  Surface albedo is an important parameter in radiative transfer simulations of
the Earth's system, as it is fundamental to correctly calculate the energy
budget of the planet. The Moderate Resolution Imaging Spectroradiometer (MODIS)
instruments on NASA's Terra and Aqua satellites continuously monitor daily and
yearly changes in reflection at the planetary surface. The MODIS Surface
Reflectance black-sky albedo dataset (MCD43D, version 6.1) gives detailed
albedo maps in seven spectral bands in the visible and near-infrared range.
These albedo maps allow us to classify different Lambertian surface types and
their seasonal and yearly variability and change, albeit only in seven spectral
bands. However, a complete set of albedo maps covering the entire wavelength
range is required to simulate radiance spectra, and to correctly retrieve
atmospheric and cloud properties from Earth's remote sensing. We use a
Principal Component Analysis (PCA) regression algorithm to generate
hyperspectral albedo maps of Earth. Combining different datasets of
hyperspectral reflectance laboratory measurements for various dry soils,
vegetation surfaces, and mixtures of both, we reconstruct the albedo maps in
the entire wavelength range from 400 to 2500~nm. The PCA method is trained with
a 10-years average of MODIS data for each day of the year. We obtain
hyperspectral albedo maps with a spatial resolution of 0.05{\deg} in latitude
and longitude, a spectral resolution of 10~nm, and a temporal resolution of
1~day. Using the hyperspectral albedo maps, we estimate the spectral profiles
of different land surfaces, such as forests, deserts, cities and icy surfaces,
and study their seasonal variability. These albedo maps shall enable to refine
calculations of Earth's energy budget, its seasonal variability, and improve
climate simulations.
