Título:	Evaluation of early season mapping of integrated crop livestock systems using Sentinel-2 data.
Autoria:	TORO, A. P. S. G. D. WERNER, J. P. S. REIS, A. A. dos ESQUERDO, J. C. D. M. ANTUNES, J. F. G. COUTINHO, A. C. LAMPARELLI, R. A. C. MAGALHÃES, P. S. G. FIGUEIREDO, G. K. D. A.
Afiliação:	FEAGRI/UNICAMP; FEAGRI/UNICAMP; UNICAMP; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA, FEAGRI/UNICAMP; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; UNICAMP; UNICAMP; FEAGRI/UNICAMP.
Ano de publicação:	2022
Referência:	The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 43, B3, p. 1335-1340, 2022.
Conteúdo:	ABSTRACT. Various approaches were developed considering the need to increase agricultural productivity in cultivated areas without more deforestation, such as the Integrated Crop livestock systems (ICLS). The ICLS could be composed of annual crops followed by pastureland with the presence of cattle. Due to the high temporal dynamic of rotation between crops over the season, monitoring these areas is a big challenge. Also, agricultural organizations worldwide highlight the need for early-season maps for this kind of work. In this context, this study evaluated the potential of open data (Sentinel-2) data to map ICLS areas. The performance of two classifiers was evaluated: one of Machine Learning (random forest) and the other of Deep Learning (LSTM). Three different time windows of data were tested (Entire season, 180 days, and 120 days). Using the RF classifier, it was possible to achieve satisfactory results (Overall accuracy higher than 80%) for the early season (180 days). However, further studies are needed to explain better the lower(when compared to Random Forest) accuracy achieved by LSTM net (0.79 % for 180 days) and compare the results achieved here with results for a study area with different rates of cloud cover.
Thesagro:	Sensoriamento Remoto
NAL Thesaurus:	Remote sensing
Palavras-chave:	Agricultura regenerativa Identificação de culturas Floresta aleatória Aprendizado profundo LSTM Regenerative agriculture Crop identification Random forest
Digital Object Identifier:	https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1335-2022
Notas:	Edition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France.
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em anais de congresso (CNPTIA)