Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China: Methods and assessment
TLDR
Wang et al. as mentioned in this paper developed a generalized regression neural network (GRNN) model to estimate PM2.5 concentrations at a national scale, and different assessment experiments are undertaken in time and space, to comprehensively evaluate and compare the performance of widely used models.About:
This article is published in Atmospheric Environment.The article was published on 2017-03-01 and is currently open access. It has received 168 citations till now. The article focuses on the topics: Linear regression.read more
Citations
More filters
Journal ArticleDOI
Deep learning in environmental remote sensing: Achievements and challenges
Qiangqiang Yuan,Huanfeng Shen,Tongwen Li,Zhiwei Li,Shuwen Li,Yun Jiang,Hongzhang Xu,Weiwei Tan,Qianqian Yang,Jiwen Wang,Jianhao Gao,Liangpei Zhang +11 more
TL;DR: The potential of DL in environmental remote sensing, including land cover mapping, environmental parameter retrieval, data fusion and downscaling, and information reconstruction and prediction, will be analyzed and a typical network structure will be introduced.
Journal ArticleDOI
Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach
Journal ArticleDOI
Estimating Ground-Level PM2.5 by Fusing Satellite and Station Observations: A Geo-Intelligent Deep Learning Approach
TL;DR: In this article, a geo-intelligent approach, which incorporates geographical correlation into an intelligent deep learning architecture, is developed to estimate PM2.5 in a deep belief network (denoted as Geoi-DBN).
Journal ArticleDOI
Improved 1 km resolution PM 2.5 estimates across China using enhanced space–time extremely randomized trees
Jing Wei,Jing Wei,Zhanqing Li,Maureen Cribb,Wei Huang,Wenhao Xue,Lin Sun,Jianping Guo,Yiran Peng,Jing Li,Alexei Lyapustin,Lei Liu,Hao Wu,Yimeng Song +13 more
TL;DR: In this paper, the space-time extremely randomized trees (STET) model was enhanced by integrating updated spatiotemporal information and additional auxiliary data to improve the spatial resolution and overall accuracy of PM 2.5 estimates across mainland China.
Journal ArticleDOI
Satellite-based mapping of daily high-resolution ground PM 2.5 in China via space-time regression modeling
Qingqing He,Bo Huang +1 more
TL;DR: In this article, a space-time regression model that is an improved geographically and temporally weighted regression (GTWR) with an interior point algorithm (IPA)-based efficient mechanism for selecting optimal parameter values, was developed to estimate a large set of daily PM2.5 concentrations.
References
More filters
Journal ArticleDOI
MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications
Michele M. Rienecker,Max J. Suarez,Ronald Gelaro,Ricardo Todling,Julio T. Bacmeister,Julio T. Bacmeister,Emily Liu,Emily Liu,Michael G. Bosilovich,Siegfried D. Schubert,Lawrence L. Takacs,Lawrence L. Takacs,Gi-Kong Kim,S. C. Bloom,S. C. Bloom,Junye Chen,Junye Chen,Douglas Collins,Douglas Collins,Austin Conaty,Austin Conaty,Arlindo da Silva,Wei Gu,Wei Gu,Joanna Joiner,Randal D. Koster,Robert A. Lucchesi,Robert A. Lucchesi,Andrea Molod,Andrea Molod,Tommy Owens,Tommy Owens,Steven Pawson,Philip Pegion,Philip Pegion,Christopher R. Redder,Christopher R. Redder,Rolf H. Reichle,Franklin R. Robertson,Albert G. Ruddick,Albert G. Ruddick,Meta Sienkiewicz,Meta Sienkiewicz,John S. Woollen +43 more
TL;DR: The Modern-Era Retrospective Analysis for Research and Applications (MERRA) was undertaken by NASA's Global Modeling and Assimilation Office with two primary objectives: to place observations from NASA's Earth Observing System satellites into a climate context and to improve upon the hydrologic cycle represented in earlier generations of reanalyses as mentioned in this paper.
Journal ArticleDOI
A general regression neural network
TL;DR: The general regression neural network (GRNN) is a one-pass learning algorithm with a highly parallel structure that provides smooth transitions from one observed value to another.
Journal ArticleDOI
The MODIS Aerosol Algorithm, Products and Validation
Lorraine A. Remer,Yoram J. Kaufman,Didier Tanré,Shana Mattoo,D. A. Chu,J. V. Martins,Rong-Rong Li,Charles Ichoku,Robert C. Levy,Richard G. Kleidman,Thomas F. Eck,Eric Vermote,Brent N. Holben +12 more
TL;DR: In this article, the spectral optical thickness and effective radius of the aerosol over the ocean were validated by comparison with two years of Aerosol Robotic Network (AERONET) data.
Journal ArticleDOI
Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences
M.W. Gardner,Stephen Dorling +1 more
TL;DR: This paper presents a general introduction and discussion of recent applications of the multilayer perceptron, one type of artificial neural network, in the atmospheric sciences.
Journal ArticleDOI
The Collection 6 MODIS aerosol products over land and ocean
Robert C. Levy,Shana Mattoo,L. A. Munchak,Lorraine A. Remer,Andrew M. Sayer,Andrew M. Sayer,Falguni Patadia,Falguni Patadia,N. C. Hsu +8 more
TL;DR: The Collection 6 (C6) algorithm as mentioned in this paper was proposed to retrieve aerosol optical depth (AOD) and aerosol size parameters from MODIS-observed spectral reflectance.