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		<doi>10.1109/LGRS.2017.2789120</doi>
		<issn>1545-598X</issn>
		<citationkey>SanchesFeDiSoLuScMa:2018:SeImAg</citationkey>
		<title>Campo Verde database: seeking to improve agricultural remote sensing of tropical areas</title>
		<year>2018</year>
		<month>Mar.</month>
		<typeofwork>journal article</typeofwork>
		<secondarytype>PRE PI</secondarytype>
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		<author>Sanches, Ieda Del'Arco,</author>
		<author>Feitosa, Raul Queiroz,</author>
		<author>Diaz, Pedro Marco Achanccaray,</author>
		<author>Soares, Marinalva Dias,</author>
		<author>Luiz, Alfredo José Barreto,</author>
		<author>Schultz, Bruno,</author>
		<author>Maurano, Luis Eduardo Pinheiro,</author>
		<orcid></orcid>
		<orcid>0000-0001-8344-5096</orcid>
		<group>DIDSR-CGOBT-INPE-MCTIC-GOV-BR</group>
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		<group></group>
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		<group></group>
		<group>DIDSR-CGOBT-INPE-MCTIC-GOV-BR</group>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)</affiliation>
		<affiliation>Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)</affiliation>
		<affiliation>Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)</affiliation>
		<affiliation>Embrapa</affiliation>
		<affiliation>Geoambiente</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<electronicmailaddress></electronicmailaddress>
		<electronicmailaddress>raul@ele.puc-rio.br</electronicmailaddress>
		<electronicmailaddress>pmad9589@ele.puc-rio.br</electronicmailaddress>
		<electronicmailaddress>mdiasoares@gmail.com</electronicmailaddress>
		<electronicmailaddress>alfredo.luiz@embrapa.br</electronicmailaddress>
		<electronicmailaddress>schultz.florestal@gmail.com</electronicmailaddress>
		<electronicmailaddress>luis.maurano@inpe.br</electronicmailaddress>
		<journal>IEEE Geoscience and Remote Sensing Letters</journal>
		<volume>15</volume>
		<number>3</number>
		<pages>369-373</pages>
		<secondarymark>A1_GEOGRAFIA A1_ENGENHARIAS_IV A2_INTERDISCIPLINAR A2_GEOCIÊNCIAS B1_CIÊNCIA_DA_COMPUTAÇÃO B2_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA B2_BIOTECNOLOGIA B3_ASTRONOMIA_/_FÍSICA C_CIÊNCIAS_AGRÁRIAS_I</secondarymark>
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		<contenttype>External Contribution</contenttype>
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		<keywords>Agricultural mapping/monitoring, double cropping systems, free available database, remote sensing, synthetic aperture radar (SAR), tropical agriculture.</keywords>
		<abstract>In tropical/subtropical regions, the favorable climate associated with the use of agricultural technologies, such as no tillage, minimum cultivation, irrigation, early varieties, desiccants, flowering inducing, and crop rotation, makes agriculture highly dynamic. In this letter, we present the Campo Verde agricultural database. The purpose of creating and sharing these data is to foster advancement of remote sensing technology in areas of tropical agriculture, primarily the development and testing of methods for crop recognition and agricultural mapping. Campo Verde is a municipality of Mato Grosso state, localized in the Cerrado (Brazilian Savanna) biome, in central west Brazil. Soybean, maize, and cotton are the primary crops cultivated in this region. Double cropping systems are widely adopted in this area. There is also livestock and forestry production. Our database provides the land-use classes for 513 fields by month for one Brazilian crop year (between October 2015 and July 2016). This information was gathered during two field campaigns in Campo Verde (December 2015 and May 2016) and by visual interpretation of a time series of Landsat-8/Operational Land Imager (OLI) images using an experienced interpreter. A set of 14 preprocessed synthetic aperture radar Sentinel-1 and 15 Landsat-8/OLI mosaic images is also made available. It is important to promote the use of radar data for tropical agricultural applications, especially because the use of optical remote sensing in these regions is hindered by the high frequency of cloud cover. To demonstrate the utility of our database, results of an experiment conducted using the Sentinel-1 data set are presented.</abstract>
		<area>SRE</area>
		<language>en</language>
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		<notes>Prêmio CAPES Elsevier 2023 - ODS 2: Fome zero e Agricultura sustentável</notes>
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