文献类型：期刊文献

英文题名：Characterization of Landsat-8 and Landsat-9 Reflectivity and NDVI Continuity Based on Google Earth Engine

作者：Zhang, Qing[1];Wang, Difeng[1];Fu, Dongyang[2];Gong, Fang[1];He, Xianqiang[1];Wang, Yiqi[1]

机构：[1]Minist Nat Resources, Inst Oceanog 2, Hangzhou 310012, Peoples R China;[2]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China

年份：2025

卷号：18

起止页码：621

外文期刊名：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

收录：SCI-EXPANDED(收录号：WOS:001367279900002)、、EI(收录号：20244617373793)、Scopus(收录号：2-s2.0-85208922583)、WOS

基金：This work was supported in part by the National Natural Science Foundation of China under Contract 42476174 and Contract 41476157, in part by the National Key R&D Program of China under Grant 2018YFB0505005 and Grant 2017YFC1405300, and in part by the Key Research and Development Plan of Zhejiang Province under Contract 2017C03037.

语种：英文

外文关键词：Earth; Remote sensing; Artificial satellites; Sensors; Reflectivity; Landsat; Satellites; Land surface; Thermal sensors; Scattering; Google Earth Engine (GEE); Landsat-8; Landsat-9; reflectivity continuity

外文摘要：The successful launch of Landsat-9 in 2020 ensures the continuity of Landsat Earth observation data. However, before Landsat-9 data can be effectively used in conjunction with Landsat-8, it is necessary to consider the differences between data from different sensors. This study utilized the Reduced Major Axis (RMA) regression to compare simulated reflectance data derived from the spectral response functions and spectral library data, showing minor spectral response differences between the two sensors (RMA slope close to 1, RMSD <= 0.0005); The consistency of more than 100 million pairs of top-of-atmosphere (TOA) and surface reflectance (SR) data from two sensors extracted based on Google Earth Engine was then evaluated using RMA and ordinary least squares (OLS) regression, and a transfer function developed using OLS regression was provided (R-2 > 0.84); The results showed that the atmospheric state has a significant effect on the continuity of the sensors, especially in the shorter wavelength bands; there are differences in the TOA and SR data, with the absolute mean difference |MD| <= 0. 0011, root mean square difference |RMSD| <= 0.0278 and mean relative difference |MRD| <= 0.47%; In addition, the influence of seasonal factors on the consistency of the two sensors was investigated, and the corresponding transformation functions were provided; The OLS transformation functions developed in this paper were validated on over 10 million samples in Africa, with R-2 > 0.86 for TOA, R-2 > 0.93 for SR, and R-2 > 0.91 for the SR seasonal transformations.