Satellite Remote Sensing of the Environment

Abstract

Satellite remote sensing of the environment is a relatively young science, beginning in earnest in the 1970s. Since then, rapid developments in sensor technology, computer power and data storage capabilities have resulted in remarkable advances in what can be observed, at what resolution and with what accuracy. Here, we summarise the fundamental physical concepts and the types of sensor that have been used for observing the biosphere, hydrosphere, oceans and ice at the surface of the Earth.

Key Concepts:

  • Satellites can provide global, synoptic coverage of key parameters such as albedo, surface temperature, soil moisture, rainfall, primary productivity in the surface oceans, and vegetation cover.

  • There is a continuous time series of visible and infrared observations covering the last four decades. Much of these data are freely available.

  • Improvements in technology mean image resolution of less than a metre is achievable, greatly improving classification accuracy.

  • Radar remote sensing is proving particularly useful for observing the hydrological cycle due to its all‐weather day/night capability.

Keywords: albedo; emissivity; hydrology; oceans; cryosphere; vegetation; meteorology

Figure 1.

(a) The electromagnetic spectrum, showing the main ‘atmospheric windows’ where absorption by the atmosphere is relatively low and, thus, where satellite remote sensing of the earth surface is carried out and (b) the spectral emission characteristics of the Sun and the surface of the earth. Note that the peaks in emission are in the visible and thermal IR, respectively.

Figure 2.

The spectral reflectance or albedo of different natural surfaces as a function of wavelength. It can be seen that ripe wheat, for example, has a higher albedo at approximately 1 μm, compared with that of unripe wheat, potentially allowing discrimination of the two. It can also be seen that for all the vegetation types included, there is a rapid increase in albedo at approximately 0.7 μm, which forms the basis for the vegetation index discussed in the section on applications.

Figure 3.

A Landsat Thematic Mapper image of part of northeast Majorca. (a) A true colour composite (bands 1, 2 and 3). (b) The image segmented into five ‘classes’ or categories according to the spectral properties of each pixel, that is, each class has similar spectral characteristics and should, therefore, represent a particular type of surface. Noise in the image, atmospheric interference and overlapping spectral properties of different materials in the bands of TM often result in an imperfect classification, requiring user intervention (supervised classification).

Figure 4.

Global monitoring of key bio‐geographical variables with MODIS. (a) Global NDVI map as of September 2011. (b) Global measurements of the carbon stored by plants (net primary productivity) during photosynthesis are an important piece of the climate change puzzle. Scientists need to know how much of the carbon dioxide released by burning fossil fuels the biosphere can absorb and how much will linger in the atmosphere. Source: NASA Earth Observatory.

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Further Reading

Rees WG (2001) Physical Principles of Remote Sensing, 2nd edn. Cambridge: Cambridge University Press.

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How to Cite close
Bamber, Jonathan L, and Schumann, Guy(Jun 2012) Satellite Remote Sensing of the Environment. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0003267.pub2]