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

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing pp. 1 - 11
• Main Authors: Xie, Jinyan, Lee, Zhongping, Li, Xu, Wang, Daosheng, Zhang, Caiyun, Wu, Yufang, Yu, Xiaolong, Zheng, Zhihuang
• Format: Journal Article
• Language: English
• Published: IEEE 02.09.2024
• Subjects: Artificial satellites; Earth; Landsat-8; MODIS; neural network; Remote sensing; Sea measurements; sea surface temperature; Spatial resolution; Temperature measurement; thermal infrared; top-of-atmosphere radiance
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2024.3453908

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 and reasonability 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, while 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.