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

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

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.

Saved in:
Bibliographic Details
Main 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.
Other Authors: 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.
Format: Artigo de periódico biblioteca
Language:Ingles
English
Published: 2023-03-20
Subjects: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,
Online Access:http://www.alice.cnptia.embrapa.br/alice/handle/doc/1152495
https://doi.org/10.3390/rs15041130
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
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.

Similar Items