Deriving albedo over cloudy areas with composite inversion

Aijun Chen; Lingen Bian; Yujie Liu

doi:10.1117/12.763606

25 July 2007 Deriving albedo over cloudy areas with composite inversion

Aijun Chen, Lingen Bian, Yujie Liu

Proceedings Volume 6753, Geoinformatics 2007: Geospatial Information Science; 675337 (2007) https://doi.org/10.1117/12.763606
Event: Geoinformatics 2007, 2007, Nanjing, China

Abstract

A new way called compositive inversion is presented in the paper to derive albedo over cloudy areas with multi-angular satellite remote sensing data. It combines complementary angular clear observations of pixels having same BRDF shape and directly retrieves BRDF parameters and albedo with RossThick-LiSparse Reciprocal model when the minimum multi-angular observations meet its requirement. Contrastive retrieval experiments with five continuous 16-day Terra MODIS data over the Tibetan Plateau (30 cases in total) showed that its retrieval capability is much higher than that of the magnitude inversion, a backup algorithm adopted by the U.S. BRDF/albedo products, and accuracy of its retrievals is rather equivalent to those with magnitude inversion. Since the unique ancillary data used in which is the land-cover type classification data, which is superior over a priori BRDF database, so the compositive inversion can give more accurate albedo information, and is an applicable and practical way to derive albedo of cloudy areas. The idea of compositive inversion provides a new way to derive albedo in shorter temporal cycle and a particular view for use of multi-angular remote sensing data to derive land surface information.

Citation Download Citation

Aijun Chen, Lingen Bian, and Yujie Liu "Deriving albedo over cloudy areas with composite inversion", Proc. SPIE 6753, Geoinformatics 2007: Geospatial Information Science, 675337 (25 July 2007); https://doi.org/10.1117/12.763606

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