Biophysical Remote Sensing

Remote sensing is a collection of spatiotemporal views (images) of Earth's surface from vantage points, high above Earth. Such a collection has become instrumental to monitor and predict the future biophysical changes on Earth's surface that human beings will need to overcome. By using remote sensing techniques that involve the interpretation of reflective and absorptive properties of electromagnetic radiations (EMR), image interpreters are able to predict sea surface temperatures, wind patterns, the health of vegetation, possible occurrences of fire based on the structures of various plant canopy, and many other environmental factors. The EMR is absorbed and reflected by both the biological and physical objects of the Earth.

The solar radiation distribution among the vegetation is a function of canopy structure that strongly affects the productivity of the canopy and influences the surrounding biophysical environment. The productivity of the plant canopy is related to the well-being of humankind. The recent advances in biophysical remote sensing has made it possible to include all the biological and physical processes influencing the microclimate and atmospheric exchange characteristics of terrestrial biosphere over a range of spatial and temporal scales. The vegetation, the principal biosphere component, tightly couples with the radioactive, meteorological, hydrological, and biological processes. There is interconnectedness among atmosphere/cloud, wind, water, topography, soil moisture, and land cover types in the biosphere, and the biophysical remote sensing helps monitor such interconnectedness.

Biophysical remote sensing helps display patchy warm-and-cool patterns of the atmospheric/cloud conditions on the images. A dark image (dark signature) indicates relatively cool conditions while the bright signatures indicate relatively warm conditions.

Wind produces characteristic patterns of smears and streaks on images resulting in lighter and darker color signatures. Often, these signatures extend over vast areas. For example, wind velocity is typically lower downwind due to [Page 252]obstructions and reduces cooling effects resulting in relatively bright images; sheltered areas are generally warmer than terrain exposed to windy conditions during the winter. Theoretically, warmer areas generate relatively brighter images than cooler areas.

This MODIS Terra image, acquired in August 2, 2006, shows most of Greece, with its jagged coastline, and many islands and peninsulas.

Source: Jeff Schmaltz/NASA.

Another biophysical component, water, behaves differently than a land surface. During the daytime, water bodies have a cooler surface temperature than soils and rocks, and water bodies appear darker on the daytime image. Since water and ground surface temperatures reverse themselves during the day and nighttime, the thermal signature of water bodies becomes a reliable index of the timing of image. Warmer signatures on water bodies as compared with the adjacent terrain indicate nighttime image acquisition, while relatively cooler signatures indicate daytime imagery.

Scattered rain showers produce parallel lines on the image, just like the lines developed from a malfunctioned scanner. A heavy overcast layer (dense cloud) reduces thermal contrasts between terrain objects because of reradiation of energy between the terrain and cloud layer but the resulting thermal contrast becomes relatively low.

Topography plays a dominant role in remote sensing as solar heat and shadow vary with the timing of images. If images are taken in the nighttime, the thermal properties of images are displayed due to the reradiation of surface temperatures. If the images are taken during the daytime, the ridges and slopes facing the south [Page 253]and east reveal brighter signatures due to solar illumination, while areas facing the north are shaded from solar illumination and display dark (cool) signatures. On the nighttime image, topographic features are mostly eliminated as only the geologic features (thermal characteristics of objects) influence the image appearances. The total and spectral global solar irradiance absorbed by the vegetation, canopy layer, spectral reflectance, and transmittance for each discrete wavelength at various exposures can be calculated by using Equation 1 and merging satellite images with digital elevation data.

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