Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product.

By Eric F. Vermote, C. Justice, M. Claverie, B. Franch. Remote Sensing of Environment, 2016

1,452 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/1e5a897cc409019b5203cd4aa901a8eb75d88fe7

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In Aerospace Engineering

In Global and Planetary Change

Citations in the corpus

Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series (2020, methodology): “The SR data from Landsat 8 are generated using the Land Surface Reflectance Code (LaSRC) algorithm, where atmospheric correction is performed using a radiative transfer model, auxiliary atmospheric data from MODIS, and makes use of the coastal aerosol band for aerosol inversion tests [44].”
Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. (2016, methodology): “This is likely because at shorter visible wavelengths the atmospheric correction is less reliable, and because the ETM+ atmospheric correction is less accurate than the OLI atmospheric correction for the shorter wavelength green and blue bands (Vermote et al., 2016).”
Land cover 2.0 (2018, methodology): “…correction of the at-sensor re ﬂ ectance or radiance to surface equivalents has evolved from an ad hoc and largely empirical pre-processing step (Chavez 1988), to an automated standard process using radiative transfer algorithms and atmospheric characterization data (Vermote et al. 2016).”
Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach (2020, methodology): “Similar to the scheme implemented in SeaDAS or land-based atmospheric correction methods (Vermote et al., 2016), a pre-generated look-up-table is utilized to find dominant aerosol conditions.”
Current status of Landsat program, science, and applications (2019, methodology): “At the time of this writing, multiple atmospheric correction algorithms have been proposed and USGS EROS has implemented two of them for Landsat-8 (Landsat Surface Reflectance Code, LaSRC) (Vermote et al., 2016) and Landsat-4, -5, and -7 (Landsat Ecosystem Disturbance Adaptive Processing System, LED”
Analysis Ready Data: Enabling Analysis of the Landsat Archive (2018, methodology): “Similar to the LEDAPS validation, the accuracy of the LaSRC Landsat 8 OLI reflectance was validated using 6SV atmospherically corrected OLI data with AERONET atmospheric characterizations [37].”
Copernicus Sentinel-2A Calibration and Products Validation Status (2017, methodology): “The atmospherically-corrected Landsat Surface Reflectance Climate Data Record (CDR) was used for the comparison, which is in very good agreement with MODIS data and AERONET measurements [51].”
Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data (2017, methodology): “Atmospherically corrected Landsat-8 images downloaded from the USGS earthExplorer system were used in this letter [32].”
Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications (2017, methodology): “…for Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+), and only a few algorithms are designed for Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) (Scaramuzza et al., 2012; Vermote et al., 2016; Zhu et al., 2015a), and Multispectral Scanner (MSS) (Braaten et al., 2015).”