Mapping Surface Broadband Albedo from Satellite Observations: A Review of Literatures on Algorithms and Products

作者:Qu, Y (Qu, Ying)[ 1,2 ] ; Liang, SL (Liang, Shunlin)[ 1,2,3 ] ; Liu, Q (Liu, Qiang)[ 1,2 ] ; He, T (He, Tao)[ 3 ] ; Liu, SH (Liu, Suhong)[ 1,4 ] ; Li, XW (Li, Xiaowen)[ 1,4 ]

REMOTE SENSING

卷: 7  期: 1  页: 990-1020

DOI: 10.3390/rs70100990

出版年: JAN 2015

摘要

Surface albedo is one of the key controlling geophysical parameters in the surface energy budget studies, and its temporal and spatial variation is closely related to the global climate change and regional weather system due to the albedo feedback mechanism. As an efficient tool for monitoring the surfaces of the Earth, remote sensing is widely used for deriving long-term surface broadband albedo with various geostationary and polar-orbit satellite platforms in recent decades. Moreover, the algorithms for estimating surface broadband albedo from satellite observations, including narrow-to-broadband conversions, bidirectional reflectance distribution function (BRDF) angular modeling, direct-estimation algorithm and the algorithms for estimating albedo from geostationary satellite data, are developed and improved. In this paper, we present a comprehensive literature review on algorithms and products for mapping surface broadband albedo with satellite observations and provide a discussion of different algorithms and products in a historical perspective based on citation analysis of the published literature. This paper shows that the observation technologies and accuracy requirement of applications are important, and long-term, global fully-covered (including land, ocean, and sea-ice surfaces), gap-free, surface broadband albedo products with higher spatial and temporal resolution are required for climate change, surface energy budget, and hydrological studies.

通讯作者地址: Qu, Y (通讯作者)

Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China

[ 3 ] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA

[ 4 ] Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China