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Title: GLASS daytime all-wave net radiation product: Algorithm development and preliminary validation

Abstract

Mapping surface all-wave net radiation (R n) is critically needed for various applications. Several existing R n products from numerical models and satellite observations have coarse spatial resolutions and their accuracies may not meet the requirements of land applications. In this study, we develop the Global LAnd Surface Satellite (GLASS) daytime R n product at a 5 km spatial resolution. Its algorithm for converting shortwave radiation to all-wave net radiation using the Multivariate Adaptive Regression Splines (MARS) model is determined after comparison with three other algorithms. The validation of the GLASS R n product based on high-quality in situ measurements in the United States shows a coefficient of determination value of 0.879, an average root mean square error value of 31.61 Wm -2, and an average bias of 17.59 Wm -2. Furthermore, we also compare our product/algorithm with another satellite product (CERES-SYN) and two reanalysis products (MERRA and JRA55), and find that the accuracy of the much higher spatial resolution GLASS R n product is satisfactory. The GLASS R n product from 2000 to the present is operational and freely available to the public.

Authors:
 [1];  [2];  [1];  [1];  [1];  [1];  [1];  [1];  [1]
  1. Beijing Normal University, Beijing, (China). State Key Laboratory of Remote Sensing Science and School of Geography
  2. Beijing Normal University, Beijing, (China). State Key Laboratory of Remote Sensing Science and School of Geography; University of Maryland, College Park, MD, (United States). Department of Geographical Sciences
Publication Date:
Research Org.:
Oregon State Univ., Corvallis, OR (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1258030
Grant/Contract Number:  
FG02-04ER63917
Resource Type:
Accepted Manuscript
Journal Name:
Remote Sensing
Additional Journal Information:
Journal Volume: 8; Journal Issue: 3; Journal ID: ISSN 2072-4292
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 58 GEOSCIENCES; net radiation; GLASS products; remote sensing; satellite

Citation Formats

Jiang, Bo, Liang, Shunlin, Ma, Han, Zhang, Xiaotong, Xiao, Zhiqiang, Zhao, Xiang, Jia, Kun, Yao, Yunjun, and Jia, Aolin. GLASS daytime all-wave net radiation product: Algorithm development and preliminary validation. United States: N. p., 2016. Web. doi:10.3390/rs8030222.
Jiang, Bo, Liang, Shunlin, Ma, Han, Zhang, Xiaotong, Xiao, Zhiqiang, Zhao, Xiang, Jia, Kun, Yao, Yunjun, & Jia, Aolin. GLASS daytime all-wave net radiation product: Algorithm development and preliminary validation. United States. doi:10.3390/rs8030222.
Jiang, Bo, Liang, Shunlin, Ma, Han, Zhang, Xiaotong, Xiao, Zhiqiang, Zhao, Xiang, Jia, Kun, Yao, Yunjun, and Jia, Aolin. Wed . "GLASS daytime all-wave net radiation product: Algorithm development and preliminary validation". United States. doi:10.3390/rs8030222. https://www.osti.gov/servlets/purl/1258030.
@article{osti_1258030,
title = {GLASS daytime all-wave net radiation product: Algorithm development and preliminary validation},
author = {Jiang, Bo and Liang, Shunlin and Ma, Han and Zhang, Xiaotong and Xiao, Zhiqiang and Zhao, Xiang and Jia, Kun and Yao, Yunjun and Jia, Aolin},
abstractNote = {Mapping surface all-wave net radiation (Rn) is critically needed for various applications. Several existing Rn products from numerical models and satellite observations have coarse spatial resolutions and their accuracies may not meet the requirements of land applications. In this study, we develop the Global LAnd Surface Satellite (GLASS) daytime Rn product at a 5 km spatial resolution. Its algorithm for converting shortwave radiation to all-wave net radiation using the Multivariate Adaptive Regression Splines (MARS) model is determined after comparison with three other algorithms. The validation of the GLASS Rn product based on high-quality in situ measurements in the United States shows a coefficient of determination value of 0.879, an average root mean square error value of 31.61 Wm-2, and an average bias of 17.59 Wm-2. Furthermore, we also compare our product/algorithm with another satellite product (CERES-SYN) and two reanalysis products (MERRA and JRA55), and find that the accuracy of the much higher spatial resolution GLASS Rn product is satisfactory. The GLASS Rn product from 2000 to the present is operational and freely available to the public.},
doi = {10.3390/rs8030222},
journal = {Remote Sensing},
number = 3,
volume = 8,
place = {United States},
year = {2016},
month = {3}
}

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