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

Remote Sensing

Eduardo Marinho³  Christelle Vancutsem⁴  Dominique Fasbender⁴  François Kayitakire⁴  Giancarlo Pini¹  Jean-François Pekel⁴  Ioannis Gitas² 

[1] World Food Programme, Via Viola Giulio, 68 Roma, Italy; E-Mail:;id="af1-remotesensing-06-10947">1Center for International Forestry Research, Rua do Russel, 459/601 Rio de Janeiro (RJ), Braz;Center for International Forestry Research, Rua do Russel, 459/601 Rio de Janeiro (RJ), Brazil;Joint Research Center of the European Commission, Via Enrico Fermi, 2749 Ispra, Italy; E-Mails:

关键词: green-up onset;    sowing probabilities;    Niger;    crops;    statistical model;    MODIS;    remote sensing;    phenology;    food security;   

DOI  :  10.3390/rs61110947

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.