|Reference : Wheat Yield Estimates at NUTS-3 level using MODIS data: an approach based on the decreas...|
|Scientific congresses and symposiums : Paper published in a book|
|Life sciences : Agriculture & agronomy|
|Wheat Yield Estimates at NUTS-3 level using MODIS data: an approach based on the decreasing curves of green area index temporal profiles|
|Kouadio, Amani Louis [Université de Liège - ULg > > > Doct. sc. (sc. & gest. env. - Bologne)]|
|Duveiller, Gregory [Université Catholique de Louvain - UCL > > > >]|
|Djaby, Bakary [Université de Liège - ULg > Département des sciences et gestion de l'environnement > Département des sciences et gestion de l'environnement >]|
|Tychon, Bernard [Université de Liège - ULg > Département des sciences et gestion de l'environnement > Département des sciences et gestion de l'environnement >]|
|Defourny, Pierre [Université Catholique de Louvain - UCL > > > >]|
|Proceedings of RSPSoc2010 Annual Conference. 1st-3rd September 2010, Cork, Ireland (Nottingham: RSPSoc)|
|RSPSoc2010 Annual Conference :Visualising the Earth : From the sea-bed to the cloud-tops|
|1st-3rd September 2010|
|Remote Sensing and Photogrametry Society (RSPSoc)|
|[en] Yield estimates; NUTS-3 level; Green Area Index; Senescence; Wheat|
|[en] Wheat is the most widely-grown food crop in the world and the most important cereal crop traded on international markets. An early prediction of its yield prior to harvest at regional, national and international scales can play a crucial role in global markets, policy and decision making. Many models for yield forecasting are available with varying levels of complexity and empiricism. The use of remote sensing technology for monitoring crop condition and predicting crop yields at regional scales have been studied extensively during these last decades. Earth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for yield and production forecasting.
At field level, studies on crop breeding showed that a close correlation exists between green leaf area during maturation and grain yield in wheat. Thus, the onset and rate of senescence appeared as important factors for determining grain yield of this crop. The aim of this research is to explore a simplified approach for wheat yield forecasting at the European NUTS-3 administrative level, based on metrics derived from the senescence phase of green area index (GAI) estimated from remote sensing data. This study takes advantage of considerable recent improvements in sensor technology and data availability through the opportunity of applying medium/coarse spatial resolution data for deriving crop specific GAI time series by selecting pixels whose ground-projected instantaneous field of view is constituted by a high cover fraction of winter wheat.
This approach is tested on 2 crop growing season over a 300 by 300 km study site comprising Belgium and northern France within the framework of the GLOBAM (GLObal Agricultural Monitoring systems by integration of earth observation and modelling techniques) project. The validation of such an approach will involve the comparison with official wheat yield data at NUTS-3 level.
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