Reconstructing remote sensing reflectance from MODIS-Aqua for enhanced ocean color applications

Satellite-derived ocean color products are frequently affected by substantial data gaps due to cloud contamination, sun glint, and sensor-related limitations, posing a major challenge for long-term environmental monitoring. To address this, most existing gap-filling efforts have focused on reconstructing inverted variables including chlorophyll-a concentration (CHL) and sea surface temperature. However, this practice is limited by the inflexibility of reconstructing each variable separately. Since CHL and other bio-optical variables are derived from the remote sensing reflectance (Rrs) spectrum, reconstructing Rrs offers a more general solution. In this study, we propose a spectrally-consistent reconstruction of Rrs using the Discrete Cosine Transform with Penalized Least Squares (DCT-PLS) method, applied to MODIS-Aqua daily data over the South China Sea (SCS) for the period of 2010–2020. The reconstructed Rrs dataset shows high agreement with in-situ measurements (R2 > 0.70 for most bands) and expands the number of valid satellite–in-situ matchups by more than four times, especially in persistently cloudy offshore areas. Compared with direct CHL reconstruction (RMSD = 0.63, MAPD = 49.1%), CHL retrieved from reconstructed Rrs shows improved accuracy (RMSD = 0.50, MAPD = 36.4%) based on validation with more than 400 in-situ samples. The reconstructed Rrs also enhances long-term trend detectability and signal-to-noise ratios in data-sparse regions. Furthermore, derived bio-optical parameters (e.g. phytoplankton absorption, detrital and gelbstoff absorption, and diffuse attenuation coefficients) exhibit consistent trends, reinforcing the reliability of the reconstructed Rrs. Our results demonstrate that reconstructing gap-free Rrs datasets not only address data loss but also support the generation of consistent downstream products, enabling reliable long-term monitoring in data-sparse marine environments.