Surface Daytime Net Radiation Estimation Using Artificial Neural Networks

Jiang, Bo; Zhang, Yi; Liang, Shunlin; Zhang, Xiaolin; Xiao, Zhiqiang

doi:10.3390/rs61111031

Cited by 35 publications

(21 citation statements)

References 44 publications

(51 reference statements)

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“…Remote sensing retrieval has become one of the most important methods for obtaining the radiation data [4]. However, clouds make it impossible to retrieve surface thermal radiation components directly from satellite observations, and the alternative solution is to estimate the all-wave net radiation from the satellite shortwave net radiation product, in conjunction with other information [5,6].…”

Section: Introductionmentioning

confidence: 99%

Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product

et al. 2016

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Abstract:The Clouds and the Earth's Radiant Energy System (CERES) generates one of the few global satellite radiation products. The CERES ARM Validation Experiment (CAVE) has been providing long-term in situ observations for the validation of the CERES products. However, the number of these sites is low and their distribution is globally sparse, and particularly the surface net radiation product has not been rigorously validated yet. Therefore, additional validation efforts are highly required to determine the accuracy of the CERES radiation products. In this study, global land surface measurements were comprehensively collected for use in the validation of the CERES net radiation (R n ) product on a daily (340 sites) and a monthly (260 sites) basis, respectively. The validation results demonstrated that the CERES R n product was, overall, highly accurate. The daily validations had a Mean Bias Error (MBE) of 3.43 W¨m´2, Root Mean Square Error (RMSE) of 33.56 W¨m´2, and R 2 of 0.79, and the monthly validations had an MBE of 3.40 W¨m´2, RMSE of 25.57 W¨m´2, and R 2 of 0.84. The accuracy was slightly lower for the high latitudes. Following the validation, the monthly CERES R n product, from March 2000 to July 2014, was used for a further analysis. The global spatiotemporal variation of the R n , which occurred during the measurement period, was analyzed. In addition, two hot spot regions, the southern Great Plains and south-central Africa, were then selected for use in determining the driving factors or attribution of the R n variation. We determined that R n over the southern Great Plains decreased by´0.33 W¨m´2 per year, which was mainly driven by changes in surface green vegetation and precipitation. In south-central Africa, R n decreased at a rate of´0.63 W¨m´2 per year, the major driving factor of which was surface green vegetation.

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Section: Introductionmentioning

confidence: 99%

Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product

et al. 2016

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“…The variables considered in this study are shown in Table 2. The readers are referred to Jiang et al [37] for more information about these data. R si * stands for the GLASS R si product and was used for global GLASS daytime R n production in this study.…”

Section: Datamentioning

confidence: 99%

“…Jiang et al [37] applied the GRNN model for daytime R n estimation, and the evaluation results proved that this model worked very well and was stable under various conditions. The architecture of the GRNN model used in this study is shown in Figure A1.…”

Section: B General Regression Neural Network (Grnn) Modelmentioning

confidence: 99%

“…Finally, the output layer performs a normalization step to yield the predicted value of the output variable. In accordance with Jiang et al [37] and Xiao et al [52], the Gaussian kernel function was used for GRNN training in the present study, and the smoothing parameter in the kernel function was the only free parameter that needed to be determined. Thus, GRNN training was essentially optimization of the smoothing parameter and the architecture and weights of a GRNN were determined when the input was given.…”

Section: B General Regression Neural Network (Grnn) Modelmentioning

confidence: 99%

“…The most practical solution is to estimate incident shortwave radiation directly from satellite observations and then convert it into all-wave net radiation using either linear [30][31][32][33][34] or nonlinear [35,36] models. Jiang et al [37] recently developed two artificial neuron networks models using comprehensive global in situ observations and found that the nonlinear models can produce better accuracy than the linear models. The primary objective of this study is to generate the accurate long-term high-resolution Global LAnd Surface Satellite (GLASS) R n product.…”

Section: Introductionmentioning

confidence: 99%

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

Jiang

Liang

et al. 2016

Remote Sensing

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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. 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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Comprehensive Assessment of Global Surface Net Radiation Products and Uncertainty Analysis

et al. 2018

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Earth surface net radiation (Rn) characterizes the surface radiation budget and plays a critical role in ecological, biogeochemical, and hydrological processes. The Rn products from remote sensing and reanalysis have not been validated comprehensively. In this study, four Rn products (Clouds and the Earth's Radiant Energy System [CERES], ERA‐Interim, Modern‐Era Retrospective analysis for Research and Applications version 2, and Japanese 55‐year Reanalysis) were validated using global ground measurements on monthly (255 sites) and annual (172 sites) timescales. These products have similar accuracies, with average root‐mean‐square error (RMSE) ranges of 5.35 W m−2 (monthly) and 2.30 W m−2 (annually). However, varying accuracies and systemic biases exist across different climatic zones. The annual land Rn intercomparison illustrates that large uncertainty exists over polar regions and deserts. A significantly negative annual anomaly in the CERES product for the 2001–2008 period is identified when examining annual Rn anomalies over the global land surface. Detailed uncertainty analysis indicates that the global CERES Rn anomaly is mainly due to different versions of input data such as aerosol optical thickness and atmospheric profiles (in 2006 and 2008) and cloud properties (in 2002). This work demonstrates that temporal analysis provides powerful quality control for global time series satellite products when the validation using ground measurements fails to capture potential issues.

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Surface Daytime Net Radiation Estimation Using Artificial Neural Networks

Cited by 35 publications

References 44 publications

Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product

Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product

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

Comprehensive Assessment of Global Surface Net Radiation Products and Uncertainty Analysis

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