Son, Young Baek (2003-05). POC algorithms based on spectral remote sensing data and its temporal and spatial variability in the Gulf of Mexico. Doctoral Dissertation. Thesis uri icon

abstract

  • This dissertation consists of three studies dealing with particulate organic carbon (POC). The first study describes the temporal and spatial variability of particulate matter (PM) and POC, and physical processes that affect the distribution of PM and POC with synchronous remote sensing data. The purpose of the second study is to develop POC algorithms in the Gulf of Mexico based on satellite data using numerical methods and to compare POC estimates with spectral radiance. The purpose of the third study is to investigate climatological variations from the temporal and spatial POC estimates based on SeaWiFS spectral radiance and physical processes, and to determine the physical mechanisms that affect the distribution of POC in the Gulf of Mexico. For the first and second studies, hydrographic data from the Northeastern Gulf of Mexico (NEGOM) study were collected on each of 9 cruises from November 1997 to August 2000 across 11 lines. Remotely sensed data sets were obtained from NASA and NOAA using algorithms that have been developed for interpretation of ocean color data from various satellite sensors. For the third study, we use the time-series of POC estimates, sea surface temperature (SST), sea surface height anomaly (SSHA), sea surface wind (SSW), and precipitation rate (PR) that might cause climatological variability and physical processes. The distribution of surface PM and POC concentrations were affected by one or more factors such as river discharge, wind stress, stratification, and the Loop Current/Eddies. To estimate POC concentration, empirical and model-based approaches were used using regression and principal component analysis (PCA) methods. We tested simulated data for reasonable and suitable algorithms in Case 1 and Case 2 waters. Monthly mean values of POC concentrations calculated with PCA algorithms. The spatial and temporal variations of POC and physical forcing data were analyzed with the empirical orthogonal function (EOF) method. The results showed variations in the Gulf of Mexico on both annual and inter-annual time scales.
  • This dissertation consists of three studies dealing with particulate organic carbon
    (POC). The first study describes the temporal and spatial variability of particulate matter
    (PM) and POC, and physical processes that affect the distribution of PM and POC with
    synchronous remote sensing data. The purpose of the second study is to develop POC
    algorithms in the Gulf of Mexico based on satellite data using numerical methods and to
    compare POC estimates with spectral radiance. The purpose of the third study is to
    investigate climatological variations from the temporal and spatial POC estimates based
    on SeaWiFS spectral radiance and physical processes, and to determine the physical
    mechanisms that affect the distribution of POC in the Gulf of Mexico.
    For the first and second studies, hydrographic data from the Northeastern Gulf of
    Mexico (NEGOM) study were collected on each of 9 cruises from November 1997 to
    August 2000 across 11 lines. Remotely sensed data sets were obtained from NASA and
    NOAA using algorithms that have been developed for interpretation of ocean color data
    from various satellite sensors. For the third study, we use the time-series of POC
    estimates, sea surface temperature (SST), sea surface height anomaly (SSHA), sea surface wind (SSW), and precipitation rate (PR) that might cause climatological
    variability and physical processes.
    The distribution of surface PM and POC concentrations were affected by one or
    more factors such as river discharge, wind stress, stratification, and the Loop
    Current/Eddies. To estimate POC concentration, empirical and model-based approaches
    were used using regression and principal component analysis (PCA) methods. We tested
    simulated data for reasonable and suitable algorithms in Case 1 and Case 2 waters.
    Monthly mean values of POC concentrations calculated with PCA algorithms.
    The spatial and temporal variations of POC and physical forcing data were analyzed
    with the empirical orthogonal function (EOF) method. The results showed variations in
    the Gulf of Mexico on both annual and inter-annual time scales.

publication date

  • May 2003