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		<identifier>8JMKD3MGP6W34M/3PSMCLB</identifier>
		<repository>sid.inpe.br/marte2/2017/10.27.15.44.42</repository>
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		<isbn>978-85-17-00088-1</isbn>
		<label>59674</label>
		<citationkey>GrisDalMouRosFlo:2017:PrDaEs</citationkey>
		<title>Pré-processamento dos dados de espectroscopia na região do Vis-NIR melhoram a predição do carbono orgânico do solo?</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
		<numberoffiles>1</numberoffiles>
		<size>542 KiB</size>
		<author>Gris, Diego Jose,</author>
		<author>Dalmolin, Ricardo Simão Diniz,</author>
		<author>Moura-Bueno, Jean Michel,</author>
		<author>Rosin, Nicolas Augusto,</author>
		<author>Flores, João Pedro Moro,</author>
		<electronicmailaddress>diegojgris@gmail.com</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>6304-6311</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>Soil is one of the most important carbon (C) reservoirs in the environment, acting as a sink or source of C to the atmosphere, depending on its management. Therefore, data on soil organic carbon (SOC) is important for managing C in the ecosystem, but traditional methods for mapping SOC are highly demanding in time and resources. In this scenario, diffuse reflectance spectroscopy (DRS) emerges as a more efficient technique for SOC assessment. This study evaluates preprocessing techniques for soil spectra and models for SOC prediction. The study was conducted at Giruá, State of Rio Grande do Sul (RS), Brazil, where 841 soil samples were collected at 261 sites, in an area of 940 ha. SOC was determined analytically through wet combustion. Soil spectra were obtained using a FieldSpec Pro spectroradiometer. Preprocessing techniques included: smoothing (SMO), continuum removal (CRR), standard normal variate (SNV), detrend (DET), Savitzky-Golay first derivative (SGD), and Savitzky-Golay first derivative after transformation to absorbance (ASG). The samples were divided in training (70%) and validation (30%) sets. Two models were tested for SOC prediction: partial least squares regression (PLSR) and random forest (RF). SMO was the best preprocessing technique for PLSR, while SGD performed best for RF. PLSR had the best performance for SOC prediction (Rv2 = 0.72; RMSEv = 0.52% and RPIQv = 2.23) when compared to RF (Rv2 = 0.71; RMSEv = 0.53% and RPIQv = 2.20).</abstract>
		<area>SRE</area>
		<type>Solos e umidade do solo</type>
		<language>pt</language>
		<targetfile>59674.pdf</targetfile>
		<usergroup>banon</usergroup>
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		<citingitemlist>sid.inpe.br/marte2/2017/09.25.14.55 2</citingitemlist>
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		<url>http://marte2.sid.inpe.br/rep-/sid.inpe.br/marte2/2017/10.27.15.44.42</url>
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