SAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms.

TORO, A. P. S. G. D. D.; BUENO, I. T.; WERNER, J. P. S.; ANTUNES, J. F. G.; LAMPARELLI, R. A. C.; COUTINHO, A. C.; ESQUERDO, J. C. D. M.; MAGALHÃES, P. S. G.; FIGUEIREDO, G. K. D. A.

SAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms.

Autores

TORO, A. P. S. G. D. D.

ESQUERDO, J. C. D. M.

MAGALHÃES, P. S. G.

FIGUEIREDO, G. K. D. A.

Descripción

In this work, we explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. To explore the scalability of the proposed methods, we tested them in two regions with different latitudes, cloud cover rates, field sizes, landscapes, and crop types. Finally, the potential of SAR (Sentinel-1) and optical (Sentinel-2) data was tested.

Palabras clave

Floresta aleatória, Agricultura regenerativa, Sistemas integrados lavoura-pecuária, Aprendizado de máquina, Aprendizado profundo, Regenerative agriculture, Random forest, Integrated Crop-livestock systems, ICLS, Long short-term memory, LSTM, Multisource, Transformer, Agricultura, Agriculture