A drought generally refers to an extended time period (tens of days, months or years) when the surface and root-zone soil moisture values drop consistently below a level that affects crop and vegetation growth or survive (called agricultural drought). This prolonged soil moisture deficiency is usually caused by consistently below average precipitation (meteorological drought). Further dry down of soil moisture may result in water reserves of the region to fall below statistical average (hydrological drought). Any droughts may cause a substantial impact on the ecosystem, agriculture, water management of the affected region. To human, they may pose serious impact or even threat on the region's food production, life quality, economic performance, and even political stability or a country's security. Monitoring and forecasting drought occurrences have thus been a high priority for various levels of governments and a hot research topic for environmental scientists. In this presentation, a concept for a comprehensive drought monitoring and forecasting system using available microwave, optical satellite observations and the Noah land surface model predictions will be proposed and demonstrated using the rainfall and soil moisture retrievals from the Advanced Microwave Scanning Radiometer (AMSR-E), the NDVI data from the Moderate-resolution Imaging Spectroradiometer (MODIS), and the Noah land surface model in NASA's Land Information System (LIS) driven by the output of NCEP's Global Data Assimilation System (GDAS). The soil moisture retrievals are assimilated into the Noah LSM using the Ensemble Kalman Filter implemented in LIS, resulting in an analysis data layer of surface and root-zone soil moisture. Climatological data (means and variances) of rainfall, soil moisture and runoff of all 25x25km grids/pixels over the domain of North America Land Data Assimilation System (NLDAS) are obtained from the observations of AMSR-E and the analysis of Noah LSM since June, 2002. Then the daily soil moisture simulations and/or predictions for every day in 2007 are compared with their climatological means and variances. The drought areas detected from these comparisons are then evaluated with other independently obtained results. Applications of this remote sensing and modeling system for drought monitoring and forecasts will be discussed.