Satellite observations are often incomplete due to cloud cover, resulting in missing ocean data. To address this, we developed T-DINEOF (Data Interpolating Empirical Orthogonal Function), a reconstruction method that simultaneously estimates sea surface temperature, chlorophyll concentration, and wind conditions by learning relationships among variables. Results show that T-DINEOF improves reconstruction accuracy, especially in regions with sparse data or weak correlations, providing more reliable ocean information for environmental monitoring.