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Off-Nadir Angle
Off-Nadir Angle is a term in satellite imagery and remote sensing that refers to the angle between the viewing direction of the sensor and the point on the Earth surface that lies vertically beneath the satellite. Knowledge about off-nadir angles is critical to understanding satellite imagery quality, spatial accuracy, coverage of the Earth surface and any kind of geometric distortion. The greater the off-nadir angle is, the more oblique the view of the Earth surface is performed by the sensor. Off-nadir satellite imagery is common to be applied in geospatial tasks related to Earth observation, geographic information systems mapping, urban planning, disaster assessment, agriculture, environmental monitoring, infrastructure analysis, and change detection. This guide will explain what off-nadir angle means, how it is different from nadir viewing, how viewing geometry impacts satellite imagery and why it is important in choosing high-resolution satellite imagery data.

The Off-Nadir Angle is one of the important factors in the satellite remote sensing process, which denotes the distance between the viewing direction of the sensors and the vertical, or nadir, direction. This factor has significant influence on the determination of the satellite image geometry, resolution, ground coverage, perspective, and positional accuracy. The larger off-nadir angle provides satellites an opportunity to record images outside the direct ground track. However, an increase in the viewing angle leads to more geometric distortions, longer shadows, and changes in the appearance of the terrain and objects. It is crucial to know about the off-nadir angle when choosing satellite images for the purpose of GIS mapping, Earth observation, urban planning, agriculture, disaster management, environmental monitoring, infrastructure evaluation, and change detection. This guide is intended to provide information about off-nadir viewing, its relation to nadir imagery, main advantages and disadvantages, and influence of viewing geometry on the geospatial data quality.
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