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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Mapping croplands, cropping patterns, and crop types using MODIS time-series data

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Autor(es):
Chen, Yaoliang [1, 2, 3] ; Lu, Dengsheng [1, 2] ; Moran, Emilio [1] ; Batistella, Mateus [4, 5] ; Dutra, Luciano Vieira [6] ; Sanches, Ieda Del'Arco [6] ; Bicudo da Silva, Ramon Felipe [5] ; Huang, Jingfeng [3] ; Barreto Luiz, Alfredo Jose [4] ; Falcao de Oliveira, Maria Antonia [6]
Número total de Autores: 10
Afiliação do(s) autor(es):
[1] Michigan State Univ, Ctr Global Change & Earth Observat, E Lansing, MI 48824 - USA
[2] Zhejiang Agr & Forestry Univ, Sch Environm & Resource Sci, State Key Lab Subtrop Silviculture, Hangzhou 311300, Zhejiang - Peoples R China
[3] Zhejiang Univ, Coll Environm & Resource Sci, Inst Appl Remote Sensing & Informat Technol, Hangzhou, Zhejiang - Peoples R China
[4] Brazilian Agr Res Corp EMBRAPA, Brasilia, DF - Brazil
[5] Univ Estadual Campinas, Ctr Environm Studies & Res, Campinas, SP - Brazil
[6] Natl Inst Space Res, Sao Jose Dos Campos, SP - Brazil
Número total de Afiliações: 6
Tipo de documento: Artigo Científico
Fonte: International Journal of Applied Earth Observation and Geoinformation; v. 69, p. 133-147, JUL 2018.
Citações Web of Science: 17
Resumo

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. (AU)

Processo FAPESP: 15/25892-7 - Segurança alimentar e uso da terra: o desafio do Telecoupling
Beneficiário:Ramon Felipe Bicudo da Silva
Linha de fomento: Bolsas no Brasil - Pós-Doutorado
Processo FAPESP: 14/50628-9 - Segurança alimentar e uso da terra: o desafio do Telecoupling
Beneficiário:Mateus Batistella
Linha de fomento: Auxílio à Pesquisa - Temático