01669nam a2200337 a 450000100080000000500110000800800410001902000220006010000220008224501320010426001350023630000120037152006150038365000180099865000150101665000240103165000240105565000120107965000220109165000140111365000090112765300160113665300220115270000190117470000230119370000300121670000230124670000210126970000210129070000200131121191202020-01-21       2019    bl uuuu     u00u1 u    #d  a978-85-17-00097-31 aPADILHA, M. C. C.  aPrediction statistical model for soil organic carbon mapping in crop areas using the Landsat/OLI sensor.h[electronic resource]  aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 19., 2019, Santos. Anais... São José dos Campos: INPE, 2019. Ref. 96042.c2019  ap. 1-4.  aAbstract: 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.  aLinear models  aPrediction  aRegression analysis  aSoil organic carbon  aCarbono  aRegressão Linear  aSatélite  aSolo  aLandsat OLI  aLinear regression1 aVICENTE, L. E.1 aDEMATTÊ, J. A. M.1 aLOEBMANN, D. G. dos S. W.1 aURBINA SALAZAR, D.1 aKOGA-VICENTE, A.1 aARAUJO, L. S. de1 aMANZATTO, C. V.