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

JOÃO P. S. WERNER, UNIVERSIDADE ESTADUAL DE CAMPINAS; MARIANA BELGIU, UNIVERSITY OF TWENTE; INACIO T. BUENO, UNIVERSIDADE ESTADUAL DE CAMPINAS; ALINY A. DOS REIS, UNIVERSIDADE ESTADUAL DE CAMPINAS; ANA P. S. G. D. TORO, UNIVERSIDADE ESTADUAL DE CAMPINAS; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; ALFRED STEIN, UNIVERSITY OF TWENTE; RUBENS A. C. LAMPARELLI, UNIVERSIDADE ESTADUAL DE CAMPINAS; PAULO S. G. MAGALHÃES, UNIVERSIDADE ESTADUAL DE CAMPINAS; ALEXANDRE CAMARGO COUTINHO, CNPTIA; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; GLEYCE K. D. A. FIGUEIREDO, UNIVERSIDADE ESTADUAL DE CAMPINAS.2026-07-07http://hdl.handle.net/123456789/456817The 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.openAccessFusão de dadosICLSAgricultura regenerativaSistemas integrados lavoura-pecuáriaAprendizado profundoData fusionMulti-sensorTempCNNTemporal encoderRegenerative agricultureDeep learningMapping integrated crop–livestock systems using fused Sentinel-2 and PlanetScope time series and deep learning.Artigo de periódico