TY - JOUR
T1 - Long-term record of top-of-atmosphere albedo over land generated from AVHRR data
AU - Song, Zhen
AU - Liang, Shunlin
AU - Wang, Dongdong
AU - Zhou, Yuan
AU - Jia, Aolin
N1 - Funding Information:
This study was partially funded by The National Key Research and Development Program of China (NO. 2016YFA0600101 ) and the Chinese Scholarship Council . We thank GLASS science team in Beijing Normal University for providing the MODIS TOA albedo product, NASA/GSFC for providing AVHRR data and ( https://ltdr.nascom.nasa.gov /) CERES data ( https://ceres.larc.nasa.gov ), CM SAF for CM SAF flux data ( http://www.cmsaf.eu /) and JASMES for AVHRR snow cover data ( http://kuroshio.eorc.jaxa.jp/JASMES/index.html ). The first version of TAL-AVHRR (TAL-AVHRR V1.0) has been produced and updated to Aug, 2017 and the data are published at http://www.geodata.cn / and http://glcf.umd.edu/data . We are very grateful for all the anonymous reviewers for their valuable comments and suggestions.
Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2018/6/15
Y1 - 2018/6/15
N2 - Top-of-atmosphere (TOA) albedo is a fundamental component of Earth's energy budget. To date, long-term global land TOA albedo products with spatial resolution higher than 20-km are not available. In this study, we propose a novel algorithm to retrieve TOA albedo from multispectral imager observations acquired by Advanced Very High Resolution Radiometer (AVHRR), which provides the longest continuous record of global satellite observations since 1981. Direct estimation models were established first to derive instantaneous TOA broadband albedo under various atmospheric and surface conditions, including cloudy-sky, clear-sky (snow-free) and snow-cover conditions. To perform long-term series analysis, the instantaneous TOA albedo were then converted to daily/monthly mean values based on the diurnal curves from multi-year Clouds and the Earth's Radiant Energy System (CERES) 3-hourly flux dataset. Calibration differences between sequential AVHRR sensors were further mitigated by invariant targets normalization. The retrieved TOA albedo at 0.05° × 0.05° was validated against two TOA albedo datasets, CM SAF (Climate Monitoring Satellite Application Facility) flux data and CERES flux data, at spatial resolutions of 0.05° × 0.05° 20 km × 20 km and 1° × 1°. The instantaneous TOA albedo had an overall Root-Mean-Square-Error (RMSE) of 0.047 when compared with 20-km CERES fluxes, whereas the 1° by 1° monthly mean TOA albedo showed closer agreements with both CM SAF and CERES, with RMSE ranging from 0.029 to 0.040 and from 0.022 to 0.031, respectively. Moreover, our product was found to be highly consistent with both CERES and CM SAF at long-term trend detection. The extensive validation indicated the robustness of our algorithm and subsequently, comparable data quality with existing datasets. With global coverage and long time series (1981–2017), our product is expected to provide valuable information for climate change studies.
AB - Top-of-atmosphere (TOA) albedo is a fundamental component of Earth's energy budget. To date, long-term global land TOA albedo products with spatial resolution higher than 20-km are not available. In this study, we propose a novel algorithm to retrieve TOA albedo from multispectral imager observations acquired by Advanced Very High Resolution Radiometer (AVHRR), which provides the longest continuous record of global satellite observations since 1981. Direct estimation models were established first to derive instantaneous TOA broadband albedo under various atmospheric and surface conditions, including cloudy-sky, clear-sky (snow-free) and snow-cover conditions. To perform long-term series analysis, the instantaneous TOA albedo were then converted to daily/monthly mean values based on the diurnal curves from multi-year Clouds and the Earth's Radiant Energy System (CERES) 3-hourly flux dataset. Calibration differences between sequential AVHRR sensors were further mitigated by invariant targets normalization. The retrieved TOA albedo at 0.05° × 0.05° was validated against two TOA albedo datasets, CM SAF (Climate Monitoring Satellite Application Facility) flux data and CERES flux data, at spatial resolutions of 0.05° × 0.05° 20 km × 20 km and 1° × 1°. The instantaneous TOA albedo had an overall Root-Mean-Square-Error (RMSE) of 0.047 when compared with 20-km CERES fluxes, whereas the 1° by 1° monthly mean TOA albedo showed closer agreements with both CM SAF and CERES, with RMSE ranging from 0.029 to 0.040 and from 0.022 to 0.031, respectively. Moreover, our product was found to be highly consistent with both CERES and CM SAF at long-term trend detection. The extensive validation indicated the robustness of our algorithm and subsequently, comparable data quality with existing datasets. With global coverage and long time series (1981–2017), our product is expected to provide valuable information for climate change studies.
KW - AVHRR
KW - CERES
KW - CM SAF
KW - Climate change
KW - Earth's energy budget
KW - TOA albedo
UR - http://www.scopus.com/inward/record.url?scp=85045253461&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2018.03.044
DO - 10.1016/j.rse.2018.03.044
M3 - Article
AN - SCOPUS:85045253461
VL - 211
SP - 71
EP - 88
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
SN - 0034-4257
ER -