Comparison between deep learning architectures for the 1 km, 10/15-min estimation of downward shortwave radiation from AHI and ABI
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[1] R. Nemani,et al. A GeoNEX-based high-spatiotemporal-resolution product of land surface downward shortwave radiation and photosynthetically active radiation , 2023, Earth System Science Data.
[2] S. Liang,et al. Global hourly, 5 km, all-sky land surface temperature data from 2011 to 2021 based on integrating geostationary and polar-orbiting satellite data , 2022 .
[3] S. Liang,et al. Generating a 2-km, all-sky, hourly land surface temperature product from Advanced Baseline Imager data , 2022, Remote Sensing of Environment.
[4] S. Liang,et al. Global Daily Actual and Snow‐Free Blue‐Sky Land Surface Albedo Climatology From 20‐Year MODIS Products , 2022, Journal of Geophysical Research: Atmospheres.
[5] Trapit Bansal,et al. A moment in the sun: solar nowcasting from multispectral satellite data using self-supervised learning , 2021, e-Energy.
[6] S. Liang,et al. Comprehensive assessment of five global daily downward shortwave radiation satellite products , 2021 .
[7] Qian Yu,et al. High-spatiotemporal-resolution estimation of solar energy component in the United States using a new satellite-based model. , 2021, Journal of environmental management.
[8] Qian Yu,et al. Estimating half-hourly solar radiation over the Continental United States using GOES-16 data with iterative random forest , 2021 .
[9] Liangfu Chen,et al. A new benchmark for surface radiation products over the East Asia-Pacific region retrieved from the Himawari-8/AHI next-generation geostationary satellite , 2021, Bulletin of the American Meteorological Society.
[10] S. Davis,et al. Geophysical constraints on the reliability of solar and wind power worldwide , 2021, Nature Communications.
[11] S. Liang,et al. A synergic study on estimating surface downward shortwave radiation from satellite data , 2021 .
[12] Xiong Liu,et al. Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China , 2021, Remote Sensing of Environment.
[13] Qinhuo Liu,et al. Retrieving high-resolution surface photosynthetically active radiation from the MODIS and GOES-16 ABI data , 2021, Remote Sensing of Environment.
[14] Galen Maclaurin,et al. A physical downscaling algorithm for the generation of high-resolution spatiotemporal solar irradiance data , 2021 .
[15] Dhivya Sampath Kumar,et al. Review of power system impacts at high PV penetration Part I: Factors limiting PV penetration , 2020 .
[16] Hou Jiang,et al. Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data , 2020 .
[17] Atsushi Higuchi,et al. An Introduction to the Geostationary-NASA Earth Exchange (GeoNEX) Products: 1. Top-of-Atmosphere Reflectance and Brightness Temperature , 2020, Remote. Sens..
[18] Tao He,et al. Intercomparison of Machine-Learning Methods for Estimating Surface Shortwave and Photosynthetically Active Radiation , 2020, Remote. Sens..
[19] Yi Zhang,et al. A New Set of MODIS Land Products (MCD18): Downward Shortwave Radiation and Photosynthetically Active Radiation , 2020, Remote. Sens..
[20] Xin Li,et al. A 16-year dataset (2000–2015) of high-resolution (3 h, 10 km) global surface solar radiation , 2019 .
[21] X. Xiong,et al. GOES‐16/ABI Thermal Emissive Band Assessments Using GEO‐LEO‐GEO Double Difference , 2019, Earth and space science.
[22] Shunlin Liang,et al. Estimating surface solar irradiance from satellites: Past, present, and future perspectives , 2019, Remote Sensing of Environment.
[23] Hou Jiang,et al. A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data , 2019, Renewable and Sustainable Energy Reviews.
[24] Qing Xiao,et al. Estimating hourly land surface downward shortwave and photosynthetically active radiation from DSCOVR/EPIC observations , 2019, Remote Sensing of Environment.
[25] Rune Hylsberg Jacobsen,et al. A cloud detection algorithm for satellite imagery based on deep learning , 2019, Remote Sensing of Environment.
[26] Dengfeng Chai,et al. Cloud and cloud shadow detection in Landsat imagery based on deep convolutional neural networks , 2019, Remote Sensing of Environment.
[27] Shunlin Liang,et al. Remote sensing of earth’s energy budget: synthesis and review , 2019, Int. J. Digit. Earth.
[28] Guangjian Yan,et al. Spatial Scale Consideration for Estimating All-Sky Surface Shortwave Radiation With a Modified 1-D Radiative Transfer Model , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[29] Mingguo Ma,et al. Toward a Broadband Parameterization Scheme for Estimating Surface Solar Irradiance: Development and Preliminary Results on MODIS Products , 2018, Journal of Geophysical Research: Atmospheres.
[30] Alex Sherstinsky,et al. Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network , 2018, Physica D: Nonlinear Phenomena.
[31] Ram Rajagopal,et al. Data-driven planning of distributed energy resources amidst socio-technical complexities , 2017, Nature Energy.
[32] Bo Gao,et al. Effect of Solar-Cloud-Satellite Geometry on Land Surface Shortwave Radiation Derived from Remotely Sensed Data , 2017, Remote. Sens..
[33] J. F. Meirink,et al. An intercomparison and validation of satellite‐based surface radiative energy flux estimates over the Arctic , 2017 .
[34] Karl-Göran Karlsson,et al. CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data , 2016 .
[35] S. Pfenninger,et al. Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data , 2016 .
[36] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Chunlin Huang,et al. Representativeness errors of point-scale ground-based solar radiation measurements in the validation of remote sensing products , 2016 .
[38] Markus Reichstein,et al. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms , 2016 .
[39] Jan Kleissl,et al. High PV penetration impacts on five local distribution networks using high resolution solar resource assessment with sky imager and quasi-steady state distribution system simulations , 2016 .
[40] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[41] Qinhuo Liu,et al. A method for estimating hourly photosynthetically active radiation (PAR) in China by combining geostationary and polar-orbiting satellite data , 2015 .
[42] N. Loeb,et al. CERES Synoptic Product: Methodology and Validation of Surface Radiant Flux , 2015 .
[43] Sunny Sun-Mack,et al. Effects of 3‐D clouds on atmospheric transmission of solar radiation: Cloud type dependencies inferred from A‐train satellite data , 2014 .
[44] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[45] T. Andrews,et al. An update on Earth's energy balance in light of the latest global observations , 2012 .
[46] T. Nakajima,et al. Estimation of solar radiation using a neural network based on radiative transfer , 2011 .
[47] Taiping Zhang,et al. Assessment of BSRN radiation records for the computation of monthly means , 2010 .
[48] Xiaotong Zhang,et al. Review on Estimation of Land Surface Radiation and Energy Budgets From Ground Measurement, Remote Sensing and Model Simulations , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[49] C. Long,et al. Cirrus cloud radiative effect on surface-level shortwave and longwave irradiances at regional and global scale , 2009 .
[50] Hongliang Fang,et al. Estimation of incident photosynthetically active radiation from Moderate Resolution Imaging Spectrometer data , 2006 .
[51] William O'Hirok,et al. A simple method for removing 3-D radiative effects in satellite retrievals of surface irradiance , 2005 .
[52] Patrick E. Van Laake,et al. Simplified atmospheric radiative transfer modelling for estimating incident PAR using MODIS atmosphere products , 2004 .
[53] N. C. Strugnell,et al. First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .
[54] I. Sobola,et al. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .
[55] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[56] Kuldip K. Paliwal,et al. Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..
[57] J J Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.
[58] Chao Zhang,et al. Seismic Signal Matching and Complex Noise Suppression by Zernike Moments and Trilateral Weighted Sparse Coding , 2022, IEEE Transactions on Geoscience and Remote Sensing.
[59] S. Liang,et al. Estimation of Land Surface Downward Shortwave Radiation Using Spectral-Based Convolutional Neural Network Methods: A Case Study From the Visible Infrared Imaging Radiometer Suite Images , 2022, IEEE Transactions on Geoscience and Remote Sensing.
[60] Jiancheng Shi,et al. Estimation of shortwave solar radiation using the artificial neural network from Himawari-8 satellite imagery over China , 2020 .
[61] Hideki Kobayashi,et al. MODIS-derived global land products of shortwave radiation and diffuse and total photosynthetically active radiation at 5 km resolution from 2000 , 2018 .
[62] S. Liang,et al. Evaluating land surface albedo estimation from Landsat MSS, TM, ETM +, and OLI data based on the unified direct estimation approach , 2018 .
[63] L. Marroyo,et al. Storage requirements for PV power ramp-rate control , 2014 .
[64] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[65] Robert Frouin,et al. A review of satellite methods to derive surface shortwave irradiance , 1995 .