A semianalytical radiance model is developed which predicts the upwelled spectral radiance at the sea surface as a function of the phytoplankton pigment concentration for Morel Case 1 waters. The model is in good agreement with experimental measurements carried out in waters which were not included in the data base used to derive it. It suggests that the observed variability in the radiance is due to variations in the backscattering of plankton and the associated detrital material. The model is extended to include other material in the water, such as dissolved organic material, referred to as yellow substances, and detached coccoliths from coccolithophorids, e.g., Emiliana huxleyi. Potential applications include an improved bio‐optical algorithm for the retrieval of pigment concentrations from satellite imagery in the presence of interference from detached coccoliths and an improved atmospheric correction for satellite imagery. The model also serves to identify and to interpret deviations from Case 1 waters.
The paper is organized as follows. Section 2 describes the evolution of AVHRR SST algorithms that motivated the current formulation of the Pathfinder SST algorithm, introduced in section 3. One of the AVHRR Oceans Pathfinder highlights is that for the first time a large validation database is distributed with the Pathfinder global SST fields to allow interested investigators to develop and validate alternative formulations for the computation of AVHRR-derived SSTs. This database of "matchups" is described in section 4, together with the procedures followed to estimate Pathfinder SST algorithm coefficients. To give potential users of the Pathfinder SST fields a general feel for the performance of the algorithm, we provide an evaluation of the algorithm perfoi'mance in section 5. Section 6 describes the processing steps involved in the generation of global Pathfinder SST fields. The SST fields may be used for very different purposes, from identifying and tracking specific ocean features to conducting climate-related studies. Such different applications may involve tradeoffs between data coverage and quality (e.g., if finding the Gulf Stream is the goal, one may be more tolerant of lower-quality SST estimates as the 9179
The processing algorithms used for relating the apparent color of the ocean observed with the Coastal-Zone Color Scanner on Nimbus-7 to the concentration of phytoplankton pigments (principally the pigment responsible for photosynthesis, chlorophyll a) are developed and discussed in detail. These algorithms are applied to the shelf and slope waters of the Middle Atlantic Bight and also to Sargasso Sea waters. In all, four images are examined, and the resulting pigment concentrations are compared to continuous measurements made along ship tracks. The results suggest that over the 0.08-1.5-mg/m3 range the error in the retrieved pigment concentration is of the order of 30-40% for a variety of atmospheric turbidities. In three direct comparisons between ship-measured and satellite-retrieved values of the water-leaving radiance the atmospheric correction algorithm retrieved the water-leaving radiance with an average error of approximately 10%. This atmospheric correction algorithm does not require any surface measurements for its application.
SST is an ocean variable that is readily measured by satellites and in situ sensors, and it is needed as a key input to forecasting systems to constrain the modeled upperocean circulation and thermal structure, and for the exchange of energy • AMERICAN METEOROLOGICAL SOCIETY
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