Estimation of land surface albedo time series and trends based on MODIS data

Nikolaos Benas; Nektarios Chrysoulakis

doi:10.1117/12.2066473

21 October 2014 Estimation of land surface albedo time series and trends based on MODIS data

Nikolaos Benas, Nektarios Chrysoulakis

Proceedings Volume 9239, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI; 92390Q (2014) https://doi.org/10.1117/12.2066473
Event: SPIE Remote Sensing, 2014, Amsterdam, Netherlands

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.

Citation Download Citation

Nikolaos Benas and Nektarios Chrysoulakis "Estimation of land surface albedo time series and trends based on MODIS data", Proc. SPIE 9239, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI, 92390Q (21 October 2014); https://doi.org/10.1117/12.2066473

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available