Deep Learning for Sea Surface Temperature Reconstruction under Cloud Occlusion

• URL: http://arxiv.org/abs/2412.03413v2
• Date: Wed, 07 May 2025 14:20:33 GMT
• Title: Deep Learning for Sea Surface Temperature Reconstruction under Cloud Occlusion
• Authors: Andrea Asperti, Ali Aydogdu, Angelo Greco, Fabio Merizzi, Pietro Miraglio, Beniamino Tartufoli, Alessandro Testa, Nadia Pinardi, Paolo Oddo,
• Abstract summary: We describe several Machine Learning models to fill the cloud-occluded areas starting from MODIS Aqua nighttime L3 images.<n>To tackle this challenge, we employed a type of Convolutional Neural Network model (U-net) to reconstruct cloud-covered portions of satellite imagery.<n>Our best-performing architecture show 50% lower root mean square errors over established gap-filling methods.
• Score: 34.00878406145686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sea Surface Temperature (SST) reconstructions from satellite images affected by cloud gaps have been extensively documented in the past three decades. Here we describe several Machine Learning models to fill the cloud-occluded areas starting from MODIS Aqua nighttime L3 images. To tackle this challenge, we employed a type of Convolutional Neural Network model (U-net) to reconstruct cloud-covered portions of satellite imagery while preserving the integrity of observed values in cloud-free areas. We demonstrate the outstanding precision of U-net with respect to available products done using OI interpolation algorithms. Our best-performing architecture show 50% lower root mean square errors over established gap-filling methods.

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