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

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

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.

Authors: Marinho, E.; Vancutsem, C.; Fasbender, D.; Kayitakire, F.; Pini, G.; Pekel, J.-F.

Topic: crop production,statistics,remote sensing,food security

Geographic: Sahel,West Africa

Publication Year: 2014

ISSN: 2072-4292

Source: Remote Sensing 6(11): 10947-10965

DOI: 10.3390/rs61110947

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