Mapping Surface Broadband Albedo from Satellite Observations: A Review of Literatures on Algorithms and Products
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 s...
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ftmdpi:oai:mdpi.com:/2072-4292/7/1/990/ 2023-08-20T04:09:45+02:00 Mapping Surface Broadband Albedo from Satellite Observations: A Review of Literatures on Algorithms and Products Ying Qu Shunlin Liang Qiang Liu Tao He Suhong Liu Xiaowen Li agris 2015-01-16 application/pdf https://doi.org/10.3390/rs70100990 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs70100990 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 7; Issue 1; Pages: 990-1020 surface albedo Bidirectional Reflectance Distribution Function (BRDF) remote sensing surface energy budget Global Land Surface Satellite (GLASS) Text 2015 ftmdpi https://doi.org/10.3390/rs70100990 2023-07-31T20:41:22Z 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. Text Sea ice MDPI Open Access Publishing Remote Sensing 7 1 990 1020 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
surface albedo Bidirectional Reflectance Distribution Function (BRDF) remote sensing surface energy budget Global Land Surface Satellite (GLASS) |
spellingShingle |
surface albedo Bidirectional Reflectance Distribution Function (BRDF) remote sensing surface energy budget Global Land Surface Satellite (GLASS) Ying Qu Shunlin Liang Qiang Liu Tao He Suhong Liu Xiaowen Li Mapping Surface Broadband Albedo from Satellite Observations: A Review of Literatures on Algorithms and Products |
topic_facet |
surface albedo Bidirectional Reflectance Distribution Function (BRDF) remote sensing surface energy budget Global Land Surface Satellite (GLASS) |
description |
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. |
format |
Text |
author |
Ying Qu Shunlin Liang Qiang Liu Tao He Suhong Liu Xiaowen Li |
author_facet |
Ying Qu Shunlin Liang Qiang Liu Tao He Suhong Liu Xiaowen Li |
author_sort |
Ying Qu |
title |
Mapping Surface Broadband Albedo from Satellite Observations: A Review of Literatures on Algorithms and Products |
title_short |
Mapping Surface Broadband Albedo from Satellite Observations: A Review of Literatures on Algorithms and Products |
title_full |
Mapping Surface Broadband Albedo from Satellite Observations: A Review of Literatures on Algorithms and Products |
title_fullStr |
Mapping Surface Broadband Albedo from Satellite Observations: A Review of Literatures on Algorithms and Products |
title_full_unstemmed |
Mapping Surface Broadband Albedo from Satellite Observations: A Review of Literatures on Algorithms and Products |
title_sort |
mapping surface broadband albedo from satellite observations: a review of literatures on algorithms and products |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2015 |
url |
https://doi.org/10.3390/rs70100990 |
op_coverage |
agris |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Remote Sensing; Volume 7; Issue 1; Pages: 990-1020 |
op_relation |
https://dx.doi.org/10.3390/rs70100990 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs70100990 |
container_title |
Remote Sensing |
container_volume |
7 |
container_issue |
1 |
container_start_page |
990 |
op_container_end_page |
1020 |
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1774723422398447616 |