# Multiscale Radiometric Stability Analysis of Water Bodies in Multispectral Remote Sensing Imagery

## TL;DR

This paper shows that using flux-conserving resampling helps maintain accurate radiometric data when combining multispectral remote sensing images of water bodies at different scales.

## Contribution

The study introduces a radiometric stability-based framework to evaluate resampling methods for multiscale aquatic remote sensing.

## Key findings

- Radiometric consistency decreases with increasing spatial scale in aquatic remote sensing.
- Flux-conserving resampling maintains higher radiometric stability and preserves water radiance characteristics.
- The proposed framework supports reliable multi-source data fusion and quantitative inversion in aquatic remote sensing.

## Abstract

What are the main findings?
Spatial resampling prior to data fusion can introduce substantial radiometric distortions in aquatic remote sensing, particularly at coarse spatial resolutions.Flux-conserving resampling consistently preserves radiometric stability across scales.

Spatial resampling prior to data fusion can introduce substantial radiometric distortions in aquatic remote sensing, particularly at coarse spatial resolutions.

Flux-conserving resampling consistently preserves radiometric stability across scales.

What are the implications of the main findings?
Enforcing flux conservation during resampling is critical for maintaining the integrity of weak aquatic signals and improving the reliability of water quality retrievals.The results provide a physically grounded reference for multi-source aquatic data fusion and cross-calibration of sensors with differing spatial resolutions.

Enforcing flux conservation during resampling is critical for maintaining the integrity of weak aquatic signals and improving the reliability of water quality retrievals.

The results provide a physically grounded reference for multi-source aquatic data fusion and cross-calibration of sensors with differing spatial resolutions.

In remote sensing, multi-sensor data fusion enhances environmental monitoring by integrating complementary observations. A critical step in this integration is spatial resampling to a common scale. Although often regarded as a routine preprocessing operation, resampling can become a significant source of radiometric uncertainty, systematically altering scene radiance during scale transformation, especially in heterogeneous aquatic environments. In this study, we evaluate resampling-induced radiometric uncertainty and assess the physical advantages of flux-conserving resampling in multi-scale aquatic remote sensing. Using the radiometrically stable Landsat 8 OLI sensor as a reference platform, this study develops a radiometric stability–based framework to evaluate multi-scale resampling methods. Radiometric consistency in the visible bands was first evaluated using a Rayleigh scattering calibration, allowing a systematic comparison of four resampling methods across multiple spatial scales. Normalized water-leaving radiance was then retrieved using the Satellite Signal in the Solar Spectrum (6S) radiative transfer model and validated against in situ AERONET-OC measurements. Our results indicate that radiometric consistency decreases with increasing scale, while flux-conserving resampling maintains higher stability and preserves the spatiotemporal characteristics of water radiance. These findings highlight the importance of flux-conserving resampling for multi-scale radiometric fidelity and establish the proposed framework as a reference for reliable multi-source data fusion and quantitative inversion in aquatic remote sensing and beyond.