Remote sensing research develops methods to gather information about Earth and other planets from a distance, mainly using satellites, aircraft and drones. It focuses on measuring reflected sunlight, emitted thermal radiation and other parts of the electromagnetic spectrum to infer surface and atmospheric properties.

A central theme is converting raw sensor data into geophysical information. This includes deriving surface temperature, vegetation health, soil moisture, snow cover, land use and ocean properties. Researchers design algorithms to correct for atmospheric effects, sensor geometry and calibration issues so that measurements are accurate and comparable over time.

Another key area is atmospheric remote sensing. Instruments that observe the limb or nadir of the atmosphere retrieve vertical profiles of temperature, trace gases, aerosols and clouds. These data support climate research, air quality monitoring and numerical weather prediction. Hyperspectral and multispectral techniques are widely studied to distinguish materials and gases by their spectral signatures.

Remote sensing science also investigates active systems such as radar and lidar. These provide information on surface roughness, topography, biomass, ice sheet thickness, cloud structure and aerosol layers, often independent of daylight. Combining active and passive observations improves understanding of complex phenomena like precipitation and polar processes.

A growing line of research links remote sensing with in situ measurements, models and data assimilation, creating integrated observing systems. Machine learning is increasingly used to classify land cover, detect change, and retrieve physical parameters from large image archives. Overall, the research aims to produce reliable, long term, globally consistent datasets for environmental monitoring, resource management, hazard assessment and planetary exploration.