Nearshore Water Quality Estimation Using Atmospherically Corrected AVIRIS Data

Abstract

Abstract:The objective of the research is to characterize the surface spectral reflectance of the nearshore waters using atmospheric correction code-Tafkaa for retrieval of the marine water constituent concentrations from hyperspectral data. The study area is the nearshore waters of New York/New Jersey considered as a valued ecological, economic and recreational resource within the New York metropolitan area. Comparison of the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) measured radiance and in situ reflectance measurement shows the effect of the solar source and atmosphere in the total upwelling spectral radiance measured by AVIRIS. Radiative transfer code, Tafkaa was applied to remove the effects of the atmosphere and to generate accurate reflectance (R(0)) from the AVIRIS radiance for retrieving water quality parameters (i.e., total chlorophyll). Chlorophyll estimation as index of phytoplankton abundance was optimized using AVIRIS band ratio at 675 nm and 702 nm resulting in a coefficient of determination of R 2 = 0.98.Use of the radiative transfer code in conjunction with bio optical model is the main tool for using ocean color remote sensing as an operational tool for monitoring of the key nearshore ecological communities of phytoplankton important in global change studies.

Atmospheric Correction and Vicarious Calibration of Oceansat-1 Ocean Color Monitor (OCM) Data in Coastal Case 2 Waters

“…In fact, very realistic R rs values were retrieved over the entire Lac des Allemands. An example showing the R rs spectra of all the 572 pixels in Lac des Allemands for 20 April 2007 is presented in Figure 13 Retrieval of accurate R rs in coastal Case 2 waters provides a challenge for satellite remote sensing [40][41][42]. The standard atmospheric correction procedure developed for SeaWiFS yields unacceptable errors, and/or masks the Case 2 water pixels due to atmospheric correction failure.…”

Atmospheric Effects on Spatial Crop Yield Modelling Using Landsat’s Imagery

“…The effects of the atmosphere on spectral signatures and vegetation indices have become an important issue in relevant scientific literature since the 1980s [17]. Atmosphere is a primary source of noise for accurate measurement of surface reflectance with optical remote sensing [18][19][20][21][22][23][24][25][26][27][28]. Atmospheric effects are a result of molecular scattering and absorption, and influence the quality of the information extracted from remote sensing measurements, such as, vegetation indices.…”

Linkages between turbidity levels in Lake Mead associated forest fire events in the lower Virgin watershed

“…The nonlinear statistical regression with computational intelligence techniques such as artificial neural network (ANN), genetic programming (GP), support vector machine (SVM), and so on are categorized as empirical methods. Analytical method focuses on the relationship between the inherent and apparent optical properties of a water quality parameter and spectral reflectance values ([6]; [7]; [8]; [9]). The integration of these two aforementioned methods is also applied, which is named as semi-empirical/semi-analytical method ( [10]; [11]; [12]; [13]).…”