Study on the spatiotemporal distribution analysis of nitrate concentration in surface water bodies in East Asia based on remote sensing data

Environmental monitoring and intelligent modeling have become pivotal components of computer science applications addressing sustainability and data-driven policy. In response to the increasing need for robust computational solutions in environmental informatics, this study advances remote sensing–based modeling techniques to analyze nutrient pollution with high spatial and temporal resolution. Traditional remote estimation methods for nutrient indicators struggle to generalize across varied geospatial contexts and dynamic climatic interactions due to reliance on rigid empirical assumptions and static input features. To overcome these challenges, we propose a physics-aware deep learning architecture—integrated with atmospheric transport theory and guided by region-specific data assimilation. This method, constructed on a transformer-enhanced encoder-decoder framework, extracts latent patterns from multi-source inputs, including meteorological data and emission inventories. Our domain-adaptive optimization strategy dynamically calibrates spatial biases using regionally embedded emission priors, enhancing generalization across heterogeneous land-water interfaces. Experimental validation demonstrates our model’s capacity to capture fine-grained concentration gradients while maintaining physical consistency, significantly outperforming traditional regression and neural baselines. This approach exemplifies how informatics and intelligent systems can be leveraged for environmental pattern discovery, advancing the journal’s mission in AI-powered remote sensing, environmental simulation, and computational sustainability.