Optimum combination of spectral variables for crop mapping in heterogeneous landscapes based on Sentinel-2 time series and machine learning.

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Author(s): OLIVEIRA JÚNIOR, J. G. de; ESQUERDO, J. C. D. M.; LAMPARELLI, R. A. C.

Summary: This article aimed to determine a workflow for more efficient large-scale crop mapping using a time series of images from the Sentinel-2 Satellite, statistical methods of attribute selection, and machine learning. The proposed methodology explores the best possible combination of spectral variables related to vegetation (16 vegetation indices in the RGB, NIR, SWIR, and Red Edge regions) to characterize different spectro-temporal profiles of Land Use and Land Cover (LULC) in spatially heterogeneous landscapes.

Publication year: 2024

Types of publication: Journal article

Unit: Embrapa Digital Agriculture

Keywords: Agricultural monitoring, Aprendizado de máquina, Cobertura da terra, Land cover, Land use, Monitoramento agrícola, Random forest, Red Edge, Remote sensing, SITS, Sensoriamento Remoto, Séries temporais, Time series analysis, Uso da Terra

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