Forecasting of Cereal Yields in a Semi-arid Area Using the Simple Algorithm for Yield Estimation (SAFY) Agro-Meteorological Model Combined with Optical SPOT/HRV Images.

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.

Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters.

The main objective of this study is to analyze the potential use of Sentinel-1 (S1) radar data for the estimation of soil characteristics (roughness and water content) and cereal vegetation parameters (leaf area index (LAI), and vegetation height (H)) in agricultural areas. Simultaneously to several radar acquisitions made between 2015 and 2017, using S1 sensors over the Kairouan Plain (Tunisia, North Africa), ground measurements of soil roughness, soil water content, LAI and H were recorded. The NDVI (normalized difference vegetation index) index computed from Landsat optical images revealed a strong correlation with in situ measurements of LAI. The sensitivity of the S1 measurements to variations in soil moisture, which has been reported in several scientific publications, is confirmed in this study. This sensitivity decreases with increasing vegetation cover growth (NDVI), and is stronger in the VV (vertical) polarization than in the VH cross-polarization. The results also reveal a similar increase in the dynamic range of radar signals observed in the VV and VH polarizations as a function of soil roughness. The sensitivity of S1 measurements to vegetation parameters (LAI and H) in the VV polarization is also determined, showing that the radar signal strength decreases when the vegetation parameters increase. No vegetation parameter sensitivity is observed in the VH polarization, probably as a consequence of volume scattering effects.