By Wen-Jun Zhang
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Extra info for Ecological Modeling
Figure 1. Atmospheric windows within the reflective part of the EMR. 18 George P. 76 µm. 75 μm. e. 91 µm). 4 μm. Consequently, it is natural to expect that correction of these atmospheric effects can be particularly useful for improving the quality of the remotely sensed data. Description of such atmospheric correction approaches can be found elsewhere, including Slater (1980) and Rees (2001). As the solar waves propagate through Earth‘s atmosphere from these atmospheric windows, radiation that is not absorbed or scattered in the atmosphere can reach and interact with the Earth's surface.
However, turbid water reflects significant amounts of radiation, especially in the red and NIR spectral regions. There is a shift in the spectral reflectance regions, as water increases in turbidity (Verbyla, 2001). However, the spectral reflectance of water is affected by the presence and concentration of dissolved and suspended organic and inorganic material, and by the depth of the water body (Jensen, 2000). Thus, the intensity and distribution of the radiance upwelling from a water body are indicative of the nature of the dissolved and suspended mater in the water and of the water depth.
Also aiming to create an index resistant to atmospheric influence, Kaufman and Tanre (1992) have developed the Atmospherically Resistant Vegetation Index (ARVI) for the MODIS sensor. This index is using the blue and red channel to isolate the atmospheric effects and subsequently apply the corrections on the red and NIR reflectance. Kaufman and Tanre (1992) showed that ARVI is four times less sensitive to atmospheric changes than NDVI. The same principle which was applied to SAVI, correcting the red band with the information contained in the blue band, producing the Soil-adjusted Atmospherically Resistant Vegetation Index (SARVI; Huete et.