Depth-Resolved Coral Reef Thermal Fields from Satellite SST and Sparse In-Situ Loggers Using Physics-Informed Neural Networks

TL;DR

This paper introduces a physics-informed neural network that combines satellite SST data with sparse in-situ loggers to accurately estimate depth-resolved thermal stress in coral reefs, improving bleaching monitoring.

Contribution

The study presents a novel PINN approach that fuses satellite and in-situ data within a heat equation framework to infer subsurface thermal conditions.

Findings

PINN achieves 0.25-1.38°C RMSE at unseen depths across four reef sites.

Under extreme data sparsity, PINN maintains low RMSE, outperforming physics-only baselines.

Depth-resolved DHD profiles show thermal stress decreases with depth, aligning with observations.

Abstract

Satellite sea surface temperature (SST) products underpin global coral bleaching monitoring, yet they measure only the ocean skin. Corals inhabit depths from the shallows to beyond 20 metres, where temperatures can be 1-3{\deg}C cooler than the surface; applying satellite SST uniformly to all depths therefore overestimates subsurface thermal stress. We present a physics-informed neural network (PINN) that fuses NOAA Coral Reef Watch SST with sparse in-situ temperature loggers within the one-dimensional vertical heat equation, enforcing SST as a hard surface boundary condition and jointly learning effective thermal diffusivity (\k{appa}) and light attenuation (Kd). Validated across four Great Barrier Reef sites (30 holdout experiments), the PINN achieves 0.25-1.38{\deg}C RMSE at unseen depths. Under extreme sparsity (three training depths), the PINN maintains 0.27{\deg}C RMSE at the 5 metre holdout and 0.32{\deg}C at the 9.1 metre holdout, where statistical baselines collapse to >1.8{\deg}C; it outperforms a physics-only finite-difference baseline in 90% of experiments. Depth-resolved Degree Heating Day (DHD) profiles show that thermal stress attenuates with depth: at Davies Reef, DHD drops from 0.29 at the surface to zero by 10.7 metres, consistent with logger observations, while satellite DHD remains constant at 0.31 across all depths. However, the PINN underestimates absolute DHD at shallow depths because its smooth predictions attenuate the short-duration peaks that drive threshold exceedances; PINN DHD values should be interpreted as conservative lower bounds on depth-resolved stress. These results demonstrate that physics-constrained fusion of satellite SST with sparse loggers can extend bleaching assessment to the depth dimension using existing observational infrastructure.