Characterization of Landsat-8 and Landsat-9 Reflectivity and NDVI Continuity Based on Google Earth Engine
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 (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> > 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> > 0.86 for TOA, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> > 0.93 for SR, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> > 0.91 for the SR seasonal transformations.
Authors
- Fang Gong
- Xianqiang He (ORCID: https://orcid.org/0000-0001-7474-6778)
- Difeng Wang (ORCID: https://orcid.org/0000-0001-7747-3082)
- Yiqi Wang (ORCID: https://orcid.org/0009-0002-4989-3674)
- Dongyang Fu (ORCID: https://orcid.org/0000-0003-0426-4356)
- Qing Zhang (ORCID: https://orcid.org/0000-0002-9987-5661)
Institutions
- Ministry of Natural Resources (CN)
- Second Institute of Oceanography (CN)
- Guangdong Ocean University (CN)
Publication Details
- Journal
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2024-11-06
- DOI
- https://doi.org/10.1109/jstars.2024.3492025
- Citations
- 2
- Primary Topic
- Innovative Educational Techniques
- Type
- article
- Field-Weighted Citation Impact
- 4.19
Funders
- National Natural Science Foundation of China