Remote Sens. 2013, 5(5), 2184-2199; doi:10.3390/rs5052184
Article

Forecasting Regional Sugarcane Yield Based on Time Integral and Spatial Aggregation of MODIS NDVI

1 Kenya Sugar Research Foundation (KESREF), Kisumu-Miwani Road, P.O Box 44, Kisumu 40100, Kenya 2 CIRAD-UMR TETIS, Maison de la Télédétection, 500 rue J.-F. Breton, F-34093 Montpellier, France 3 CIRAD-UPR SCA, Av. Agropolis, F-34398 Montpellier Cedex 5, France 4 EMBRAPA-Programa LabEx Europa and Rio de Janeiro State University PPGMA/DESC/UERJ, Agropolis International, Av. Agropolis, F-34094 Montpellier, France 5 CIRAD UPR SCA, Station de Ligne-Paradis, 7 chemin de l'IRAT, Saint-Pierre, F-97410 Réunion, France
* Author to whom correspondence should be addressed.
Received: 15 March 2013; in revised form: 26 April 2013 / Accepted: 26 April 2013 / Published: 10 May 2013
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
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Abstract: 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.
Keywords: MODIS; NDVI; environment; sugarcane; yield forecasting

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MDPI and ACS Style

Mulianga, B.; Bégué, A.; Simoes, M.; Todoroff, P. Forecasting Regional Sugarcane Yield Based on Time Integral and Spatial Aggregation of MODIS NDVI. Remote Sens. 2013, 5, 2184-2199.

AMA Style

Mulianga B, Bégué A, Simoes M, Todoroff P. Forecasting Regional Sugarcane Yield Based on Time Integral and Spatial Aggregation of MODIS NDVI. Remote Sensing. 2013; 5(5):2184-2199.

Chicago/Turabian Style

Mulianga, Betty; Bégué, Agnès; Simoes, Margareth; Todoroff, Pierre. 2013. "Forecasting Regional Sugarcane Yield Based on Time Integral and Spatial Aggregation of MODIS NDVI." Remote Sens. 5, no. 5: 2184-2199.

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