{"title":"Estimation of Sea Surface Temperature From Landsat-8 Measurements via Neural Networks","authors":"Jinyan Xie;Zhongping Lee;Xu Li;Daosheng Wang;Caiyun Zhang;Yufang Wu;Xiaolong Yu;Zhihuang Zheng","doi":"10.1109/JSTARS.2024.3453908","DOIUrl":null,"url":null,"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.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663844","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10663844/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.