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

Jiang, Bo; Liang, Shunlin; Ma, Han; Zhang, Xiaotong; Xiao, Zhiqiang; Zhao, Xiang; Jia, Kun; Yao, Yunjun; Jia, Aolin

doi:10.3390/rs8030222

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

Journal Article · Wed Mar 09 00:00:00 EST 2016 · Remote Sensing

DOI:https://doi.org/10.3390/rs8030222· OSTI ID:1258030

Jiang, Bo ^[1]; Liang, Shunlin ^[2]; Ma, Han ^[1]; Zhang, Xiaotong ^[1]; Xiao, Zhiqiang ^[1]; Zhao, Xiang ^[1]; Jia, Kun ^[1]; Yao, Yunjun ^[1]; Jia, Aolin ^[1]

Beijing Normal University, Beijing, (China). State Key Laboratory of Remote Sensing Science and School of Geography
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

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.

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Research Organization:: Oregon State Univ., Corvallis, OR (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: FG02-04ER63917

OSTI ID:: 1258030

Journal Information:: Remote Sensing, Vol. 8, Issue 3; ISSN 2072-4292

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 29 works

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