Determining the Best Optimum Time for Predicting Sugarcane Yield Using Hyper-Temporal Satellite Imagery

Copyright © 2013 Shingirirai Mutanga et al. This is an open access article distributed under the Creative Commons Attribution Li-cense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Hyper-temporal satellite imagery provides timely up to date and relatively accurate information for the management of crops. Nonetheless models which use high time series satellite data for sugarcane yield estimation remain scant. This study determined the best optimum time for predicting sugarcane yield using the normalized difference vegetation index (NDVI) derived from SPOT-VEGETATION images. The study used actual yield data obtained from the mill and re-lated it to NDVI of several two-month periods of integration spread along the sugarcane growing cycle. Findings were in agreement with results of previous studies which indicated that the best acquisition period of satellite images for the assessment of sugarcane yield is about 2 months preceding the beginning of harvest. Overall, of the five years tested to determine the relationship between actual yield and integrated NDVI, three years showed a significant positive rela-tionship with a highest r2 value of 85%. The study however warrants further investigation to improve and develop ac-curate operational sugarcane yield estimation models at the local level given that other years had weak results. Such hybrid models may combine different vegetation indexes with agro-meteorological models which take into accoun