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		<identifier>8JMKD3MGPDW34P/4ADCQBS</identifier>
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		<issn>2179-4847</issn>
		<citationkey>NascimentoBaueCalhRizz:2023:EsTrLí</citationkey>
		<title>Estimativa da troca líquida de carbono a partir dos produtos MODIS e dados meteorológicos aplicados a modelos de aprendizado de máquina</title>
		<format>On-line.</format>
		<year>2023</year>
		<secondarytype>PRE CN</secondarytype>
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		<author>Nascimento, Aline Andrade do,</author>
		<author>Bauer, Lucas de Oliveira,</author>
		<author>Calheiros, Alan James Peixoto,</author>
		<author>Rizzo, Luciana V.,</author>
		<group>CAP-COMP-DIPGR-INPE-MCTI-GOV-BR</group>
		<group>CAP-COMP-DIPGR-INPE-MCTI-GOV-BR</group>
		<group>COPDT-CGIP-INPE-MCTI-GOV-BR</group>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Universidade de São Paulo (USP)</affiliation>
		<electronicmailaddress>aline.andrade@inpe.br</electronicmailaddress>
		<electronicmailaddress>lucas.bauer@inpe.br</electronicmailaddress>
		<electronicmailaddress>alan.calheiros@inpe.br</electronicmailaddress>
		<electronicmailaddress>lrizzo@usp.br</electronicmailaddress>
		<editor>Vinhas, Lubia (INPE),</editor>
		<editor>Feitosa, Flavia F. (UFABC),</editor>
		<conferencename>Simpósio Brasileiro de Geoinformática, 24 (GEOINFO)</conferencename>
		<conferencelocation>On-line</conferencelocation>
		<date>04 a 06 dez. 2023</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<booktitle>Anais</booktitle>
		<tertiarytype>Short paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<abstract>The Fluxcom project employed machine learning and surface data to estimate the global carbon balance. Nevertheless, these estimates are less accurate in tropical regions, including the Amazon. This study focuses on estimating the net ecosystem exchange (NEE) in a 0.25° cell located at the K67 Tower in the Tapajós National Forest, Santarém. We use data from the ERA-5 reanalysis model, MODIS products, and the BrSa1 Fluxnet tower. The data from the former sets were used to train machine learning models, while the latter served as the target (NEE) for estimating its time series from 2002 to 2011. The estimation results closely matched those of the Fluxcom project. RESUMO: O projeto Fluxcom usou machine learning e dados de superfície para estimar o balanço de carbono global, porém as estimativas são menos precisas em regiões tropicais, incluindo a Amazônia. Este trabalho foca na estimativa da troca líquida de carbono (NEE) em uma célula de 0.25° junto à Torre K67, na Floresta Nacional dos Tapajós, Santarém. Utilizaram-se dados do modelo de reanálise ERA-5, produtos do MODIS e da torre de fluxo BrSa1 da Fluxnet. Os dados dos primeiros conjuntos foram usados para treinar modelos de machine learning, enquanto os últimos foram utilizados como alvo (NEE) para estimar sua série temporal de 2002 a 2011. Os resultados da estimativa se aproximaram dos da Fluxcom.</abstract>
		<area>SRE</area>
		<language>pt</language>
		<targetfile>Nascimento_estimativa.pdf</targetfile>
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