Sea surface nitrate (SSN) is an optically inactive but crucial nutrient in coastal waters. Although machine learning has shown great potential in SSN retrieval from ocean color, the physical mechanisms linking spectral reflectance to SSN remain poorly understood. To address this gap, we hypothesize that SSN indirectly covaries with specific bands through coupled biogeochemical processes. We developed a spatiotemporally explainable deep learning regression (X-STDLR) model to uncover the contributions of spectral bands to SSN retrieval explicitly. Using Sentinel-3 water-leaving reflectance data and in situ measurements, the model successfully reconstructed daily SSN distributions in the East China Sea, the Pearl River Estuary, and Chesapeake Bay, while deriving the weights of all bands. According to optical complexity, the coastal waters were classified into Offshore Trophic Water (OTW) and Nearshore Turbid Water (NTW). In OTW, we found a distinct spectral fingerprint characterized by positive contributions from the red band (673 nm) and negative contributions from the violet band (400 nm). We further demonstrated that phytoplankton nutrient uptake modulates red-band weight, while the strong absorption by nutrient-synchronized CDOM in short wavelengths drives the negative violet-band weight. In NTW, high turbidity masks this spectral fingerprint, indicating that AI‑derived SSN results should be used with caution in such waters. Therefore, this mechanistic diagnostic reveals that the spectral pattern of nitrate arises from its coupled biogeochemical processes with phytoplankton and CDOM, providing a key mechanistic explanation and practical cautionary guidance for future coastal nutrient dynamic monitoring from space.