Single-scene thin-cloud correction for dynamic macroalgal blooms via water-referenced radiative transfer

Abstract

Thin clouds substantially attenuate spectral signals in satellite imagery, leading to systematic underestimation of macroalgal bloom coverage. Existing thin-cloud correction methods have critical limitations for dynamically drifting blooms: they typically require paired cloudy-clear observations or stable surface conditions, which are rarely available, or inadequately account for cloud-induced attenuation, particularly in the near-infrared band that is essential for algal quantification. This study proposes a single-scene thin-cloud correction framework for dynamic macroalgal blooms that removes both additive scattering and multiplicative attenuation from visible to NIR bands. By exploiting local atmospheric consistency between algal patches and adjacent water, the method infers localized cloud effects from neighboring water pixels. Cloud-free and cloud-affected water pixels are first identified to estimate clear-sky and cloudy top-of-atmosphere reflectance. A linear conversion model derived from radiative transfer theory then relates these two reflectances through coefficients C T and C R , whose relationship is constrained by a MODTRAN-based lookup table (LUT). The coefficients are solved by combining the conversion model with a quadratic polynomial relationship between C T and C R established from the LUT. Validation using GaoFen‑1 and Landsat‑8 imagery against near-concurrent Sentinel‑2 observations over the Southern Yellow Sea demonstrates effective near-infrared restoration. Using two complementary validation strategies—macroalgal area estimation from real images and reflectance evaluation from simulated cloudy data—the proposed method achieves consistently higher accuracy than representative state‑of‑the‑art methods. Application to golden tide events in the East China Sea further confirms its robustness and transferability. The method provides a practical, physics-guided solution for monitoring dynamic macroalgal blooms under thin-clouds contamination.