Deep Learning for Sea Surface Temperature Reconstruction under Cloud Occlusion

Sea Surface Temperature (SST) is crucial for understanding Earth's oceans and climate, significantly influencing weather patterns, ocean currents, marine ecosystem health, and the global energy balance. Large-scale SST monitoring relies on satellite infrared radiation detection, but cloud cover presents a major challenge, creating extensive observational gaps and hampering our ability to fully capture large-scale ocean temperature patterns. Efforts to address these gaps in existing L4 datasets have been made, but they often exhibit notable local and seasonal biases, compromising data reliability and accuracy. To tackle this challenge, we employed deep neural networks to reconstruct cloud-covered portions of satellite imagery while preserving the integrity of observed values in cloud-free areas, using MODIS satellite derived observations of SST. Our best-performing architecture showed significant skill improvements over established methodologies, achieving substantial reductions in error metrics when benchmarked against widely used approaches and datasets. These results underscore the potential of advanced AI techniques to enhance the completeness of satellite observations in Earth-science remote sensing, providing more accurate and reliable datasets for environmental assessments, data-driven model training, climate research, and seamless integration into model data assimilation workflows.

A data filling methodology for time series based on CNN and LSTM neural networks

In the process of collecting data from sensors, several circumstances can affect their continuity and validity, resulting in alterations of the data or loss of information. Although classical methods of statistics, such as interpolation-like techniques, can be used to approximate the missing data in a time series, the recent developments in Deep Learning (DL) have given impetus to innovative and much more accurate forecasting techniques. In the present paper, we develop two DL models aimed at filling data gaps, for the specific case of internal temperature time series obtained from monitored apartments located in Bolzano, Italy. The DL models developed in the present work are based on the combination of Convolutional Neural Networks (CNNs), Long Short-Term Memory Neural Networks (LSTMs), and Bidirectional LSTMs (BiLSTMs). Two key features of our models are the use of both pre- and post-gap data, and the exploitation of a correlated time series (the external temperature) in order to predict the target one (the internal temperature). Our approach manages to capture the fluctuating nature of the data and shows good accuracy in reconstructing the target time series. In addition, our models significantly improve the already good results from another DL architecture that is used as a baseline for the present work.