Title:	SAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms.
Authors:	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.
Affiliation:	ANA P. S. G. D. D. TORO, UNIVERSIDADE ESTADUAL DE CAMPINAS; INACIO T. BUENO, UNIVERSIDADE ESTADUAL DE CAMPINAS; JOÃO PAULO SAMPAIO WERNER, UNIVERSIDADE ESTADUAL DE CAMPINAS; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; RUBENS AUGUSTO DE CAMARGO LAMPARELLI, UNIVERSIDADE ESTADUAL DE CAMPINAS; ALEXANDRE CAMARGO COUTINHO, CNPTIA; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; PAULO S. G. MAGALHÃES, UNIVERSIDADE ESTADUAL DE CAMPINAS; GLEYCE KELLY DANTAS ARAÚJO FIGUEIREDO, UNIVERSIDADE ESTADUAL DE CAMPINAS.
Date Issued:	2023
Citation:	Remote Sensing, v. 15, n. 4, 1130, Feb. 2023.
Description:	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.
Thesagro:	Agricultura
NAL Thesaurus:	Agriculture
Keywords:	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
DOI:	https://doi.org/10.3390/rs15041130
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPTIA)