TITLE:
Remote Sensing and GIS Based Spectro-Agrometeorological Maize Yield Forecast Model for South Tigray Zone, Ethiopia
AUTHORS:
Abiy Wogderes Zinna, Karuturi Venkata Suryabhagavan
KEYWORDS:
Ethiopia, Forecast Model, GIS, Maize Yield, NDVI, Remote Sensing, RFE
JOURNAL NAME:
Journal of Geographic Information System,
Vol.8 No.2,
April
28,
2016
ABSTRACT: Remote-sensing data
acquired by satellite imageries have a wide scope in agricultural applications
owing to their synoptic and repetitive coverage. This study reports the
development of an operational spectro-agrometereological yield model for maize
crop derived from time series data of SPOT VEGETATION, actual and potential
evapotranspiration and rainfall estimate satellite data for the years
2003-2012. Indices of these input data were utilized to validate their strength
in explaining grain yield recorded by the Central Statistical Agency through correlation
analyses. Crop masking at crop land area was applied and refined using
agro-ecological zones suitable for maize. Rainfall estimates and average
Normalized Difference Vegetation Index were found highly correlated to maize
yield with the former accounting for 85% variation and the latter 80%,
respectively. The developed spectro-agrometeorological yield model was
successfully validated against the predicted Zone level yields estimated by
Central Statistical Agency (r2 =
0.88, RMSE = 1.405 q·ha-1 and
21% coefficient of variation). Thus, remote sensing and geographical
information system based maize yield forecast improved quality and timelines of
the data besides distinguishing yield production levels/areas and making
intervention very easy for the decision makers thereby proving the clear
potential of spectro-agrometeorological factors for maize yield forecasting,
particularly for Ethiopia.