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Open AccessArticle

A Multisensor Approach to Global Retrievals of Land Surface Albedo

Finnish Meteorological Institute, Erik Palménin aukio 1, FI-00560 Helsinki, Finland
National Oceanic and Atmospheric Administration (NOAA), 1225 West Dayton Street, Madison, WI 53706, USA
School for the Environment, University of Massachusetts Boston, Boston, MA 02125, USA
University of Bern, Institute of Geography, Hallerstrasse 12, 3012 Bern, Switzerland
EUMETSAT, Eumetsat Allee 1, D-64295 Darmstadt, Germany
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(6), 848;
Received: 28 March 2018 / Revised: 7 May 2018 / Accepted: 23 May 2018 / Published: 29 May 2018
(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)
Satellite-based retrievals offer the most cost-effective way to comprehensively map the surface albedo of the Earth, a key variable for understanding the dynamics of radiative energy interactions in the atmosphere-surface system. Surface albedo retrievals have commonly been designed separately for each different spaceborne optical imager. Here, we introduce a novel type of processing framework that combines the data from two polar-orbiting optical imager families, the Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). The goal of the paper is to demonstrate that multisensor albedo retrievals can provide a significant reduction in the sampling time required for a robust and comprehensive surface albedo retrieval, without a major degradation in retrieval accuracy, as compared to state-of-the-art single-sensor retrievals. We evaluated the multisensor retrievals against reference in situ albedo measurements and compare them with existing datasets. The results show that global land surface albedo retrievals with a sampling period of 10 days can offer near-complete spatial coverage, with a retrieval bias mostly comparable to existing single sensor datasets, except for bright surfaces (deserts and snow) where the retrieval framework shows degraded performance because of atmospheric correction design compromises. A level difference is found between the single sensor datasets and the demonstrator developed here, pointing towards a need for further work in the atmospheric correction, particularly over bright surfaces, and inter-sensor radiance homogenization. The introduced framework is expandable to include other sensors in the future. View Full-Text
Keywords: surface albedo; retrieval; multisensor; evaluation; land surface surface albedo; retrieval; multisensor; evaluation; land surface
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MDPI and ACS Style

Riihelä, A.; Manninen, T.; Key, J.; Sun, Q.; Sütterlin, M.; Lattanzio, A.; Schaaf, C. A Multisensor Approach to Global Retrievals of Land Surface Albedo. Remote Sens. 2018, 10, 848.

AMA Style

Riihelä A, Manninen T, Key J, Sun Q, Sütterlin M, Lattanzio A, Schaaf C. A Multisensor Approach to Global Retrievals of Land Surface Albedo. Remote Sensing. 2018; 10(6):848.

Chicago/Turabian Style

Riihelä, Aku; Manninen, Terhikki; Key, Jeffrey; Sun, Qingsong; Sütterlin, Melanie; Lattanzio, Alessio; Schaaf, Crystal. 2018. "A Multisensor Approach to Global Retrievals of Land Surface Albedo" Remote Sens. 10, no. 6: 848.

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