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Open AccessArticle

A Multisensor Approach to Global Retrievals of Land Surface Albedo

1
Finnish Meteorological Institute, Erik Palménin aukio 1, FI-00560 Helsinki, Finland
2
National Oceanic and Atmospheric Administration (NOAA), 1225 West Dayton Street, Madison, WI 53706, USA
3
School for the Environment, University of Massachusetts Boston, Boston, MA 02125, USA
4
University of Bern, Institute of Geography, Hallerstrasse 12, 3012 Bern, Switzerland
5
EUMETSAT, Eumetsat Allee 1, D-64295 Darmstadt, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(6), 848; https://doi.org/10.3390/rs10060848
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.

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