Drought is one of the most impo rtant challenges facing the planet. When itrnhappens, it usually re sults in serious econom ic. environmental, and social cr ises.rnDespite the growi ng number of freely available biophysical, climate. and satelliterndata for characterizing and modeli ng drought, research efforts have beenrnconstrained to using only meteo rological point data, such as the amount of rainfall,rnfor drought monitoring information. This po int data is insufficient for representingrndiversified ecosystems, and the data has coarse reso lut ion levels (lim ited spatialrncoverage). Researchers also have limited tools for data retrieval and integration forrnimproved drought identification and model ing. which usua lly results in a time de layrnfo r informat ion to reach dec is ion makers. Taking this into account, this dissertationrnresearc h has three objectives: I) identify the most re levant attr ibutes for effic ient lyrnimplementing drought monitoring, 2) develop a new approach for extractingrnknowledge from sate ll ite imageries for improved ident ifica tion and pred iction ofrndrought, and 3) evaluate the new approach for national and regio nal dro ughtrnprediction appl ications. Using an exploratory research approach and modeli ngrnresearch method, different data co llect ion and analys is techniques were executedrnusing knowledge d iscovery in a database approach. The data mi ning modelsrndeveloped using art ificial neural network and regress ion tree models were able tornpredict DroughtObject with accuracy of 0.70 - 0.95 co rrelat ion coefficients. in a netarnfour month s' time lag. The develo ped DroughtObject model was evaluated for itsrnapplication in showing drought severity and food defic it status. There were positivernrelat ionships between DroughtObjecl products and crop yield data up to 0.91 R2rnvalues. The results confirmed that the model can direct ly be used by those who arerncurrently responsible for drought monitoring and ri sk management. The newrnconcept developed in this research was prolotypcd and demonstrated in an easy-touscrnapproach. with a focus on demonstfaling the concept of DroughtObjeclrncharacterization and identification fro m a group of pixels. This demonSiration alsornrevealed poss ible future system deve lopments.rnThis di ssert ation research could he lp deci sion makers use advanced satelliterntechnology fo r crrcctive drought monitoring and early warning systems in va riousrnregio ns. Combined with proper pol ic ies. Ihese systems can he lp to prevent faminernand starvat ion in food-insecure reg ions. Up to now, satellite technologies have beenrnused primarily in areas of meteoro logical applications. In this research. the mainrnemphas is is on mining knowledge from satell ite images for dro ught ri sk assessmentrnand sa ving the lives of individua ls who are affected by recurring drought s. Thernfindings of this research can help decision makers take time ly and appropriaternactions to save lives in drought-affected areas using advanced satellite techno logy.