Satellite Imagery: Strengths and Weaknesses

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Table of Contents

Introduction
Visible Satellite Imagery
Infrared Satellite Imagery
Vapor Satellite Imagery
Fog Satellite Imagery
Conclusion
References

Introduction

The invention of the satellite was vital to exploring the planet from above by revealing features that had not been seen. The technology has been used to capture images of landscapes and other fascinating features on the earth. Weather forecasters and flight directors rely on this technology to give reliable information to the public and the cabin crew. Many astronauts and space researchers depend on satellite imagery as a weather forecasting system to schedule their space missions. Various types of technology present significant opportunities as well as drawbacks that impact their overall applications.

Visible Satellite Imagery

Visible satellite imagery uses sunlight to record images, making it work well during the day. This technology possesses the ability to display the difference between underlying fog and stratus, which other imaging technologies like Infrared imagery may not capture, provided such areas are not blocked from view by higher clouds (Felegari et al., 2021). Contrary to the advantages mentioned above, the University of Wisconsin Department of Atmospheric and Oceanic Sciences indicates that visible imagery is only useful during the daylight hours, making it difficult to distinguish low clouds from high clouds since all clouds reflect a similar amount of light (Felegari et al., 2021). In addition, it is challenging to distinguish snow from clouds, which may lead to unreliable data.

Infrared Satellite Imagery

Infrared imagery uses emitted wavelengths to record images, which can be done during the day or night. The University of Wisconsin, Department of Atmospheric and Oceanic Sciences, states that this technology has the ability to distinguish higher clouds from lower ones. Furthermore, it makes it possible to observe storms at night using this imagery, in addition to differentiating clouds from snow cover (Zou et al., 2021). It is unreliable for detecting low clouds at night because the temperature emitted by underlying clouds and fog is somewhat the same as in surrounding areas

Vapor Satellite Imagery

This technology is often applied to detect the presence of vapor above twenty thousand feet in the atmosphere. It is mainly used to forecast and analyze the paths in addition to higher level motion of moisture when multiple such images are looped together (Gadamsetty et al., 2022). This technology is used to give pilot briefings and directions because it detects jet streams and high headwinds. It is vital for identifying mountain wave turbulence on airways even under clear visibility. Despite being used in flight control, Vapor Imagery does not show the presence of low clouds or water vapor content below the effective layer. Similarly, it is unable to give the measure of atmospheric vapor below the effective layer.

Fog Satellite Imagery

Finally, fog satellite imagery, like visible images, is majorly useful during the day only because if a fog lies between the middle and upper clouds, it becomes difficult to detect due to limited grey scale variability. In comparison to visible imagery, this technology displays smoother images. Therefore its accuracy can be hampered by contrasting temperatures between cloud top and the surrounding sea and when the land surface is small.

Conclusion

Satellite technology is vital in day-to-day global operations; weather forecasting, air travel, and space travel all rely on the data acquired by this technology. This invention has expanded human knowledge about the planet and the solar system by revealing secret features on earth and remarkable images of the galaxy. The four types of Satellite imagery we have discussed have advantages and disadvantages; however, the accuracy and the reliability of the data generated by each system depend on the time of day it is appropriate to be used. There is some visible imagery that is more reliable during daylight, whereas others, including infrared and water vapor, can be reliable both at night and during sunlight.

References

Felegari, S., Sharifi, A., Moravej, K., Amin, M., Golchin, A., Muzirafuti, A., Tariq, A. & Zhao, N. (2021). Integration of Sentinel 1 and Sentinel 2 satellite images for crop mapping. Applied Sciences, 11(21), 10104. Web.

Gadamsetty, S., Ch, R., Ch, A., Iwendi, C., & Gadekallu, T. R. (2022). Hash-based deep learning approach for remote sensing satellite imagery detection. Water, 14(5), 707. Web.

Zou, Y., Zhang, L., Liu, C., Wang, B., Hu, Y., & Chen, Q. (2021). Super-resolution reconstruction of infrared images based on a convolutional neural network with skip connections. Optics and Lasers in Engineering, 146, 106717. Web.

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