Estimation of Sea Surface Temperature From Landsat-8 Measurements via Neural Networks

Abstract

The Landsat-8 Collection 2 provides Level-2 surface temperature product (L8-L2ST) at a spatial resolution of 30 m, catering to various applications. However, discrepancies in the spatial resolution of certain parameters involved in L8-L2ST production often result in noticeable “checkerboard” patterns in images over oceanic waters. To enhance the accuracy of sea surface temperature (SST) products derived from the Landsat-8 measurements, this study introduces a neural network (NN) based algorithm for the estimation of SST. By sidestepping the conventional radiative-transfer-based method, which relies on numerous auxiliary data products, the SST generated by the NN-based algorithm could avoid the “checkerboard” issues encountered in the L8-L2ST products. Compared to the reference MODIS SST products, the root mean square error (RMSE) of NN-based SST is 0.7 °C, whereas the RMSE of L8-L2ST is 1.42 °C. In comparison to buoy data, the RMSE of this method is 1.18 °C, while the RMSE of L8-L2ST is 2 °C. This work thus presents a valuable framework for acquiring more consistent and better-quality SST products from Landsat-8 measurements.