The paper compares the risk coping potential of insurances that are based on indices derived from weather (rainfall and temperature) data as well as from crop model and remote sensing analyses. Corresponding indices were computed for the case of wheat production in the Aleppo region of northern Syria, representative for agricultural production systems in many developing countries. The results demonstrate that weather derivatives such as the rainfall sum index (RSI) and the rainfall deficit index (RDI) have a very good potential for coping with risk in semiarid areas. Crop simulation model index (CSI) on the other hand could serve as an alternative to RSI and RDI when historical farm yield data is not available or not reliable. In such cases we simulated historical yields using the CropSyst cropping system simulation model. Remote sensing data could be used to establish index insurances where weather stations are sparsely located and (daily time step) weather data thus not available. The study analyzes two indexes estimated from the Normalized Differential Vegetation Index (NDVI): (1.) the farm level NDVI (FNDVI) and (2.) the area level NDVI (ANDVI). FNDVI may have a very high potential for securing farm revenues, but may be prone to moral hazard since farm management changes and subsequent gains or losses in crop production are directly revealed by the NDVI when high resolution images are used. Therefore, we recommend ANDVI for developing countries since the index is estimated for the whole agricultural zone similar to traditional area-yield insurances.