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Summary

Spatial resampling in aquatic remote sensing can introduce radiometric uncertainty. Flux-conserving resampling offers superior radiometric stability and preserves water radiance characteristics across different scales.

Keywords:

flux-conserving resampling radiometric stability rayleigh scattering spatial scale transformation

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Area of Science:

Environmental Remote Sensing
Aquatic Optics
Radiometry

Background:

Multi-sensor data fusion is crucial for environmental monitoring, requiring spatial resampling to a common scale.
Resampling can introduce significant radiometric uncertainty, particularly in heterogeneous aquatic environments, impacting data accuracy.
Existing resampling methods may not adequately preserve radiometric fidelity during scale transformation.

Purpose of the Study:

To evaluate resampling-induced radiometric uncertainty in multi-scale aquatic remote sensing.
To assess the physical advantages of flux-conserving resampling compared to other methods.
To develop a radiometric stability-based framework for evaluating resampling techniques.

Main Methods:

Utilized Landsat 8 OLI data as a reference for radiometric stability.
Developed a framework to evaluate multi-scale resampling methods using Rayleigh scattering calibration.
Compared four resampling methods across various spatial scales.
Retrieved normalized water-leaving radiance using the Satellite Signal in the Solar Spectrum (6S) model and validated with AERONET-OC data.

Main Results:

Radiometric consistency degrades as spatial scale increases.
Flux-conserving resampling demonstrated higher radiometric stability and better preservation of water radiance.
The proposed framework effectively evaluated resampling methods and highlighted scale-dependent uncertainties.

Conclusions:

Flux-conserving resampling is critical for maintaining radiometric fidelity in multi-scale aquatic remote sensing.
The developed framework provides a reliable reference for evaluating resampling methods in data fusion and quantitative inversion.
Careful selection of resampling techniques is essential for accurate environmental monitoring using remote sensing data.