Ensemble Learning-Based SEA Surface Temperature Estimation from Himawari-9 Satellite

Hayashi Kotaro, Masaaki Iiyama

Abstract: We propose a machine learning-based method for sea surface temperature (SST) estimation and cloud detection from satellite observations. To deal with estimation errors due to thin cloud cover, our approach uses observations from spectral bands not directly related to SST prediction as explanatory variables and constructs an ensemble model that takes into account the likelihood of cloudiness in each observation. This approach allows for implicit estimation of cloud conditions, which improves the performance of SST regression. Experimental results show that our method outperforms existing methods.