POC algorithms based on spectral remote sensing data and its temporal and spatial variability in the Gulf of Mexico

摘要

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 withsynchronous remote sensing data. The purpose of the second study is to develop POCalgorithms in the Gulf of Mexico based on satellite data using numerical methods and tocompare POC estimates with spectral radiance. The purpose of the third study is toinvestigate climatological variations from the temporal and spatial POC estimates basedon SeaWiFS spectral radiance and physical processes, and to determine the physicalmechanisms that affect the distribution of POC in the Gulf of Mexico.For the first and second studies, hydrographic data from the Northeastern Gulf ofMexico (NEGOM) study were collected on each of 9 cruises from November 1997 toAugust 2000 across 11 lines. Remotely sensed data sets were obtained from NASA andNOAA using algorithms that have been developed for interpretation of ocean color datafrom various satellite sensors. For the third study, we use the time-series of POCestimates, sea surface temperature (SST), sea surface height anomaly (SSHA), sea surface wind (SSW), and precipitation rate (PR) that might cause climatologicalvariability and physical processes.The distribution of surface PM and POC concentrations were affected by one ormore factors such as river discharge, wind stress, stratification, and the LoopCurrent/Eddies. To estimate POC concentration, empirical and model-based approacheswere used using regression and principal component analysis (PCA) methods. We testedsimulated 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 analyzedwith the empirical orthogonal function (EOF) method. The results showed variations inthe Gulf of Mexico on both annual and inter-annual time scales.