BICTEL/e - ULg
Serveur institutionnel des thèses de doctorat

• Yield estimates/Prévision de rendement
• SenAFY
• Regional scale/Echelle régionale
• Green Area Index/Indice de surface verte
• Senescence/Sénescence
• MODIS
• Winter wheat/Blé d’hiver

Estimation of cereal-crop production is considered as a priority in most crop research programs due to the relevance of food grain to world agricultural production. A large number of agrometeorological models for crop yield assessment are available with various levels of complexity and empiricism. The current development of models for wheat yield forecasts, however, does not always reflect the inclusion of the loss of valuable green area and its relation to biotic and abiotic processes in production situation. At the field level, the close correlation between green leaf area during maturation and grain yield in wheat revealed that the onset and rate of senescence appeared to be important factors for determining wheat grain yield. Earth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for crop monitoring at different levels. Our study sought to explore an approach for winter wheat (Triticum aestivum L.) yield forecasts at a regional scale, based on metrics derived from the senescence phase of the green area index (GAI).

The senescence phase of winter wheat was analyzed and its modelling was achieved through two curve-fitting functions (modified Gompertz and logistic function). Metrics derived from these functions and characterizing this phase (i.e. the maximum value of GAI, the senescence rate and the time taken to reach either 37% or 50% of the remaining green surface in the senescent phase) were related to grain yields. The Senescence-based Approach For Yield estimates (SenAFY) was established and first tested at plot scale based on GAI values derived from digital hemispherical photograph. Then, it was applied at a regional scale using GAI temporal profiles retrieved from MODIS data. This second part of our study took advantage of recent methodological improvements in which imagery with high revisit frequency but coarse spatial resolution can be exploited to derive crop-specific GAI time series by selecting pixels whose ground-projected instantaneous field of view is dominated by the winter wheat. The regression-based models derived from the SenAFY provide interesting yield estimates at these two spatial scales. At regional scale, especially, the use of the SenAFY in order to forecast wheat yield gave satisfactory results. Such an approach may be considered as a first yield estimate that could be performed in order to provide better integrated yield assessments in operational systems.

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