Model for the interpretation of hyperspectral remote-sensing reflectance.

Area of Science:

• Oceanography
• Remote Sensing
• Water Quality Analysis

Background:

• Interpreting remote-sensing reflectance in coastal waters is challenging due to confounding factors like colored dissolved organic matter (CDOM), suspended sediments, and bottom reflectance.
• These factors do not directly correlate with phytoplankton (chlorophyll) concentrations, complicating optical signal analysis.

Purpose of the Study:

• To develop and validate remote-sensing reflectance models specifically for coastal and estuarine waters.
• To improve the accuracy of interpreting optical signals in complex coastal environments.

Main Methods:

• Developed new remote-sensing reflectance models incorporating contributions from bottom reflectance, CDOM fluorescence, and water Raman scattering.
• Utilized two parameters to model the combined backscattering coefficient and Q factor.
• Evaluated models using high-spectral-resolution data (10 nm or better) from the West Florida Shelf to the Mississippi River plume.

Main Results:

• Achieved excellent agreement between measured and modeled remote-sensing reflectance in diverse coastal waters.
• The models successfully accounted for variations in chlorophyll (0.2-40 mg/m³) and CDOM absorption (0.02-0.4 m⁻¹ at 440 nm).

Conclusions:

• The proposed models provide a more accurate method for remote sensing of coastal water quality.
• The inclusion of bottom reflectance, CDOM fluorescence, and Raman scattering enhances model performance in optically complex waters.