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Supporting such applications at medium to high spatial resolution may be challenging with a single optical satellite sensor, as the frequency of good-quality observations can be low. To optimize good-quality data availability, some studies propose harmonized databases. This work aims at developing an \u2018all-in-one\u2019 Google Earth Engine (GEE) web-based workflow to produce harmonized surface reflectance data from Landsat-7 (L7) ETM+, Landsat-8 (L8) OLI, and Sentinel-2 (S2) MSI top of atmosphere (TOA) reflectance data. Six major processing steps to generate a new source of near-daily Harmonized Landsat and Sentinel (HLS) reflectance observations at 30 m spatial resolution are proposed and described: band adjustment, atmospheric correction, cloud and cloud shadow masking, view and illumination angle adjustment, co-registration, and reprojection and resampling. The HLS is applied to six equivalent spectral bands, resulting in a surface nadir BRDF-adjusted reflectance (NBAR) time series gridded to a common pixel resolution, map projection, and spatial extent. The spectrally corresponding bands and derived Normalized Difference Vegetation Index (NDVI) were compared, and their sensor differences were quantified by regression analyses. Examples of HLS time series are presented for two potential applications: agricultural and forest phenology. The HLS product is also validated against ground measurements of NDVI, achieving very similar temporal trajectories and magnitude of values (R2 = 0.98). The workflow and script presented in this work may be useful for the scientific community aiming at taking advantage of multi-sensor harmonized time series of optical data.<\/jats:p>","DOI":"10.3390\/rs16152695","type":"journal-article","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T14:26:50Z","timestamp":1721744810000},"page":"2695","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Harmonized Landsat and Sentinel-2 Data with Google Earth Engine"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0220-5048","authenticated-orcid":false,"given":"Elias Fernando","family":"Berra","sequence":"first","affiliation":[{"name":"Department of Geography, Federal University of Paran\u00e1, Curitiba 81530-990, Brazil"},{"name":"Department of Forage Plants and Agrometeorology, Federal University of Rio Grande do Sul, Porto Alegre 91540-000, Brazil"}]},{"given":"Denise Cybis","family":"Fontana","sequence":"additional","affiliation":[{"name":"Department of Forage Plants and Agrometeorology, Federal University of Rio Grande do Sul, Porto Alegre 91540-000, Brazil"}]},{"given":"Feng","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Geography, University College London, London WC1E 6BT, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0405-9603","authenticated-orcid":false,"given":"Fabio Marcelo","family":"Breunig","sequence":"additional","affiliation":[{"name":"Department of Geography, Federal University of Paran\u00e1, Curitiba 81530-990, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111685","DOI":"10.1016\/j.rse.2020.111685","article-title":"Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery","volume":"240","author":"Bolton","year":"2020","journal-title":"Remote Sens. 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