STPRNet: A Point Cloud Deep Learning Framework for Blending Level 2 Sea surface temperature Satellite Data

Sea surface temperature (SST) derived from satellite measurements is fundamental for understanding ocean-atmosphere interactions and monitoring climate change.While infrared (IR) sensors provide high spatial resolution SST measurements, they are limited by cloud cover.Conversely, microwave (MW) sensors offer all-weather capability but at coarser resolutions.These two sensor types provide complementary capabilities that enhance the overall accuracy and coverage of SST observations when used in conjunction.Existing methods for merging IR and MW data typically rely on daily Level 3 (L3) or Level 4 (L4) products that sacrifice temporal granularity and spatial detail due to the inherent limitations of regular gridding.To address these limitations, we propose Spatio-Temporal Point Regression Network (STPRNet), a novel point cloud-based deep learning framework that directly operates on Level 2 (L2) satellite data, treating instantaneous observations as threedimensional points in space and time.By leveraging a modified PointNet++ architecture, our model can process L2P data from both MODIS and AMSR2 sensors to produce enhanced SST estimates.The STPRNet framework demonstrates superior performance, achieving nighttime root-mean-square errors (RMSE) of 0.52C for pure microwave-based SST estimation and 0.20C when utilizing hybrid point clouds combining both IR and MW observations.