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The use of Pseudoinvariant Areas (PIA) makes it possible to carry out a reasonably robust\udand automatic radiometric correction for long time series of remote sensing imagery, as shown\udin previous studies for large data sets of Landsat MSS, TM, and ETM+ imagery. In addition,\udthey can be employed to obtain more coherence among remote sensing data from different sensors.\udThe present work validates the use of PIA for the radiometric correction of pairs of images acquired\udalmost simultaneously (Landsat-7 (ETM+) or Landsat-8 (OLI) and Sentinel-2A (MSI)). Four pairs\udof images from a region in SW Spain, corresponding to four different dates, together with field\udspectroradiometry measurements collected at the time of satellite overpass were used to evaluate a\udPIA-based radiometric correction. The results show a high coherence between sensors (r2 = 0.964) and\udexcellent correlations to in-situ data for the MiraMon implementation (r2 > 0.9). Other methodological\udalternatives, ATCOR3 (ETM+, OLI, MSI), SAC-QGIS (ETM+, OLI, MSI), 6S-LEDAPS (ETM+),\ud6S-LaSRC (OLI), and Sen2Cor-SNAP (MSI), were also evaluated. Almost all of them, except\udfor SAC-QGIS, provided similar results to the proposed PIA-based approach. Moreover, as the\udPIA-based approach can be applied to almost any image (even to images lacking of extra atmospheric\udinformation), it can also be used to solve the robust integration of data from new platforms, such as\udLandsat-8 or Sentinel-2, to enrich global data acquired since 1972 in the Landsat program. It thus\udcontributes to the program’s continuity, a goal of great interest for the environmental, scientific,\udand technical communityPeer reviewe

remote sensing

Radiometric Correction of Simultaneously Acquired Landsat-7/Landsat-8 and Sentinel-2A Imagery Using Pseudoinvariant Areas (PIA): Contributing to the Landsat Time Series Legacy