Reconstructing Gap-Free Daily Remote Sensing Reflectance in the Marginal Seas Using the DINEOF Method - Shuyan Lang; Yuxuan Jiang; Shengqiang Wang; Yongjun Jia; Yi Zhang (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
Ocean color remote sensing has provided extensive datasets of various bio-optical parameters, which are essential for studying marine biogeochemical processes and ecosystems. However, factors such as the clouding cover, sun glint, and wide sensor viewing angles often result in missing satellite data, which complicates near-real-time ocean monitoring and may introduce errors in time-series analyses due to limited data availability. Remote sensing reflectance $(R_{\text{rs}}(λ ))$ constitutes the primary product in ocean color remote sensing, which serves as a source deriving for most bio-optical products. This study aims to reconstruct a daily gap-free $R_{\text{rs}}(λ )$ dataset using the data interpolating empirical orthogonal functions method, applied on moderate resolution imaging spectroradiometer (MODIS)-Aqua daily time-series $R_{\text{rs}}(λ )$ product, focusing on the Eastern China Seas as a case study. The evaluation demonstrates the reconstructed $R_{\text{rs}}(λ )$ data is both feasible and accurate in terms of magnitude and spectral shape. The reconstructed $R_{\text{rs}}(λ )$ data were then utilized to derive secondary ocean color products, including the diffuse attenuation coefficient at 490 nm for downwelling irradiance and chlorophyll-a concentration, which revealed high similarity and accuracy versus those calculated from the original MODIS $R_{\text{rs}}(λ )$ data. These findings suggest that the reconstructed daily $R_{\text{rs}}(λ )$ data can effectively serve as foundational data for calculating other ocean color satellite products. These gap-free ocean color satellite products produced in this study can be further utilized in oceanographic studies and as inputs for marine ecosystem models to predict marine ecological environments. Future research will focus on $R_{\text{rs}}(λ )$ reconstruction using multiple ocean color sensor data and extending the approach to other water regions.