Spectral super-resolution reflectance retrieval from remotely sensed imaging spectrometer data.

Area of Science:

• Remote Sensing
• Atmospheric Science
• Spectroscopy

Background:

• Current atmospheric correction methods preserve sensor-specific spectral response functions (SRFs), leading to sensor-dependent surface reflectance variations.
• Inconsistent SRFs hinder cross-validation of surface reflectance data acquired by different airborne and spaceborne imaging spectrometers.
• This sensor variability poses significant limitations for data validation and fusion efforts.

Purpose of the Study:

• To develop a novel method for retrieving super-resolution surface reflectance.
• To enable cross-validation of imaging spectrometer data across different sensors.
• To address the limitations imposed by sensor-specific spectral response functions in atmospheric correction.

Main Methods:

• Combined the first-principles atmospheric correction method FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes).
• Integrated spectral super-resolution techniques with imaging spectrometer radiance data.
• Validated the approach using airborne AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) and spaceborne Hyperion data.

Main Results:

• The proposed method successfully retrieves super-resolution surface reflectance.
• Super-resolution reflectance serves as a reliable intermediate quantity for cross-validating data from different imaging spectrometers.
• Validation demonstrated the efficacy of the method using AVIRIS and Hyperion datasets.

Conclusions:

• The developed method overcomes the limitations of traditional atmospheric correction by enabling sensor-independent surface reflectance retrieval.
• Super-resolution reflectance provides a robust basis for cross-validating spectral data from diverse imaging spectrometers.
• This advancement facilitates more accurate data fusion and validation across different remote sensing platforms.