In semi-arid areas characterized by frequent drought events, there is often a strong
need for an operational grain yield forecasting system, to help decision-makers with
the planning of annual imports. However, monitoring the crop canopy and production
capacity of plants, especially for cereals, can be challenging. In this paper, a new
approach to yield estimation by combining data from the Simple Algorithm for Yield
estimation (SAFY) agro-meteorological model with optical SPOT/ High Visible Resolution
(HRV) satellite data is proposed. Grain yields are then statistically estimated as
a function of Leaf Area Index (LAI) during the maximum growth period between 25 March
and 5 April. The LAI is retrieved from the SAFY model, and calibrated using SPOT/HRV
data. This study is based on the analysis of a rich database, which was acquired over
a period of two years (2010–2011, 2012–2013) at the Merguellil site in central Tunisia
(North Africa) from more than 60 test fields and 20 optical satellite SPOT/HRV images.
The validation and calibration of this methodology is presented, on the basis of two
subsets of observations derived from the experimental database. Finally, an inversion
technique is applied to estimate the overall yield of the entire studied site.