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Yujie Wang
Researcher at University of Maryland, Baltimore County
Publications - 64
Citations - 5886
Yujie Wang is an academic researcher from University of Maryland, Baltimore County. The author has contributed to research in topics: Atmospheric correction & Moderate-resolution imaging spectroradiometer. The author has an hindex of 33, co-authored 55 publications receiving 4281 citations. Previous affiliations of Yujie Wang include Goddard Space Flight Center.
Papers
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Journal ArticleDOI
Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm
Alexei Lyapustin,Alexei Lyapustin,Yujie Wang,Yujie Wang,Istvan Laszlo,Ralph A. Kahn,Sergey Korkin,Sergey Korkin,Lorraine A. Remer,Robert C. Levy,Jeffrey S. Reid +10 more
TL;DR: In this paper, an aerosol component of a new multiangle implementation of atmospheric correction (MAIAC) algorithm is presented, which performs aerosol retrievals and atmospheric correction over both dark vegetated surfaces and bright deserts based on a time series analysis and image-based processing.
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MODIS Collection 6 MAIAC Algorithm
TL;DR: In this paper, the authors describe the latest version of the algorithm MAIAC used for processing the MODIS Collection 6 data record, which has changed considerably to adapt to global processing and improve cloud/snow detection, aerosol retrievals and atmospheric correction of MODIS data.
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Assessing PM2.5 Exposures with High Spatiotemporal Resolution across the Continental United States.
TL;DR: This model uses convolutional layers, which aggregate neighboring information, into a neural network to account for spatial and temporal autocorrelation and allows epidemiologists to access PM2.5 exposure in both the short-term and the long-term.
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An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution
Qian Di,Qian Di,Heresh Amini,Liuhua Shi,Itai Kloog,Rachel F. Silvern,James T. Kelly,M. Benjamin Sabath,Christine Choirat,Petros Koutrakis,Alexei Lyapustin,Yujie Wang,Loretta J. Mickley,Joel Schwartz +13 more
TL;DR: An ensemble model that integrated multiple machine learning algorithms and predictor variables to estimate daily PM2.5 at a resolution of 1’km × 1 km across the contiguous United States allows epidemiologists to accurately estimate the adverse health effect of PM 2.5.
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Estimating Ground-Level PM(sub 2.5) Concentrations in the Southeastern United States Using MAIAC AOD Retrievals and a Two-Stage Model
Xuefei Hu,Lance A. Waller,Alexei Lyapustin,Yujie Wang,Yujie Wang,Mohammad Z. Al-Hamdan,William L. Crosson,Maurice G. Estes,Sue Estes,Dale A. Quattrochi,Sweta Jinnagara Puttaswamy,Yang Liu +11 more
TL;DR: In this paper, a new aerosol product with 1 km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields and land use parameters (e.g., forest cover, road length, elevation, and point emissions).