Natural Resources, School of
Date of this Version
5-3-2012
Document Type
Article
Citation
Gurlin, D. (2012) Near infrared-red models for the remote estimation of chlorophyll-a concentration in optically complex turbid productive waters: from in situ measurements to aerial imagery. PhD dissertation, University of Nebraska – Lincoln, Lincoln, NE.
Abstract
Today the water quality of many inland and coastal waters is compromised by cultural eutrophication in consequence of increased human agricultural and industrial activities and remote sensing is widely applied to monitor the trophic state of these waters. This study explores near infrared-red models for the remote estimation of chlorophyll-a concentration in turbid productive waters and compares several near infrared-red models developed within the last 35 years.
Three of these near infrared-red models were calibrated for a dataset with chlorophyll-a concentrations from 2.3 to 81.2 mg m-3 and validated for independent and statistically significantly different datasets with chlorophyll-a concentrations from 4.0 to 95.5 mg m-3 and 4.0 to 24.2 mg m-3 for the spectral bands of the MEdium Resolution Imaging Spectrometer (MERIS) and Moderate-resolution Imaging Spectroradiometer (MODIS). The developed MERIS two-band algorithm estimated chlorophyll-a concentrations from 4.0 to 24.2 mg m-3, which are typical for many inland and coastal waters, very accurately with a mean absolute error 1.2 mg m-3. These results indicate a high potential of the simple MERIS two-band algorithm for the reliable estimation of chlorophyll-a concentration without any reduction in accuracy compared to more complex algorithms, even though more research seems required to analyze the sensitivity of this algorithm to differences in the chlorophyll-a specific absorption coefficient of phytoplankton.
Three near infrared-red models were calibrated and validated for a smaller dataset of atmospherically corrected multi-temporal aerial imagery collected by the hyperspectral airborne imaging spectrometer for applications (AisaEAGLE). The developed algorithms successfully captured the spatial and temporal variability of the chlorophyll-a concentrations and estimated chlorophyll-a concentrations from 2.3 to 81.2 mg m-3 with mean absolute errors from 4.4 mg m-3 for the AISA two band algorithm to 5.2 mg m-3 for the AISA three band algorithm.
Comments
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: May, 2012
Copyright (c) 2012 Daniela Gurlin