An overview of remote sensing research in hydrometeorology, with an emphasis on the major contributions that have been made by United States Department of Agriculture- Agricultural Research Service (USDA-ARS) scientists, is provided. The major contributions are separated into deriving from remote sensing (1) hydrometeorological state variables and (2) energy fluxes, particularly evapotranspiration which includes plant water stress. For the state variables, remote sensing algorithms have been developed for estimating land surface temperatures from brightness temperature observations correcting for atmospheric and emissivity effects, estimating near-surface soil moisture from passive microwave remote sensing, determining snow cover from visible and snow water equivalent from microwave data, and estimating landscape roughness, topography, vegetation height, and fractional cover from lidar distancing technology. For the hydrometeorological fluxes, including plant water stress, models estimating evapotranspiration have been developed using land surface temperature as a key boundary condition with recent schemes designed to more reliably handle partial vegetation cover conditions. These research efforts in estimating evapotranspiration with remotely sensed surface temperatures have been utilized by ARS researchers in the development of the Crop Water Stress Index and Water Deficit Index for assessing plant water stress. In addition, the development of the Thermal Kinetic Window and Crop Specific Temperatures have revealed the dynamic interactions among foliage temperature, plant species, and the physical environment. ARS researchers continue to develop new and improved remote sensing algorithms for evaluating state variables and fluxes. Moreover, they are involved in new research directions to address science questions impeding hydrometeorological research. These include investigating the utility of combining multifrequency remote sensing data for improved estimation of land surface properties, and incorporating remote sensing for evaluating the effects of landscape heterogeneity on atmospheric dynamics and mean air properties and resulting feedbacks on the land surface fluxes.