Abstract
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 related 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 relationship with a highest r2 value of 85%. The study however warrants further investigation to improve and develop accurate 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 account broader crop’s physiological, growth demands, and soil management which are equally important when predicting yield.
Highlights
The 21st Century demands the promotion of fast track modernization and diversification of the sugar sector to convert it into an efficient cane industry aimed at producing sufficient stocks for manufacturing sugar, for energy [1], and other by products [2,3]
Of the five years tested to determine the relationship between actual yield and integrated normalized difference vegetation index (NDVI), three years showed a significant positive relationship with a highest r2 value of 85%
To estimate the best time of the year when the NDVI related to the sugarcane yield, the bi-monthly NDVI was plotted against the correlation coefficient
Summary
The 21st Century demands the promotion of fast track modernization and diversification of the sugar sector to convert it into an efficient cane industry aimed at producing sufficient stocks for manufacturing sugar, for energy [1], and other by products [2,3]. Remote sensing in the form of hyper-temporal satellite imagery is one of the tools that can be used to provide timely up to date and relatively accurate information for the management of sugar cane crop. Several studies have applied remote sensing techniques in sugarcane monitoring. There is limited applied remote sensing for sugarcane yield prediction yet it has been used successfully on graneous crops, such as maize and wheat [9,10,11,12], linked the paucity of publications of experimental results to the difficulty in collating data and the lengthy of the growing period of sugarcane. Given the crop’s relevance in today’s world economy, and the scant models developed this far, the study uses SPOT VEGETATION multi-temporal images to estimate the optimum time for sugarcane yield prediction
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