Dynamic Mapping of Cropland Areas in Sub-Saharan Africa Using Modis Time Series

Mapping cropland areas in a dynamic way is of great interest to successfully monitor agricultural areas and food security. Existing cropland masks are commonly derived from global/continental land use/land cover datasets. However, these products are either too coarse and does not account correctly for the specificities of some regions or not adapted to map cropland extent or are limited in spatial coverage. This study aims at developing a method for dynamic mapping of cropland areas in Sub-Saharan Africa and at producing a multi-annual map of cropland extent at 250m using MODIS time series. The originality of the approach consists of (i) including a dynamic and automatic stratification that allows tuning the classification parameters according to the inter-annual variability, and (ii) exploiting the local differences of spectral signatures between natural vegetation and crops during the green-up season. The methodology has been applied to the data of 2009 and will be applied to 3 other years (2006-2008). Then the cropland masks of each year will be combined to produce a map of the potential cropland areas. The four annual products will be compared and the inter-annual variability will be analysed. The accuracy of the product will be assessed using a large sample of points interpreted on high resolution images and will be compared to the accuracy of two global data sets at a similar spatial resolution: the global cropland extent product (Pittman et al. 2010) and Globcover.

JRC.DG.G.3 - Monitoring agricultural resources