Mapping cropland and characterizing their surface vegetation cover for major crops growingrnin the region over time and space is an essential input to many applications including, naturalrnresources assessment, food security monitoring and economics of land use practices. For suchrnapplications, multi temporal remote sensing data and GIS techniques play important role bothrnas source of data and techniques. This paper took the advantage of the multi-temporalrnModerate Resolution Imaging Spectro-radiometer (MODIS) Enhanced Vegetation Index (EVI)rndata to map cropland and types of crop growing in the study area and characterize their surfacerncover vegetation variability. The output of the study used to assess implications with respect tornecosystem services mainly grain production, soil erosion and sediment load to Lake Tana. Tornmap the types of crops, MODIS EVI data integrated with Traditional Cropping Calendar (TCC)rnand a decision tree based classification algorithm were used. The overall approach of the studyrnis summarized in four major steps; 1) Separating cropland from the non-cropland, 2)rnidentifying and mapping the different types of crops, 3) characterizing the vegetation dynamicsrnof different crops over time and space and 4) Assessing major ecosystem service and disservicernof growing different crops in the study areas. The proposed methodology revealed promisingrnresults in separating cropland from the non-cropland ones with 75% of an overall accuracyrnwhereas crop type mapping was done with 60% of accuracy. Relatively the poor classificationrnaccuracy for crop type map is due to the high landscape variability of the study area, smallerrnspatial resolution of the MODIS data which is less than individual plot. Nonetheless, crop typernmapping accuracy can further be improved if more criteria are established. Lake Tana SubrnBasin (LTSB) cropland is dominated by Teff cultivation that exposed the region for more soilrnerosion risk. The final outcomes of the present study could support researchers to develop arncrop phenology from multi-temporal data like MODIS. Moreover, maps of different cropsrngrowing in the basin could help to evaluate the economic benefit of different land use practices.rnUsing the developed crop type maps, the spatio-temporal surface cover condition and biomassrnover the cropland could be inter-related with several ecosystem disservices mainly soil loss andrnsediment load in a particular watershed.