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Missing module name in call for tid 'bibfields.AUTHOR': Marinho, Eduardo and Vancutsem, Christelle and Fasbender, Dominique and Kayitakire, François and Pini, Giancarlo and Pekel, Jean-François

From Remotely Sensed Vegetation Onset to Sowing Dates: Aggregating Pixel-Level Detections into Village-Level Sowing Probabilities

Journal: Remote Sensing
Volume: 6
Year: 2014
Number: 11
Pages: 10947--10965

Missing module name in call for tid 'bibfields.ABSTRACT'

Monitoring the start of the crop season in Sahel provides decision makers with valuable information for an early assessment of potential production and food security threats. Presently, the most common method for the estimation of sowing dates in West African countries consists of applying given thresholds on rainfall estimations. However, the coarse spatial resolution and the possible inaccuracy of these estimations are limiting factors. In this context, the remote sensing approach, which consists of deriving green-up onset dates from satellite remote sensing data, appears as an interesting alternative. It builds upon a novel statistic model that translates vegetation onset detections derived from MODIS time series into sowing probabilities at the village level. Results for Niger show that this approach outperforms the standard method adopted in the region based on rainfall thresholds.

Missing module name in call for tid 'bibfields.KEYWORDS'

MODIS,Niger,crops,food security,green-up onset,phenology,remote sensing,sowing probabilities,statistical model

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