Mapping integrated crop–livestock systems using fused Sentinel-2 and PlanetScope time series and deep learning.

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

Summary: The main objective of this research was to develop a method for mapping ICLS using deep learning algorithms applied on Satellite Image Time Series (SITS) data cubes, which consist of Sentinel-2 (S2) and PlanetScope (PS) satellite images, as well as data fused (DF) from both sensors. This study focused on two Brazilian states with varying landscapes and field sizes.

Publication year: 2024

Types of publication: Journal article

Unit: Embrapa Digital Agriculture

Keywords: Agricultura regenerativa, Aprendizado profundo, Data fusion, Deep learning, Fusão de dados, ICLS, Multi-sensor, Regenerative agriculture, Sistemas integrados lavoura-pecuária, TempCNN, Temporal encoder

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