The integration of remote sensing data involves combining various data to get better information, or more information about an area or phenomenon of interest. When it comes to combining data, it usually refers to multi-hour, multi-resolution or multi-sensor data linking. The subject of multi-sensor data integration is the combining of data collected by different sensors. A common example of this type of integration is the integration of multispectral optical data with radar imagery. Both spectrally different modes of representation complement each other: optical data is ''in charge'' of detailed spectral information (used to differentiate soil types) while radar data show structural details on the surface.

Nowadays remote sensing is an indispensable source of information about Earth's surface, primarily satellite-based remote sensing systems. Traditionally, the analysis of data collected from a particular area was based on the analysis of the data of one satellite image. The technological revolution improved spatial, temporal and radiometric resolution of satellite images, which allowed time datasets analysis, combining (integrating) data from various sensors, combining images of different scales and better integration with existing data and models. The integration of data from different sources is becoming an increasingly important factor in numerous aspects of remote sensing, and the results of this technique are used in solving everyday problems.