TITLE:
Determining the Best Optimum Time for Predicting Sugarcane Yield Using Hyper-Temporal Satellite Imagery
AUTHORS:
Shingirirai Mutanga, Chris van Schoor, Phindile Lukhele Olorunju, Tichatonga Gonah, Abel Ramoelo
KEYWORDS:
Sugarcane; NDVI; Yields; Spot Vegetation
JOURNAL NAME:
Advances in Remote Sensing,
Vol.2 No.3,
September
12,
2013
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.