Data/hora: 20/06/2024 01:45:09

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	21/09/2018	
Data da última atualização: 	07/01/2020	
Autoria: 	CHEN, Y.; LU, D.; MORAN, E.; BATISTELLA, M.; DUTRA, L. V.; DEL'ARCO SANCHES, I.; SILVA, R. F. B. da; HUANG, J.; LUIZ, A. J. B.; OLIVEIRA, M. A. F. de.	
Afiliação: 	YAOLINAG CHEN, Zhejiang Agriculture and Forestry University, Zhejiang University, Michigan State University; DENGSHENG LU, Zhejiang Agriculture and Forestry University, Michigan State University; EMILIO MORAN, Michigan State University; MATEUS BATISTELLA, CNPTIA, Unicamp; LUCIANO VIEIRA DUTRA, INPE; IARA DEL´ARCO SANCHES, INPE; RAMON FELIPE BICUDO DA SILVA, Unicamp; JINGFENG HUANG, Zhejiang University; ALFREDO JOSE BARRETO LUIZ, CNPMA; MARIA ANTONIA FALCAO DE OLIVEIRA, INPE.	
Título: 	Mapping croplands, cropping patterns, and crop types using MODIS time-series data.	
Ano de publicação: 	2018	
Fonte/Imprenta: 	International Journal of Applied Earth Observation and Geoinformation, v. 69, p. 133-147, July 2018.	
DOI: 	https://doi.org/10.1016/j.jag.2018.03.00	
Idioma: 	Inglês	
Conteúdo: 	Abstract: The importance of mapping regional and global cropland distribution in timely ways has been recognized, but separation of crop types and multiple cropping patterns is challenging due to their spectral similarity. This study developed a new approach to identify crop types (including soy, cotton and maize) and cropping patterns (Soy-Maize, Soy-Cotton, Soy-Pasture, Soy-Fallow, Fallow-Cotton and Single crop) in the state of Mato Grosso, Brazil. The Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data for 2015 and 2016 and field survey data were used in this research. The major steps of this proposed approach include: (1) reconstructing NDVI time series data by removing the cloud-contaminated pixels using the temporal interpolation algorithm, (2) identifying the best periods and developing temporal indices and phenological parameters to distinguish croplands from other land cover types, and (3) developing crop temporal indices to extract cropping patterns using NDVI time-series data and group cropping patterns into crop types. Decision tree classifier was used to map cropping patterns based on these temporal indices. Croplands from Landsat imagery in 2016, cropping pattern samples from field survey in 2016, and the planted area of crop types in 2015 were used for accuracy assessment. Overall accuracies of approximately 90%, 73% and 86%, respectively were obtained for croplands, cropping patterns, and crop types. The adjusted coefficients of determination of total crop, soy, maize, and cotton areas with corresponding statistical areas were 0.94, 0.94, 0.88 and 0.88, respectively. This research indicates that the proposed approach is promising for mapping large-scale croplands, their cropping patterns and crop types.	
Palavras-Chave: 	Crop types; Croplands; Cropping patterns; Decision tree classifier; Índice de vegetação; MODIS NDVI; Séries temporais.	
Thesagro: 	Algodão; Milho; Sensoriamento Remoto; Sistema de Informação Geográfica; Soja.	
Thesaurus Nal: 	Geographic information systems; Normalized difference vegetation index; Remote sensing; Time series analysis.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (19713-1) - UPC - AP - DD -  -  -  - DIS