Land surface albedo is a critical parameter affecting the earth's climate and is required by global and regional climatic modeling and surface energy balance monitoring. Surface albedo retrieved from satellite observations at one atmospheric condition may not be suitable for applicati to other atmospheric conditions. In this paper the authors separate the apparent surface albedo from the inherent surface albedo, which is independent of atmospheric conditions, based on extensive radiative transfer simulations under a variety of atmospheric conditions. The results show that spectral inherent albedos are different from spectral apparent albedos in many cases. Total shortwave apparent albedos under both clear and cloudy conditions are also significantly different from their inherent total shortwave albedos. The conversion coefficients of the surface inherent narrowband albedos derived from the MODIS (Moderate-Resolution Imaging Spectroradiometer) and the MISR (Multiangle Imaging Spectroradiometer) instruments to the surface broadband inherent albedo are reported. A new approach of predicting broadband surface inherent albedos from MODIS or MISR top of atmosphere (TOA) narrowband albedos using a neural network is proposed. The simulations show that surface total shortwave and near-infrared inherent albedos can be predicted accurately from TOA narrowband albedos without atmospheric information, whereas visible inherent albedo cannot.
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