{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:17:40Z","timestamp":1776885460424,"version":"3.51.2"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T00:00:00Z","timestamp":1644969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The monitoring of forest degradation in the Amazon through radar remote sensing methodologies has increased intensely in recent years. Synthetic aperture radar (SAR) sensors that operate in L-band have an interesting response for land use and land cover (LULC) as well as for aboveground biomass (AGB). Depending on the magnetic and solar activities and seasonality, plasma bubbles in the ionosphere appear in the equatorial and tropical regions; these factors can cause stripes across SAR images, which disturb the interpretation and the classification. Our article shows a methodology to filter these stripes using Fourier fast transform (FFT), in which a stop-band filter removes this noise. In order to make this possible, we used Environment for Visualizing Images (ENVI), Sentinel Application Platform (SNAP), and Interactive Data Language (IDL). The final filtered scenes were classified by random forest (RF), and the results of this classification showed superior performance compared to the original scenes, showing this methodology can help to recover historic series of L-band images.<\/jats:p>","DOI":"10.3390\/rs14040962","type":"journal-article","created":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T21:36:24Z","timestamp":1645047384000},"page":"962","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Removal of Ionospheric Effects from Sigma Naught Images of the ALOS\/PALSAR-2 Satellite"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4585-5067","authenticated-orcid":false,"given":"F\u00e1bio Furlan","family":"Gama","sequence":"first","affiliation":[{"name":"Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), S\u00e3o Jos\u00e9 dos Campos 12227-010, SP, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4002-0981","authenticated-orcid":false,"given":"Natalia Cristina","family":"Wiederkehr","sequence":"additional","affiliation":[{"name":"Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), S\u00e3o Jos\u00e9 dos Campos 12227-010, SP, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0247-8449","authenticated-orcid":false,"given":"Polyanna","family":"da Concei\u00e7\u00e3o Bispo","sequence":"additional","affiliation":[{"name":"Department of Geography, School of Environment, Education and Development, University of Manchester, Oxford Road, Manchester M13 9PL, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1126\/science.1120529","article-title":"Land use and climate change","volume":"310","author":"Pielke","year":"2005","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1126\/science.1111772","article-title":"Global consequences of land use","volume":"309","author":"Foley","year":"2005","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1038\/s41559-020-01368-x","article-title":"The Brazilian Amazon deforestation rate in 2020 is the greatest of the decade","volume":"5","author":"Junior","year":"2021","journal-title":"Nat. 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