Optimum combination of spectral variables for crop mapping in heterogeneous landscapes based on Sentinel-2 time series and machine learning.
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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.
Edition of proceedings of the ISPRS TC III mid-term symposium “Beyond the canopy: technologies and applications of remote sensing”, Belém, Brazil, 2024.
Edition of proceedings of the ISPRS TC III mid-term symposium “Beyond the canopy: technologies and applications of remote sensing”, Belém, Brazil, 2024.
Palabras clave
Monitoramento agrícola, Séries temporais, Aprendizado de máquina, Cobertura da terra, Agricultural monitoring, Random forest, SITS, Red Edge, Sensoriamento Remoto, Uso da Terra, Time series analysis, Remote sensing, Land cover, Land use