Abstract
Clouds and their associated shadows are major obstacles to most land surface remote sensing applications. Meanwhile, solar-cloud-satellite geometry (SCSG) makes the effect of clouds and shadows on derived land surface biophysical parameters more complicated. However, in most existing studies, the SCSG effect has been frequently neglected although it is pointed out by many works that SCSG effect is a noticeable problem, especially in the field of land surface radiation budget. Taking shortwave downward radiation (SWDR) as a testing variable, this study quantified the SCSG effect on the derived SWDR, and proposed an operational scheme to correct the big effect. The results demonstrate that the proposed correcting scheme is very effective and works very well. It is revealed that a significant under- or overestimation is detected in retrieved SWDR if the SCSG effect is ignored. Typically, the induced error in SWDR can reach up to 80%. The scheme and findings of this study are expected to be inspirational for the land surface remote sensing community, wherein solar-cloud-satellite geometry is an unavoidable issue.
Highlights
Clouds and their shadows are a major concern to the optical remote sensing of the Earth’s surface
A large number of remote sensing applications, such as image classification, derivation of reflectance/Albedo, land surface temperature (LST), radiation, vegetation index as well as many other biophysical parameters are largely impacted by clouds and shadows
While the energy-related parameters, such as land surface shortwave (SWR) or longwave radiation (LWR), can never be deemed as static even in a half hour, the traditional cloud removal techniques cannot be applicable in these cases
Summary
Clouds and their shadows are a major concern to the optical remote sensing of the Earth’s surface. A large number of remote sensing applications, such as image classification, derivation of reflectance/Albedo, land surface temperature (LST), radiation, vegetation index as well as many other biophysical parameters are largely impacted by clouds and shadows. As an example, during the image classification, the cloud removal process is widely used by assuming the surface cover types are unchanged over a short time scale (e.g., a few days). While the energy-related parameters, such as land surface shortwave (SWR) or longwave radiation (LWR), can never be deemed as static even in a half hour, the traditional cloud removal techniques cannot be applicable in these cases. A possible way to derive land surface cloudy-sky radiation is to quantify the scattering/absorption (shortwave) or thermal emittance (longwave) induced by clouds on radiation. The estimation of cloudy-sky surface radiation has made great progress, the effect of solar-cloud-sensor geometry
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