Title: Operational maize yield model development and validation based on remote sensing and agro-meteorological data in Kenya
Citation: INTERNATIONAL JOURNAL OF REMOTE SENSING vol. 28 no. 17 p. 3775-3793
Publication Year: 2007
JRC N°: JRC42751
ISSN: 0143-1161
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC42751
DOI: 10.1080/01431160601075608
Type: Articles in periodicals and books
Abstract: Remote-sensing data acquired by satellite have a wide scope for agricultural applications owing to their synoptic and repetitive coverage. On the one hand, spectral indices deduced from visible and near-infrared remote-sensing data have been extensively used for crop characterization, biomass estimation, and crop yield monitoring and forecasting. On the other hand, extensive research has been conducted using agrometerological models to estimate soil moisture to produce indicators of plant-water stress. This paper reports the development of an operational spectro-agrometeorological yield model for maize using a spectral index, the Normalized Difference Vegetation Index (NDVI) derived from SPOTVEGETATION, meteorological data obtained from the European Centre for Medium-Range Weather Forecast (ECMWF) model, and crop-water status indicators estimated by the Crop-Specific Water Balance model (CSWB). Official figures produced by the Government of Kenya (GoK) on crop yield, area planted, and production were used in the model. The statistical multiple regression linear model has been developed for six large maize-growing provinces in Kenya. The spectro-agrometerological yield model was validated by comparing the predicted province-level yields with those estimated by GoK. The performance of the NDVI and land cover weighted NDVI (CNDVI) on the yield model was tested. Using CNDVI instead of NDVI in the model reduces 26% of the unknown variance. Of the output indicators of the CSWB model, the actual evapotranspiration (ETA) performs best. CNDVI and ETA in the model explain 83% of the maize crop yield variance with a root square mean error (RMSE) of 0.3298 t ha21. Very encouraging results were obtained when the Jackknife re-sampling technique was applied, thus proving the validity of the forecast capability of the model (r250.81 and RMSE50.359 t ha21). The optimal prediction capability of the independent variables is 20 days and 30 days for the short and long maize crop cycles, respectively. The national maize production during the first crop season for the years 1998¿2003 was estimated with an RMSE of 185 060 t and coefficient of variation of 9%.
JRC Directorate:Space, Security and Migration

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