Share Email Print

Proceedings Paper

Estimation of land surface albedo time series and trends based on MODIS data
Author(s): Nikolaos Benas; Nektarios Chrysoulakis
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

The land surface albedo is among the most important parameters controlling the atmospheric radiation fluxes and the surface–atmosphere interactions. In the present study, surface albedo parameters and aerosol optical thickness (AOT) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, onboard NASA’s Terra and Aqua satellites, were analyzed and processed for the estimation of the shortwave surface albedo over Europe, Northern Africa and the Middle East, at 1 km × 1 km spatial resolution and on an 8–day average basis, for the period 2001–2012. The surface albedo was computed as a linear combination of black-sky and white-sky albedos. This methodology allows the computation of surface albedo for different values of AOT and solar zenith angle (SZA). MODIS Level 3 AOT data were used in the computations, while the surface albedo was calculated as an average of albedo values, using different SZAs on a pixel basis. The final albedo product was analyzed in terms of spatial and seasonal characteristics, and inter– annual trends, during the period examined. A strong dependency of the albedo on land cover type was found, as it was expected. The results also revealed substantial spatiotemporal variability of the surface albedo in the area examined, highlighting the great potential of satellite remote sensing in supporting climate change related studies, at both local and regional scales.

Paper Details

Date Published: 21 October 2014
PDF: 8 pages
Proc. SPIE 9239, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI, 92390Q (21 October 2014); doi: 10.1117/12.2066473
Show Author Affiliations
Nikolaos Benas, Foundation for Research and Technology-Hellas (Greece)
Nektarios Chrysoulakis, Foundation for Research and Technology-Hellas (Greece)

Published in SPIE Proceedings Vol. 9239:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI
Christopher M. U. Neale; Antonino Maltese, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?