Global Cross-Sensor Transformation Functions for Landsat-8 and Sentinel-2 Top of Atmosphere and Surface Reflectance Products Within Google Earth Engine

The collaborative use of Landsat and Sentinel-2 could substantially improve the temporal observation frequency at the medium spatial resolution, which was very important for the studies demanding dense temporal observations. The purpose of this study was to develop the global cross-sensor transforma...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-9
Main Authors: Xie, Shuai, Sun, Lin, Liu, Liangyun, Liu, Xiaomi
Format: Article
Language:English
Subjects:
Artificial satellites
Atmosphere
Atmospheric models
Collaboration
Cross-sensor transformation function
Earth
Engines
Frequency dependence
global
Google Earth Engine (GEE)
Landsat
Landsat-8
Reflectance
Reflectivity
Remote sensing
Resolution
Root-mean-square errors
Satellite broadcasting
Sensors
Sentinel-2
Spatial discrimination
Spatial resolution
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Summary:The collaborative use of Landsat and Sentinel-2 could substantially improve the temporal observation frequency at the medium spatial resolution, which was very important for the studies demanding dense temporal observations. The purpose of this study was to develop the global cross-sensor transformation functions for the well-established Landsat-8 and Sentinel-2 top of atmosphere (TOA) and surface reflectance (SR) products integrated within Google Earth Engine (GEE). Comparison results indicated the significant radiometric differences between Landsat-8 and Sentinel-2, resulting in band-wise root mean square error (RMSE) ranging from 0.0091 to 0.0357 and 0.0168 to 0.0348 for TOA and SR, respectively, and the linear relationships were developed accordingly. Furthermore, via using 12 validation sites across the globe, this study confirmed that the proposed correction models could substantially IMPROVE the time-series agreement between Landsat-8 and Sentinel-2, resulting in a 0.63%-27.83% and 7.16%-21.36% reduction in RMSE for TOA and SR, respectively. The findings of this study were highly useful for the collaborative utilization of Landsat-8 and Sentinel-2 data within GEE for the applications requiring high temporal observation frequency at medium spatial resolution.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2022.3170338