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Remote Sens. 2016, 8(3), 222;

GLASS Daytime All-Wave Net Radiation Product: Algorithm Development and Preliminary Validation

State Key Laboratory of Remote Sensing Science, and School of Geography, Beijing Normal University, Beijing 100875, China
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Author to whom correspondence should be addressed.
Academic Editors: Parth Sarathi Roy and Prasad S. Thenkabail
Received: 25 January 2016 / Revised: 24 February 2016 / Accepted: 4 March 2016 / Published: 9 March 2016
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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. 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. View Full-Text
Keywords: net radiation; GLASS products; remote sensing; satellite net radiation; GLASS products; remote sensing; satellite

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Jiang, B.; Liang, S.; Ma, H.; Zhang, X.; Xiao, Z.; Zhao, X.; Jia, K.; Yao, Y.; Jia, A. GLASS Daytime All-Wave Net Radiation Product: Algorithm Development and Preliminary Validation. Remote Sens. 2016, 8, 222.

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