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

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Author(s): 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.

Summary: 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.

Publication year: 2023

Types of publication: Journal article

Unit: Embrapa Digital Agriculture

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

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