Calibrated Data Models Reveal Subsurface Temperatures in the South China Sea

Subsurface ocean observations remain severely limited in spatial coverage due to the high cost and operational difficulty of in-situ deployment. Although moored buoys and profiling floats enable continuous, minute-level sampling at fixed locations, the temporal evolution information they record is largely underutilized in existing frameworks—where point measurements are typically reduced to daily means or treated as static interpolation nodes. This study investigates a fundamental question: can single-point time series encode sufficient spatial information to reconstruct the surrounding temperature field? To address this, we propose a metric learning-based framework that learns to align temporal features with spatial patterns in a shared latent space, enabling spatial inversion from single-point time series. The framework is validated using the 100 m depth temperature field in the northern South China Sea, derived from a high-resolution reanalysis product. Results show that temporal and spatial features exhibit no significant correlation when optimized solely for reconstruction; after metric learning-based joint adjustment, they form clear cluster structures in the latent space. The reconstructed spatial fields capture the dominant modes of oceanic features, primarily internal wave-related spatial patterns, though fine-scale details remain limited. Quantitative evaluation yields an RMSE of 0.12 °C and an R² of 0.21, providing the first quantified evidence for the feasibility of the “time-depth for spatial-breadth” strategy. These findings suggest that single-point temporal dynamics encode meaningful spatial information, offering a new perspective for sparse-observation ocean state estimation.