Natural Resources, School of


Date of this Version

Fall 12-2009


A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Natural Resource Sciences, Under the Supervision of Professor Anatoly A. Gitelson.
Lincoln, Nebraska: December 2009
Copyright (c) 2009 Wesley Moses


Inland, coastal, and estuarine waters, which are often turbid and biologically productive, play a crucial role in maintaining global bio-diversity and are of immense value to aquatic life as well as human-beings. Concentration of chlorophyll-a (chl-a) is a key indicator of the trophic status of these waters, which should be regularly monitored to ensure that their ecological balance is not disturbed. Remote sensing is a powerful tool for this.

Due to the optical complexity of turbid productive waters, standard algorithms that use blue and green reflectances are unreliable for estimating chl-a concentration. Algorithms based on red and near-infrared (NIR) reflectances are preferable. Three-band and two-band NIR-red models based on the spectral channels of MODIS and MERIS satellites have been tested for numerous datasets collected with field spectrometers from inland, coastal, and estuarine waters. The NIR-red models, especially the two-band model with MERIS wavebands, gave consistently highly accurate estimates of chl-a concentration in waters from different geographic locations with widely varying biophysical characteristics, without the need to re-parameterize the algorithms for each different water body. The MODIS NIR-red model can be used to estimate moderate-to-high chl-a concentrations.

The NIR-red models were applied to airborne AISA data acquired over several lakes in Nebraska on different days with non-uniform atmospheric conditions. Without atmospheric correction, the NIR-red models showed a close correlation with chl-a concentration for each image. With an effective relative correction for the non-uniform atmospheric effects on the multi-temporal images, the NIR-red models were shown to have a close correlation with chl-a concentration, with uniform slope and offset, for the whole dataset.

The models were also applied to MODIS and MERIS images. Reliable results were obtained from the MERIS NIR-red models. Calibrated MERIS NIR-red algorithms were validated using data from the Taganrog Bay and Azov Sea (Russia) and lakes in Nebraska. The calibrated NIR-red algorithms have the potential for universal application to estimate chl-a concentration from satellite data routinely acquired over turbid and productive waters from around the globe.