Title:	Prediction statistical model for soil organic carbon mapping in crop areas using the Landsat/OLI sensor.
Authors:	PADILHA, M. C. C. VICENTE, L. E. DEMATTÊ, J. A. M. LOEBMANN, D. G. dos S. W. URBINA SALAZAR, D. KOGA-VICENTE, A. ARAUJO, L. S. de MANZATTO, C. V.
Affiliation:	MANUELA CORRÊA DE CASTRO PADILHA, ESALQ-USP; LUIZ EDUARDO VICENTE, CNPMA; JOSÉ ALEXANDRE MELO DEMATTÊ, ESALQ-USP; DANIEL GOMES DOS SANTOS W LOEBMANN, CNPMA; DIEGO URBINA SALAZAR, ESALQ-USP; ANDREA KOGA-VICENTE; LUCIANA SPINELLI DE ARAUJO, CNPMA; CELSO VAINER MANZATTO, CNPMA.
Date Issued:	2019
Citation:	In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 19., 2019, Santos. Anais... São José dos Campos: INPE, 2019. Ref. 96042.
Pages:	p. 1-4.
Description:	Abstract: The quantification of soil organic carbon (SOC) is essential to agriculture and sustainable use of the land. However, there are difficulties to estimate it in large areas due to high cost of soil sample extraction, and laboratory preparations. There are approaches that may facilitate the estimation of SOC, such as the use of satellite imagery and the application of statistical models based on the spectral bands of the satellite under study. In July of 2017, this study proposed a prediction statistical model from optical-orbital data of the series Landsat, OLI sensor for estimating SOC content.
Thesagro:	Carbono Solo Regressão Linear Satélite
NAL Thesaurus:	Soil organic carbon Prediction Regression analysis Linear models
Keywords:	Linear regression Landsat OLI
ISBN:	978-85-17-00097-3
Type of Material:	Artigo em anais e proceedings
Access:	openAccess
Appears in Collections:	Artigo em anais de congresso (CNPMA)