With a changing climate, the frequent occurrence of drought has resulted in unprecedented grain prices and severe market instability, threatening global food security. Earth observation, especially satellite-based observation, has proven its potential for near real-time drought monitoring and early warning. This dissertation undertakes a comprehensive analysis of agriculture-oriented drought risk, impact and monitoring using time-series satellite observation combined with ancillary earth observation data, thus providing a better understanding of agricultural drought. Agricultural lands exhibit more severe drought regimes during the agricultural growing season. At the global scale, the U.S. Corn Belt, Spain & Eastern Europe, Central Russia, India, North China and Australia, are shown to be the hotspots of agricultural drought risk. For the last three decades, different agricultural drought risk change patterns are found in different regions with a relatively stable but slight declining drought risk overall for the globe, while Australia exhibits a continuous increase and Brazil exhibits a continuing decrease. Land Surface Temperature (LST) and Evapotranspiration (ET) based indicators show similar capabilities for drought monitoring and have an immediate response after drought; while for Normalized Difference Vegetation Index (NDVI) derived indicators, there shows a lagged and inconsistent drought response. The relationships between NDVI- and LST- derived drought indicators are variable, exhibiting changing functions in both spatial and temporal domains, which provides basis for effectively integrating different data sources for developing a synthetic index. Drought results in varying impacts during the growing season, with generally increasing impacts during the winter wheat main growing season and the most severe drought effects during the grain filling stage around vegetative peaks. As for the Drought Severity Index, better performance is found in rainfed-dominated than irrigation-dominated regions. This dissertation calls for continuing work to develop an improved impact-oriented agricultural drought indicator by integrating the contributions of different data sources, the dynamics of NDVI-LST interactions as well as the varying drought impacts during the growing season. Improved agricultural drought monitoring and impact assessment, together with agricultural risk analysis, can help prototype an enhanced and integrated agricultural drought monitoring system, thus offering reliable and timely information for drought mitigation, preparedness, response and recovery.