the 412 nm channel, while most other algorithms are not, a means of testing data sets for consistency was sought. A numerical filter was developed to classify data sets into the above classes. The filter uses reflectance ratios, which can be determined from space. The sensitivity of such numerical filters to measurement resulting from atmospheric correction and sensor noise errors requires further study. The semi-analytical algorithm performed superbly on each of the data sets after classification, resulting in RMS1 errors of 0.107 and 0.121, respectively, for the unpackaged and packaged data-set classes, with little bias and slopes near 1.0. In combination, the RMS1 performance was 0.114. While these numbers appear rather sterling, one must bear in mind what mis-classification does to the results. Using an average or compromise parameterization on the modified global data set yielded an RMS1 error of 0.171, while using the unpackaged parameterization on the global evaluation data set yielded an RMS1 error of 0.284. So, without classification, the algorithm performs better globally using the average parameters than it does using the unpackaged parameters. Finally, the effects of even more extreme pigment packaging must be examined in order to improve algorithm performance at high latitudes. Note, however, that the North Sea and Mississippi River plume studies contributed data to the packaged and unpackaged classess, respectively, with little effect on algorithm performance. This suggests that gelbstoff-rich Case 2 waters do not seriously degrade performance of the semi-analytical algorithm

Vicarious calibration of the Ocean PHILLS hyperspectral sensor using a coastal tree-shadow method

Ocean color remote-sensing systems require highly accurate calibration (<0.5%) for accurate retrieval of water properties. This accuracy is typically achieved by vicarious calibration which is done by comparing the atmospherically corrected remote-sensing data to accurate estimates of the water-leaving radiance. Here we present a new method for vicarious calibration of a hyperspectral sensor that exploits shadows cast by trees and cliffs along coastlines. Hyperspectral Ocean PHILLS imagery was acquired over East Sound and adjacent waters around Orcas Island, Washington, USA, in August, 1998, in concert with field data collection. To vicariously calibrate the PHILLS data, a method was developed employing pixel pairs in tree-shaded and adjacent unshadowed waters, which utilizes the sky radiance dominating the shaded pixel as a known calibration target. Transects extracted from East Sound imagery were calibrated and validated with field data (RMSE = 0.00033 sr⁻¹), providing validation of this approach for acquiring calibration-adjustment data from the image itself.This is the publisher's version of record. The original submission is copyrighted by American Geophysical Union and can be found here: http://www.agu.org

Euphotic zone depth: Its derivation and implication to ocean-color remote sensing

Euphotic zone depth, z[subscript]1%, reflects the depth where photosynthetic available radiation (PAR) is 1% of its surface value. The value of z[subscript]1% is a measure of water clarity, which is an important parameter regarding ecosystems. Based on the Case-1 water assumption, z[subscript]1% can be estimated empirically from the remotely derived concentration of chlorophyll-a ([Chl]), commonly retrieved by employing band ratios of remote sensing reflectance (R[subscript]rs). Recently, a model based on water’s inherent optical properties (IOPs) has been developed to describe the vertical attenuation of visible solar radiation. Since IOPs can be nearanalytically calculated from R[subscript]rs, so too can z[subscript]1%. In this study, for measurements made over three different regions and at different seasons (z[subscript]1% were in a range of 4.3–82.0 m with [Chl] ranging from 0.07 to 49.4 mg/m³), z[subscript]1% calculated from R[subscript]rs was compared with z[subscript]1% from in situ measured PAR profiles. It is found that the z[subscript]1% values calculated via R[supscript]rs-derived IOPs are, on average, within ~14% of the measured values, and similar results were obtained for depths of 10% and 50% of surface PAR. In comparison, however, the error was ~ 33% when z[subscript]1% is calculated via R[subscript]rs-derived [Chl]. Further, the importance of deriving euphotic zone depth from satellite ocean-color remote sensing is discussed.This is the publisher's version of record. The original submission is copyrighted by American Geophysical Union and can be found here: http://www.agu.org

Diffuse attenuation coefficient of downwelling irradiance: An evaluation of remote sensing methods.pdf

The propagation of downwelling irradiance at wavelength l from surface to a depth (z) in the ocean is governed by the diffuse attenuation coefficient, K(λ). There are two standard methods for the derivation of K(λ) in remote sensing, which both are based on empirical relationships involving the blue-to-green ratio of ocean color. Recently, a semianalytical method to derive K(λ) from reflectance has also been developed. In this study, using K(490) and K(443) as examples, we compare the K(λ) values derived from the three methods using data collected in three different regions that cover oceanic and coastal waters, with K(490) ranging from ~0.04 to 4.0 m⁻¹. The derived values are compared with the data calculated from in situ measurements of the vertical profiles of downwelling irradiance. The comparisons show that the two standard methods produced satisfactory estimates of K(λ) in oceanic waters where attenuation is relatively low but resulted in significant errors in coastal waters. The newly developed semianalytical method appears to have no such limitation as it performed well for both oceanic and coastal waters. For all data in this study the average of absolute percentage difference between the in situ measured and the semianalytically derived K is ~14% for λ = 490 nm and ~11% for λ = 443 nm

Model for the interpretation of hyperspectral remote-sensing reflectance

Remote-sensing reflectance is easier to interpret for the open ocean than for coastal regions because the optical signals are highly coupled to the phytoplankton (e.g., chlorophyll) concentrations. For estuarine or coastal waters, variable terrigenous colored dissolved organic matter (CDOM), suspended sediments, and bottom reflectance, all factors that do not covary with the pigment concentration, confound data interpretation. In this research, remote-sensing reflectance models are suggested for coastal waters, to which contributions that are due to bottom reflectance, CDOM fluorescence, and water Raman scattering are included. Through the use of two parameters to model the combination of the backscattering coefficient and the Q factor, excellent agreement was achieved between the measured and modeled remote-sensing reflectance for waters from the West Florida Shelf to the Mississippi River plume. These waters cover a range of chlorophyll of 0.2–40 mg/m³ and gelbstoff absorption at 440 nm from 0.02–0.4 m⁻¹. Data with a spectral resolution of 10 nm or better, which is consistent with that provided by the airborne visible and infrared imaging spectrometer (AVIRIS) and spacecraft spectrometers, were used in the model evaluation

SeaWiFS technical report series. Volume 17: Ocean color in the 21st century. A strategy for a 20-year time series

Beginning with the upcoming launch of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), there should be almost continuous measurements of ocean color for nearly 20 years if all of the presently planned national and international missions are implemented. This data set will present a unique opportunity to understand the coupling of physical and biological processes in the world ocean. The presence of multiple ocean color sensors will allow the eventual development of an ocean color observing system that is both cost effective and scientifically based. This report discusses the issues involved and makes recommendations intended to ensure the maximum scientific return from this unique set of planned ocean color missions. An executive summary is included with this document which briefly discusses the primary issues and suggested actions to be considered

An overview of MODIS capabilities for ocean science observations

The Moderate Resolution Imaging Spectroradiometer (MODIS) will add a significant new capability for investigating the 70% of the Earth's surface that is covered by oceans, in addition to contributing to the continuation of a decadal scale time series necessary for climate change assessment in the oceans. Sensor capabilities of particular importance for improving the accuracy of ocean products include high SNR and high stability for narrow or spectral bands, improved onboard radiometric calibration and stability monitoring, and improved science data product algorithms. Spectral bands for resolving solar-stimulated chlorophyll fluorescence and a split window in the 4-/spl mu/m region for SST will result in important new global ocean science products for biology and physics. MODIS will return full global data at 1-km resolution. The complete suite of Levels 2 and 3 ocean products is reviewed, and many areas where MODIS data are expected to make significant, new contributions to the enhanced understanding of the oceans' role in understanding climate change are discussed. In providing a highly complementary and consistent set of observations of terrestrial, atmospheric, and ocean observations, MODIS data will provide important new information on the interactions between Earth's major components