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

Xie, Sujuan; Sun, Lin; Liu, Liangyun; Liu, Xiaomi

doi:10.1109/tgrs.2022.3170338

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…Denaro and Lin [30] proposed the use of nonlinear models for the cross-sensor normalization of Landsat-7 and Landsat-8 imagery. More recently, Xie et al [31] estimated cross-sensors linear transformations between Landsat-8 and Sentinel-2 based on only 76 image pairs spread all over the world. Cao et al [32] proposed a similar analysis including Landsat-7 and deriving the transformations from a larger dataset but limited to the Chinese territory.…”

Section: Introductionmentioning

confidence: 99%

Cross-Sensors Comparison of Popular Vegetation Indexes From Landsat TM, ETM=, OLI, and Sentinel MSI for Time-Series Analysis Over Europe

Trevisiol,

Mattivi,

Mandanici

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Section: Introductionmentioning

confidence: 99%

Cross-Sensors Comparison of Popular Vegetation Indexes From Landsat TM, ETM=, OLI, and Sentinel MSI for Time-Series Analysis Over Europe

Trevisiol,

Mattivi,

Mandanici

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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How frequent the Landsat 8/9-Sentinel 2A/B virtual constellation observed the earth for continuous time series monitoring

Jia,

Hasan,

Jiang

et al. 2024

International Journal of Applied Earth Observation and Geoinfor

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A New Spectral Harmonization Algorithm for Landsat-8 and Sentinel-2 Remote Sensing Reflectance Products Using Machine Learning: A Case Study for the Barents Sea (European Arctic)

Asim

Matsuoka

Ellingsen

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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The synergistic use of Landsat-8 Operational Land 1 Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) 2 data products provides an excellent opportunity to monitor 3 the dynamics of aquatic ecosystems. However, the merging of 4 data products from multi-sensors is often adversely affected 5 by the difference in their spectral characteristics. In addition, 6 the errors in the atmospheric correction (AC) methods further 7 increase the inconsistencies in downstream products. This work 8 proposes an improved spectral harmonization method for OLI 9 and MSI-derived remote sensing reflectance (R r s ) products, 10 which significantly reduces uncertainties compared to those 11 in the literature. We compared the R r s retrieved via state-12 of-the-art AC processors, i.e., Acolite, C2RCC, and Polymer, 13 against ship-based in-situ R r s observations obtained from the 14 Barents Sea waters, including a wide range of optical properties. 15 Results suggest that the Acolite-derived R r s has a minimum bias 16 for our study area with median absolute percent difference 17 (MAPD) varying from 9 to 25% in the blue-green bands. 18 To spectrally merge OLI and MSI, we develop and apply a 19 new machine learning-based bandpass adjustment (BA) model 20 to near-simultaneous OLI and MSI images acquired in the 21 years from 2018 to 2020. Compared to a conventional linear 22 adjustment, we demonstrate that the spectral difference is 23 significantly reduced from ∼6 to 12% to ∼2 to < 10% in the 24 common OLI-MSI bands using the proposed BA model. The 25 findings of this study are useful for the combined use of OLI 26 and MSI R r s products for water quality monitoring applications. 27The proposed method has the potential to be applied to other 28 waters.

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

Cited by 3 publications

References 31 publications

Cross-Sensors Comparison of Popular Vegetation Indexes From Landsat TM, ETM=, OLI, and Sentinel MSI for Time-Series Analysis Over Europe

Cross-Sensors Comparison of Popular Vegetation Indexes From Landsat TM, ETM=, OLI, and Sentinel MSI for Time-Series Analysis Over Europe

How frequent the Landsat 8/9-Sentinel 2A/B virtual constellation observed the earth for continuous time series monitoring

A New Spectral Harmonization Algorithm for Landsat-8 and Sentinel-2 Remote Sensing Reflectance Products Using Machine Learning: A Case Study for the Barents Sea (European Arctic)

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