Prediction statistical model for soil organic carbon mapping in crop areas using the Landsat/OLI sensor.

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Author(s): 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.

Summary: 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.

Publication year: 2019

Types of publication: Paper in annals and proceedings

Unit: Embrapa Environment

Keywords: Carbono, Landsat OLI, Linear models, Linear regression, Prediction, Regression analysis, Regressão Linear, Satélite, Soil organic carbon, Solo

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