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| 11. |  | ARVOR, D.; JONATHAN, M.; MEIRELLES, M. S. P.; DUBREUIL, V. Detecting outliers and asserting consistency in agriculture ground truth information by using temporal VI data from modis. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 37, pt. B7, p. 1031-1036, 2008. Edition of Proceedings of XXI ISPRS Congress, Beijing, Jul. 2008.| Biblioteca(s): Embrapa Solos. |
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| 19. | | MEIRELLES, M. S. P.; FREITAS, P. L. de; FERRAZ, R. P. D.; ARVOR, D.; DUBREUIL, V. Avaliação da dinâmica de uso da terra por meio de dados de sensores remotos para uma agricultura sustentável. In: LEITE, L. F. C.; MACIEL, G. A.; ARAÚJO, A. S. F. de (Ed.). Agricultura conservacionista no Brasil. Brasília, DF: Embrapa, 2014. pt 5, cap. 6, p. 489-511.| Biblioteca(s): Embrapa Solos. |
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| Registros recuperados : 76 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
14/02/2014 |
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Data da última atualização: |
20/09/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 3 |
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Autoria: |
MULIANGA, B.; BÉGUÈ, A.; MEIRELLES, M. S. P.; TODOROFF, P. |
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Afiliação: |
Betty Mulianga; Agnès Bégué; MARGARETH SIMOES P MEIRELLES, CNPS; Pierre Todoroff. |
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Título: |
Forecasting regional sugarcane yield based on time integral and spatial aggregation of MODIS NDVI. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
Remote Sensing, v. 5, n. 5, p. 2184-2199, 2013. |
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DOI: |
https://doi.org/10.3390/rs5052184 |
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Idioma: |
Inglês |
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Conteúdo: |
This study explored the suitability of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectrometer (MODIS) obtained for six sugar management zones, over nine years (2002?2010), to forecast sugarcane yield on an annual and zonal base. To take into account the characteristics of the sugarcane crop management (15-month cycle for a ratoon, accompanied with continuous harvest in Western Kenya), the temporal series of NDVI was normalized through an original weighting method that considered the growth period of the sugarcane crop (wNDVI), and correlated it with historical yield datasets. Results when using wNDVI were consistent with historical yield and significant at P-value = 0.001, while results when using traditional annual NDVI integrated over the calendar year were not significant. This correlation between yield and wNDVI is mainly drawn by the spatial dimension of the data set (R2 = 0.53, when all years are aggregated together), rather than by the temporal dimension of the data set (R2 = 0.1, when all zones are aggregated). A test on 2012 yield estimation with this model realized a RMSE less than 5 t·ha?1. Despite progress in the methodology through the weighted NDVI, and an extensive spatio-temporal analysis, this paper shows the difficulty in forecasting sugarcane yield on an annual base using current satellite low-resolution data. This is particularly true in the context of small scale farmers with fields measuring less than the size of MODIS 250 m pixel, and in the context of a 15-month crop cycle with no seasonal cropping calendar. Future satellite missions should permit monitoring of sugarcane yields using image resolutions that facilitate extraction of crop phenology from a group of individual plots. MenosThis study explored the suitability of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectrometer (MODIS) obtained for six sugar management zones, over nine years (2002?2010), to forecast sugarcane yield on an annual and zonal base. To take into account the characteristics of the sugarcane crop management (15-month cycle for a ratoon, accompanied with continuous harvest in Western Kenya), the temporal series of NDVI was normalized through an original weighting method that considered the growth period of the sugarcane crop (wNDVI), and correlated it with historical yield datasets. Results when using wNDVI were consistent with historical yield and significant at P-value = 0.001, while results when using traditional annual NDVI integrated over the calendar year were not significant. This correlation between yield and wNDVI is mainly drawn by the spatial dimension of the data set (R2 = 0.53, when all years are aggregated together), rather than by the temporal dimension of the data set (R2 = 0.1, when all zones are aggregated). A test on 2012 yield estimation with this model realized a RMSE less than 5 t·ha?1. Despite progress in the methodology through the weighted NDVI, and an extensive spatio-temporal analysis, this paper shows the difficulty in forecasting sugarcane yield on an annual base using current satellite low-resolution data. This is particularly true in the context of small scale farmers with fields measuring less than the siz... Mostrar Tudo |
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Palavras-Chave: |
MODIS; NDVI; Yield forecasting. |
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Thesaurus NAL: |
environment; sugarcane. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/97415/1/remotesensing-05-02184-published-abril-2013.pdf
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Marc: |
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024 7 $ahttps://doi.org/10.3390/rs5052184$2DOI
100 1 $aMULIANGA, B.
245 $aForecasting regional sugarcane yield based on time integral and spatial aggregation of MODIS NDVI.$h[electronic resource]
260 $c2013
520 $aThis study explored the suitability of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectrometer (MODIS) obtained for six sugar management zones, over nine years (2002?2010), to forecast sugarcane yield on an annual and zonal base. To take into account the characteristics of the sugarcane crop management (15-month cycle for a ratoon, accompanied with continuous harvest in Western Kenya), the temporal series of NDVI was normalized through an original weighting method that considered the growth period of the sugarcane crop (wNDVI), and correlated it with historical yield datasets. Results when using wNDVI were consistent with historical yield and significant at P-value = 0.001, while results when using traditional annual NDVI integrated over the calendar year were not significant. This correlation between yield and wNDVI is mainly drawn by the spatial dimension of the data set (R2 = 0.53, when all years are aggregated together), rather than by the temporal dimension of the data set (R2 = 0.1, when all zones are aggregated). A test on 2012 yield estimation with this model realized a RMSE less than 5 t·ha?1. Despite progress in the methodology through the weighted NDVI, and an extensive spatio-temporal analysis, this paper shows the difficulty in forecasting sugarcane yield on an annual base using current satellite low-resolution data. This is particularly true in the context of small scale farmers with fields measuring less than the size of MODIS 250 m pixel, and in the context of a 15-month crop cycle with no seasonal cropping calendar. Future satellite missions should permit monitoring of sugarcane yields using image resolutions that facilitate extraction of crop phenology from a group of individual plots.
650 $aenvironment
650 $asugarcane
653 $aMODIS
653 $aNDVI
653 $aYield forecasting
700 1 $aBÉGUÈ, A.
700 1 $aMEIRELLES, M. S. P.
700 1 $aTODOROFF, P.
773 $tRemote Sensing$gv. 5, n. 5, p. 2184-2199, 2013.
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