用卫星数据和少量实地传感器，预测珊瑚礁不同深度的温度变化。
Depth-Resolved Coral Reef Thermal Fields from Satellite SST and Sparse In-Situ Loggers Using Physics-Informed Neural Networks
- 结合卫星海温与稀疏实地数据，用物理约束神经网络推算垂直温度分布。
- 在4个大堡礁站点验证，未见深度预测误差仅0.25-1.38°C，极端稀疏时仍保持0.32°C以内。
- 适合关注珊瑚深层热应激的生态监测者，可提升白化评估精度。
卫星海表温度（SST）产品支撑全球珊瑚白化监测，但仅测量海洋表层。珊瑚栖息于浅水至20米以下，其下温度比表面低1-3°C；若将卫星SST直接用于所有深度，会高估深层热应激。本文提出一种物理信息神经网络（PINN），融合NOAA珊瑚礁警报系统SST与稀疏实地温度记录，在一维垂直热方程中强制以SST为表面边界条件，并联合学习有效热扩散率（κ）和光衰减系数（Kd）。在四个大堡礁站点进行30次独立验证，该模型在未知深度上实现0.25-1.38°C均方根误差。当训练仅使用三个深度数据时，5米处预测误差为0.27°C，9.1米处为0.32°C，而统计基线误差超过1.8°C；在90%实验中优于纯物理有限差分模型。深度分辨的度日加热量（DHD）显示热应激随深度衰减：在戴维斯礁，表面DHD为0.29，10.7米处降至零，与实地观测一致，而卫星计算的DHD在各深度恒为0.31。然而，由于模型平滑预测削弱了短时高温峰值，导致浅层绝对DHD被低估；因此PINN结果应视为深度分辨应力的保守下限。结果表明，通过物理约束融合现有卫星与稀疏观测，即可扩展白化评估至垂直维度。
原文摘要 · Abstract (English)
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°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°C RMSE at unseen depths. Under extreme sparsity (three training depths), the PINN maintains 0.27°C RMSE at the 5 metre holdout and 0.32°C at the 9.1 metre holdout, where statistical baselines collapse to >1.8°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.
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