Preprints
https://doi.org/10.5194/egusphere-2024-167
https://doi.org/10.5194/egusphere-2024-167
06 Mar 2024
 | 06 Mar 2024

Development of a HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution

Giulia Roccetti, Luca Bugliaro, Felix Gödde, Claudia Emde, Ulrich Hamann, Mihail Manev, Michael Fritz Sterzik, and Cedric Wehrum

Abstract. 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 dataset (MCD43C3, Version 6.1) gives detailed albedo maps in seven different 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 different times of the year. We obtain hyperspectral albedo maps with a spatial resolution of 0.05° in latitude and longitude, a spectral resolution of 10 nm, and a temporal resolution of 8 days. 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.

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Giulia Roccetti, Luca Bugliaro, Felix Gödde, Claudia Emde, Ulrich Hamann, Mihail Manev, Michael Fritz Sterzik, and Cedric Wehrum

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-167', Anonymous Referee #1, 09 Apr 2024
  • RC2: 'Comment on egusphere-2024-167', Luis Ackermann, 10 May 2024
Giulia Roccetti, Luca Bugliaro, Felix Gödde, Claudia Emde, Ulrich Hamann, Mihail Manev, Michael Fritz Sterzik, and Cedric Wehrum

Data sets

HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution Giulia Roccetti et al. https://zenodo.org/records/10494404

Video supplement

HAMSTER dataset Giulia Roccetti https://av.tib.eu/media/66248

Giulia Roccetti, Luca Bugliaro, Felix Gödde, Claudia Emde, Ulrich Hamann, Mihail Manev, Michael Fritz Sterzik, and Cedric Wehrum

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Latest update: 12 Jul 2024
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Short summary
The amount of sunlight reflected by Earth’s surface (albedo) is crucial for its radiative system. Satellite instruments offer detailed spatial and temporal albedo maps, but only in seven specific wavelength bands. We generate albedo maps that fully cover the visible and near-infrared range with a machine learning algorithm. These provide information about how the reflectivity of different land surfaces vary through the year. Our dataset enhances the understanding of Earth's energy balance.