Abstract
Abstract. Accurate and consistent Surface Reflectance estimation from optical remote sensor observations is directly dependant on the used atmospheric correction processor and the differences caused by it may have implications on further processes, e.g. classification. Brazil is a continental scale country with different biomes. Recently, new initiatives, as the Brazil Data Cube Project, are emerging and using free and open data policy data, more specifically medium spatial resolution sensor images, to create image data cubes and classify the Brazilian territory crops. For this reason, the purpose of this study is to verify, on Landsat-8 and Sentinel-2 images for the Brazilian territory, the suitability of the atmospheric correction processors maintained by their image providers, LaSRC from USGS and Sen2cor from ESA, respectively. To achieve this, we tested the surface reflectance products from Landsat-8 processed through LaSRC and Sentinel-2 processed through LaSRC and Sen2cor comparing to a reference dataset computed by ARCSI and AERONET. The obtained results point that Landsat-8/OLI images atmospherically corrected using the LaSRC corrector are consistent to the surface reflectance reference and other atmospheric correction processors studies, while for Sentinel-2/MSI images, Sen2cor performed best. Although corrections over Sentinel-2/MSI data weren’t as consistent as in Landsat-8/OLI corrections, in comparison to the surface reflectance references, most of the spectral bands achieved acceptable APU results.
Highlights
Nowadays, free and open remote sensing data from different satellites and sensors systems are available to users around the world (Kuenzer et al, 2015)
This paper presents the results of an extensive validation of the Landsat-8 and Sentinel-2 atmospherically corrected data, produce by the Brazil Data Cube (BDC) and used to create data cubes in the Brazilian territory by comparing Landsat-8 and Sentinel-2 surface reflectance products and estimated references
Landsat-8/OLI Land Surface Reflectance Code (LaSRC) surface reflectance products were compared to the surface reflectance reference generated using ARCSI
Summary
Free and open remote sensing data from different satellites and sensors systems are available to users around the world (Kuenzer et al, 2015). ARD are procedurally generated data that ensures consistency and interoperability from data acquisition to a level required by users, which can be TOA (Topof-atmosphere) reflectance, surface reflectance (SR) or other standardized data (Giuliani et al, 2017). In this context, EODC are a set of images with spatially aligned pixels and one temporal dimension containing a set of values from which time series can be extracted (Appel and Pebesma, 2019, Ferreira et al, 2020). Many initiatives started to create EODC from ARD, the Australian Data Cube (Lewis et al, 2017), Swiss Data Cube (Giuliani et al, 2017), Armenian Data Cube (Asmaryan et al, 2019), Catalan Data Cube (Maso et al, 2019), Africa Regional Data Cube (Killough, 2019) and more recently
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