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		<isbn>978-85-17-00088-1</isbn>
		<label>60064</label>
		<citationkey>GracianiBrogRodr:2017:ApReNe</citationkey>
		<title>Aplicación de Red Neuronal Artificial sobre imágenes TerraSAR para determinar la humedad del suelo</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
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		<size>612 KiB</size>
		<author>Graciani, Silvio Daniel,</author>
		<author>Brogioni, Marco,</author>
		<author>Rodríguez, Leticia,</author>
		<electronicmailaddress>sgraciani@fich.unl.edu.ar</electronicmailaddress>
		<editor>Gherardi, Douglas Francisco Marcolino,</editor>
		<editor>Aragão, Luiz Eduardo Oliveira e Cruz de,</editor>
		<e-mailaddress>daniela.seki@inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Sensoriamento Remoto, 18 (SBSR)</conferencename>
		<conferencelocation>Santos</conferencelocation>
		<date>28-31 maio 2017</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>3648-3655</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>Soil moisture (SM) is a key variable that directly influences the redistribution of radiant energy , evapotranspiration, infiltration, etc. Knowing its spatial distribution is strategic for agricultural activities. The In situ measurements of SM are time consuming, providing only point information. Synthetic aperture radar  SAR images bring the possibility of estimating SM over extensive areas (about 40x40 km2), with adequate spatial and temporal coverage.Work was developed in a sector of the Castellanos Department - Province of Santa Fe, in the same HS maps were obtained from the application of an Artificial Neural Network (ANN) on a data set of satellite measurements and field. SevenX-band SAR images from the satellite TerraSAR were calibrated and processed. Soil moisture was measured in situ by means of a TDR probe, samples were taken from the field for gravimetric water content determination. Days for field campaigns were performed as close as possible to the image days. These measured data of SM were used to validate those obtained with ANN, showing consistency when compared.</abstract>
		<area>SRE</area>
		<type>Hidrologia</type>
		<language>es</language>
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