The concept of object identification and modeling has fueled a lengthy scientific effort to convert remotely sensed images into geographic phenomena. The objective of this article was to develop a new concept for characterizing and identifying drought spatial objects from satellite images for improved drought prediction and mitigation using a back propagation artificial neural network (ANN). To characterize drought as a spatial object, 11 attributes from multi-sensors and resolutions ( such as

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... tandardized Deviation of Normalized Difference Vegetation Index [SDNDVI], Digital Elevation Model [DEM], Soil Water Holding Capacity, Ecological Regions, Land Cover, Standard Precipitation Index [SPI] , and oceanic indices were used. After characterizing and identifying drought spatial objects, the experimental focus was on predicting drought in a one-to four-month time lag using a back propagation ANN. Using this approach, the drought was predicted for one to four months lead time with correlation coefficient (r) accuracies of 0.70-0.95. The output of this new concept could help in integrating the available information from multi-sensors and resolutions for a drought mitigation application at different levels of decision making. Future research may focus on experimenting with the approach in wider coverage areas, such as at regional or continental levels, and quantifying the uncertainty level of the approach for its practical use in drought adaptation planning and mitigation applications.