{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T12:35:45Z","timestamp":1770726945507,"version":"3.49.0"},"reference-count":61,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T00:00:00Z","timestamp":1619740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["FPA 275\/G\/GRO\/COPE\/17\/10042"],"award-info":[{"award-number":["FPA 275\/G\/GRO\/COPE\/17\/10042"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007543","name":"Grantov\u00e1 Agentura, Univerzita Karlova","doi-asserted-by":"publisher","award":["GA UK No. 512217"],"award-info":[{"award-number":["GA UK No. 512217"]}],"id":[{"id":"10.13039\/100007543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study deals with a local incidence angle correction method, i.e., the land cover-specific local incidence angle correction (LC-SLIAC), based on the linear relationship between the backscatter values and the local incidence angle (LIA) for a given land cover type in the monitored area. Using the combination of CORINE Land Cover and Hansen et al.\u2019s Global Forest Change databases, a wide range of different LIAs for a specific forest type can be generated for each scene. The algorithm was developed and tested in the cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, Shuttle Radar Topography Mission (SRTM) digital elevation model, and CORINE Land Cover and Hansen et al.\u2019s Global Forest Change databases. The developed method was created primarily for time-series analyses of forests in mountainous areas. LC-SLIAC was tested in 16 study areas over several protected areas in Central Europe. The results after correction by LC-SLIAC showed a reduction of variance and range of backscatter values. Statistically significant reduction in variance (of more than 40%) was achieved in areas with LIA range &gt;50\u00b0 and LIA interquartile range (IQR) &gt;12\u00b0, while in areas with low LIA range and LIA IQR, the decrease in variance was very low and statistically not significant. Six case studies with different LIA ranges were further analyzed in pre- and post-correction time series. Time-series after the correction showed a reduced fluctuation of backscatter values caused by different LIAs in each acquisition path. This reduction was statistically significant (with up to 95% reduction of variance) in areas with a difference in LIA greater than or equal to 27\u00b0. LC-SLIAC is freely available on GitHub and GEE, making the method accessible to the wide remote sensing community.<\/jats:p>","DOI":"10.3390\/rs13091743","type":"journal-article","created":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T05:10:55Z","timestamp":1619759455000},"page":"1743","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Land Cover-Specific Local Incidence Angle Correction: A Method for Time-Series Analysis of Forest Ecosystems"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9738-938X","authenticated-orcid":false,"given":"Daniel","family":"Paluba","sequence":"first","affiliation":[{"name":"EO4Landscape Research Team, Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University, 128 43 Prague, Czech Republic"}]},{"given":"Josef","family":"La\u0161tovi\u010dka","sequence":"additional","affiliation":[{"name":"EO4Landscape Research Team, Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University, 128 43 Prague, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1813-4878","authenticated-orcid":false,"given":"Antonios","family":"Mouratidis","sequence":"additional","affiliation":[{"name":"EO4Landscape Research Team, Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University, 128 43 Prague, Czech Republic"},{"name":"Department of Physical and Environmental Geography, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0307-9688","authenticated-orcid":false,"given":"P\u0159emysl","family":"\u0160tych","sequence":"additional","affiliation":[{"name":"EO4Landscape Research Team, Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University, 128 43 Prague, Czech Republic"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5326","DOI":"10.1109\/JSTARS.2020.3021052","article-title":"Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review","volume":"13","author":"Amani","year":"2020","journal-title":"IEEE J. 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