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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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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: |
LEADER 02447naa a2200229 a 4500
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005 2021-09-20
008 2013 bl uuuu u00u1 u #d
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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Registro original: |
Embrapa Solos (CNPS) |
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Biblioteca(s): |
Embrapa Uva e Vinho. |
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Data corrente: |
03/01/2013 |
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Data da última atualização: |
10/11/2015 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
CARGNINO, C.; SÁ, A. A. de; LIMA, C. M. de; SARAIVA, M. D.; NACHTIGALL, G. R.; ERNANI, P. R. |
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Afiliação: |
CAMILA CARGNINO, UDESC; ALESSANDRA APARECIDA DE SÁ, UDESC; CHRISTIANO MIGNONI DE LIMA, UCS; MURILO DAMIANI SARAIVA, UCS; GILMAR RIBEIRO NACHTIGALL, CNPUV; PAULO ROBERTO ERNANI, UDESC. |
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Título: |
Crescimento de frutos de macieira Maxigala submetida a tratamentos de irrigação e fertirrigação. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE FRUTICULTURA, 22., 2012, Bento Gonçalves. Anais... Bento Gonçalves: SBF, 2012. |
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Páginas: |
p. 6142-6145. |
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Idioma: |
Português |
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Palavras-Chave: |
Maxigala. |
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Thesagro: |
Crescimento; Fertirrigação; Fruticultura; Irrigação; Maçã; Mudança Climática; Nutrição vegetal. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/73164/1/CARGNINO-22CBF-2012.pdf
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Marc: |
LEADER 00837nam a2200265 a 4500
001 1944002
005 2015-11-10
008 2012 bl uuuu u00u1 u #d
100 1 $aCARGNINO, C.
245 $aCrescimento de frutos de macieira Maxigala submetida a tratamentos de irrigação e fertirrigação.$h[electronic resource]
260 $aIn: CONGRESSO BRASILEIRO DE FRUTICULTURA, 22., 2012, Bento Gonçalves. Anais... Bento Gonçalves: SBF$c2012
300 $ap. 6142-6145.
650 $aCrescimento
650 $aFertirrigação
650 $aFruticultura
650 $aIrrigação
650 $aMaçã
650 $aMudança Climática
650 $aNutrição vegetal
653 $aMaxigala
700 1 $aSÁ, A. A. de
700 1 $aLIMA, C. M. de
700 1 $aSARAIVA, M. D.
700 1 $aNACHTIGALL, G. R.
700 1 $aERNANI, P. R.
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