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Environmental Sciences

D-Index
58
Citations
15071
World Ranking
3223
National Ranking
21

Overview

Jungho Im is affiliated with the Ulsan National Institute of Science and Technology in South Korea. Their research primarily focuses on environmental science and earth and planetary sciences, with a substantial number of publications in related subfields.

The main fields of study covered in their work include:

  • Environmental Science
  • Earth and Planetary Sciences

Within these broader areas, their research extends into specific subfields such as:

  • Atmospheric Science
  • Global and Planetary Change
  • Environmental Engineering
  • Oceanography
  • Ecology

The topics frequently addressed in their publications include:

  • Atmospheric chemistry and aerosols
  • Meteorological Phenomena and Simulations
  • Climate variability and models
  • Air Quality Monitoring and Forecasting
  • Remote Sensing in Agriculture
  • Atmospheric aerosols and clouds
  • Remote Sensing and LiDAR Applications

Their recent papers illustrate the application of machine learning and remote sensing technologies to environmental and atmospheric issues. Notable papers include:

  • Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia (2021, Environmental Pollution)
  • Comparative Assessment of Various Machine Learning-Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas (2020, Earth and Space Science)
  • Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks (2020, The Cryosphere)
  • On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradient (2021, Agricultural and Forest Meteorology)
  • Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia (2021, ISPRS Journal of Photogrammetry and Remote Sensing)

Im's frequent coauthors comprise several collaborators, including:

  • Cheolhee Yoo
  • Dongjin Cho
  • Daehyeon Han
  • Yoojin Kang
  • Seohui Park

Their publications appear regularly in specific scientific venues such as:

  • Remote Sensing
  • GIScience & Remote Sensing
  • Scholarworks@UNIST (Ulsan National Institute of Science and Technology)
  • Remote Sensing of Environment
  • International Journal of Applied Earth Observation and Geoinformation

Best Publications

  • Support vector machines in remote sensing: A review

    Giorgos Mountrakis;Jungho Im;Caesar Ogole

  • Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data

    Jinyoung Rhee;Jungho Im;Gregory J. Carbone

  • Object-based change detection using correlation image analysis and image segmentation

    J. Im;J. R. Jensen;J. A. Tullis

  • A change detection model based on neighborhood correlation image analysis and decision tree classification

    Jungho Im;John R. Jensen

  • Forest biomass estimation from airborne LiDAR data using machine learning approaches

    Colin J. Gleason;Jungho Im;Jungho Im

  • Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification

    Yinghai Ke;Lindi J. Quackenbush;Jungho Im

  • Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions

    Seonyoung Park;Jungho Im;Eunna Jang;Jinyoung Rhee

  • Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations

    Yinghai Ke;Jungho Im;Junghee Lee;Huili Gong

  • Meteorological drought forecasting for ungauged areas based on machine learning: Using long-range climate forecast and remote sensing data

    Jinyoung Rhee;Jungho Im

  • Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches

    Jungho Im;Seonyoung Park;Jinyoung Rhee;Jongjin Baik

  • Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images

    Cheolhee Yoo;Daehyeon Han;Jungho Im;Benjamin Bechtel

  • Machine learning approaches to coastal water quality monitoring using GOCI satellite data

    Yong Hoon Kim;Jungho Im;Ho Kyung Ha;Jong-Kuk Choi

  • Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula

    Seonyoung Park;Jungho Im;Sumin Park;Jinyoung Rhee

  • Downscaling of MODIS One Kilometer Evapotranspiration Using Landsat-8 Data and Machine Learning Approaches

    Yinghai Ke;Jungho Im;Seonyoung Park;Huili Gong

  • Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia.

    Yoojin Kang;Hyunyoung Choi;Jungho Im;Seohui Park

  • Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data

    Cheolhee Yoo;Jungho Im;Seonyoung Park;Lindi J. Quackenbush

  • Comparative Assessment of Various Machine Learning-Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas

    Dongjin Cho;Cheolhee Yoo;Jungho Im;Dong‐Hyun Cha

  • Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest

    Manqi Li;Jungho Im;Colin Beier

  • Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data

    Seonyoung Park;Jungho Im;Seohui Park;Cheolhee Yoo

  • Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate

    Nishan Bhattarai;Nishan Bhattarai;Stephen B. Shaw;Lindi J. Quackenbush;Jungho Im

  • Hyperspectral Remote Sensing of Vegetation

    Jungho Im;John R. Jensen

  • Characteristics of Landsat 8 OLI-derived NDVI by comparison with

    Yinghai Ke;Junghee Lee;Youngryel Ryu;Huili Gong

Frequent Co-Authors

Myong-In Lee
Myong-In Lee Ulsan National Institute of Science and Technology
John R. Jensen
John R. Jensen University of South Carolina
Jhoon Kim
Jhoon Kim Yonsei University
Kyung Hwa Cho
Kyung Hwa Cho Korea University
Angus Atkinson
Angus Atkinson Plymouth Marine Laboratory
Sophie Fielding
Sophie Fielding British Antarctic Survey
Mark D. Coleman
Mark D. Coleman University of Idaho
Rokjin J. Park
Rokjin J. Park Seoul National University
Youngryel Ryu
Youngryel Ryu Seoul National University
Benjamin Bechtel
Benjamin Bechtel Ruhr University Bochum

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