What is he best known for?
The fields of study he is best known for:
- Statistics
- Meteorology
- Climate change
His scientific interests lie mostly in Remote sensing, Environmental science, Aerosol, Atmospheric correction and Meteorology.
Yujie Wang combines subjects such as Smoothing, Image resolution, Mixed model and Autocorrelation with his study of Remote sensing.
His Aerosol research incorporates themes from Statistics and Regression.
His research on Atmospheric correction often connects related areas such as Albedo.
Yujie Wang regularly ties together related areas like Moderate-resolution imaging spectroradiometer in his Meteorology studies.
His Algorithm research is multidisciplinary, relying on both Shortwave infrared, Reflectance factor and AERONET.
His most cited work include:
- Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm (283 citations)
- Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm (283 citations)
- Assessing PM2.5 Exposures with High Spatiotemporal Resolution across the Continental United States. (194 citations)
What are the main themes of his work throughout his whole career to date?
Environmental science, Remote sensing, Atmospheric correction, Moderate-resolution imaging spectroradiometer and Aerosol are his primary areas of study.
His Environmental science research includes elements of Meteorology, AERONET, Snow, Atmospheric sciences and Climatology.
His biological study spans a wide range of topics, including Primary production, Cloud cover, Vegetation and Bidirectional reflectance distribution function.
His work deals with themes such as Missing data, Albedo, Atmospheric radiative transfer codes, Radiative transfer and Algorithm, which intersect with Atmospheric correction.
His Moderate-resolution imaging spectroradiometer study combines topics from a wide range of disciplines, such as Spatial distribution and Normalized Difference Vegetation Index.
As part of one scientific family, Yujie Wang deals mainly with the area of Aerosol, narrowing it down to issues related to the Radiance, and often Surface pressure.
He most often published in these fields:
- Environmental science (125.61%)
- Remote sensing (101.22%)
- Atmospheric correction (73.17%)
What were the highlights of his more recent work (between 2018-2021)?
- Environmental science (125.61%)
- Remote sensing (101.22%)
- AERONET (26.83%)
In recent papers he was focusing on the following fields of study:
Yujie Wang mainly focuses on Environmental science, Remote sensing, AERONET, Aerosol and Gradient boosting.
His study in Environmental science intersects with areas of studies such as Water vapor, Moderate-resolution imaging spectroradiometer, Atmospheric correction, Meteorology and Vegetation.
His Moderate-resolution imaging spectroradiometer study integrates concerns from other disciplines, such as Lidar and Atmospheric model.
His study focuses on the intersection of Remote sensing and fields such as Snow with connections in the field of Atmospheric sciences.
His AERONET research integrates issues from Spatial ecology, Storm, Climate model and Spatial contextual awareness.
In general Aerosol, his work in Single-scattering albedo, Biomass burning and Mineral dust is often linked to EPIC and Spectral absorption linking many areas of study.
Between 2018 and 2021, his most popular works were:
- An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution (88 citations)
- Impacts of snow and cloud covers on satellite-derived PM2.5 levels. (31 citations)
- Impacts of snow and cloud covers on satellite-derived PM2.5 levels. (31 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Meteorology
- Climate change
Yujie Wang mainly investigates Environmental science, Gradient boosting, Statistics, Ensemble forecasting and Cloud cover.
His Gradient boosting research covers fields of interest such as Generalized additive model, Elevation, Standard deviation, Geographic difference and Air pollution.
His study in the field of Contrast also crosses realms of Estimation.
His Cloud cover study combines topics in areas such as Covariate, Snow, Meteorology, Predictive modelling and Remote sensing.
His work on Data set expands to the thematically related Covariate.
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