Estimation of the dynamics and yields of cereals in a semi-arid area using remote sensing and the SAFY growth model

In semi-arid areas, high climatic variability induce a major risk on food security. Therefore an operational forecasting system for the grain yields is required and could help decisionmakers to make early decisions and plan annual imports. It can be challenging to monitor the crop canopy and production capacity of plants, especially cereals. In this context, the aim of the present study is to analyze the characteristics of two types of irrigated and non-irrigated cereals: barley and wheat. Through the use of a rich database, acquired over a period of two years for more than 30 test fields, and from 20 optical satellite SPOT/HRV images, two research approaches are considered. Firstly, statistical analysis is used to characterize the vegetation's dynamics and grain yield, based on remotely sensed (satellite) Normalized Difference Vegetation Index (NDVI) measurements. A relationship is established between the NDVI and LAI (leaf area index). Different robust relationships (exponential or linear) are established between the satellite NDVI index acquired from SPOT/HRV images, just before the time of maximum growth (April), and grain and straw, for barley and wheat vegetation covers. Following validation of the proposed empirical approaches, yield maps are produced