Engineering topics

Campbell, Janet W.

Publications and source records attributed to Campbell, Janet W..

6 records

NASA NTRS2001

Mixed Layer Drift Revealed by Satellite Data

For the first time we are able to derive ocean currents using the wavelet algorithm for feature tracking from two different sensors (MODIS and SeaWiFS) on different satellites. Satellite ocean color data provide an important insight to the marine biosphere because of their capability to quantify certain fundamental properties (such as phytoplankton pigment concentration, marine primary production, etc.) on a global basis. The mixed layer drift can be derived because the ocean color signal bears information from a much larger depth (10 to 30 meters) as compared with the sea surface temperature data. Although the drifter data are very limited in the study area, the comparison shows a general agreement between drifter data and satellite tracking results, especially for the cases near the Gulf Stream boundary.

Liu, Antony K.↗

NASA NTRS1998

Fuzzy Classification of Ocean Color Satellite Data for Bio-optical Algorithm Constituent Retrievals

The ocean has been traditionally viewed as a 2 class system. Morel and Prieur (1977) classified ocean water according to the dominant absorbent particle suspended in the water column. Case 1 is described as having a high concentration of phytoplankton (and detritus) relative to other particles. Conversely, case 2 is described as having inorganic particles such as suspended sediments in high concentrations. Little work has gone into the problem of mixing bio-optical models for these different water types. An approach is put forth here to blend bio-optical algorithms based on a fuzzy classification scheme. This scheme involves two procedures. First, a clustering procedure identifies classes and builds class statistics from in-situ optical measurements. Next, a classification procedure assigns satellite pixels partial memberships to these classes based on their ocean color reflectance signature. These membership assignments can be used as the basis for a weighting retrievals from class-specific bio-optical algorithms. This technique is demonstrated with in-situ optical measurements and an image from the SeaWiFS ocean color satellite.

Campbell, Janet W.↗

NASA NTRS1998

SeaWiFS Technical Report Series: Satellite Primary Productivity Data and Algorithm Development: A Science Plan for Mission to Planet Earth - Volume 42

Two issues regarding primary productivity, as it pertains to the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Program and the National Aeronautics and Space Administration (NASA) Mission to Planet Earth (MTPE) are presented in this volume. Chapter 1 describes the development of a science plan for deriving primary production for the world ocean using satellite measurements, by the Ocean Primary Productivity Working Group (OPPWG). Chapter 2 presents discussions by the same group, of algorithm classification, algorithm parameterization and data availability, algorithm testing and validation, and the benefits of a consensus primary productivity algorithm.

Falkowski, Paul G.↗

NASA NTRS1995

SeaWiFS technical report series. Volume 32: Level-3 SeaWiFS data products. Spatial and temporal binning algorithms

The level-3 data products from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) are statistical data sets derived from level-2 data. Each data set will be based on a fixed global grid of equal-area bins that are approximately 9 x 9 sq km. Statistics available for each bin include the sum and sum of squares of the natural logarithm of derived level-2 geophysical variables where sums are accumulated over a binning period. Operationally, products with binning periods of 1 day, 8 days, 1 month, and 1 year will be produced and archived. From these accumulated values and for each bin, estimates of the mean, standard deviation, median, and mode may be derived for each geophysical variable. This report contains two major parts: the first (Section 2) is intended as a users' guide for level-3 SeaWiFS data products. It contains an overview of level-0 to level-3 data processing, a discussion of important statistical considerations when using level-3 data, and details of how to use the level-3 data. The second part (Section 3) presents a comparative statistical study of several binning algorithms based on CZCS and moored fluorometer data. The operational binning algorithms were selected based on the results of this study.

Hooker, Stanford B.↗

NASA NTRS1995

The lognormal distribution as a model for bio-optical variability in the sea

The lognormal distribution is presented as a useful model for bio-optical variability at a variety of spatial and temporal scales. A parametric statistical framework is presented for using the lognormal model to assess the effects of heterogeneity and scale on closure. Variability at small scales affects but is unresolved by large-scale measurements. An assumed lognormal distribution allows one to integrate over small-scale variability to predict large-scale measurements. Examples are presented to demonstrate how knowledge of the variance can be incorporated into models to relate measurements made at different scales.

Campbell, Janet W.↗

NASA NTRS1989

Temporal patterns of phytoplankton abundance in the North Atlantic

A time series of CZCS images is being developed to study phytoplankton distribution patterns in the North Atlantic. The goal of this study is to observe temporal variability in phytoplankton pigments and other organic particulates, and to infer from these patterns the potential flux of biogenic materials from the euphotic layer to the deep ocean. Early results of this project are presented in this paper. Specifically, the satellite data used were 13 monthly composited images of CZCS data for the North Atlantic from January 1979 to January 1980. Results are presented for seasonal patterns along the 20 deg W meridian.

Campbell, Janet W.↗