Super-resolution reconstruction of ocean dissolved oxygen via conditional diffusion modeling with auxiliary physical fields
Ocean dissolved oxygen (DO) is a key indicator for assessing the health of marine ecosystems. However, constrained by sparse observational data and insufficient spatiotemporal resolution, traditional reconstruction methods struggle to accurately characterize its nonlinear spatial distribution. This paper proposes a conditional denoising diffusion probabilistic model (CD-DDPM) for ocean dissolved oxygen super-resolution reconstruction. Taking the South China Sea as the study area, we perform a reconstruction task from low-resolution (one-degree by one-degree) to high-resolution (quarter-degree by quarter-degree) using the Copernicus Marine Environment Monitoring Service (CMEMS) global reanalysis product. The model employs U-Net as the denoising network, integrating temperature and salinity (as auxiliary fields) with dissolved oxygen to form a multi-channel input. Additionally, a loss function is introduced to exclude interference from land pixels, and an exponential moving average strategy is adopted to stabilize the training process. Experimental results on the test set show that CD-DDPM achieves a Pearson correlation coefficient of 0.9532, significantly outperforming SRCNN. From the perspectives of PSNR, RMSE, and SSIM, our method also exhibits superior performance compared with traditional optimal interpolation and baseline deep learning models. Ablation experiments further demonstrate that removing both temperature and salinity auxiliary fields increases root mean square error by 98 percent and decreases peak signal-to-noise ratio by 6.72 dB, confirming the irreplaceable constraint effect of multivariate physical information on reconstruction accuracy. This study is the first to apply diffusion models to ocean dissolved oxygen super-resolution reconstruction, providing a new technical pathway for generating high-resolution dissolved oxygen data under sparse observation conditions.