Imputation of missing parts in UAV orthomosaics using PlanetScope and Sentinel-2 data: a case study in a grass-dominated área.

PEREIRA, F. R. da S.; REIS, A. A. dos; FREITAS, R. G.; OLIVEIRA, S. R. de M.; AMARAL, L. R. do; FIGUEIREDO, G. K. D. A.; ANTUNES, J. F. G.; LAMPARELLI, R. A. C.; MORO, E.; MAGALHÃES, P. S. G.

Imputation of missing parts in UAV orthomosaics using PlanetScope and Sentinel-2 data: a case study in a grass-dominated área.

Autores

PEREIRA, F. R. da S.

REIS, A. A. dos

FREITAS, R. G.

OLIVEIRA, S. R. de M.

AMARAL, L. R. do

FIGUEIREDO, G. K. D. A.

Descripción

In this study, we propose a methodological framework to impute missing parts of UAV orthomosaics using PlanetScope (PS) and Sentinel-2 (S2) data and the random forest (RF) algorithm of an integrated crop-livestock system (ICLS) covered by grass at the time.

Palabras clave

Índice de vegetação, Aprendizado de máquina, Random forest, Data intercalibration, Spatial gap-filling method, Spatial imputation method, Machine learning, Agricultura de Precisão, Sensoriamento Remoto, Precision agriculture, Remote sensing, Unmanned aerial vehicles, Normalized difference vegetation index