| Support vector machines in remote sensing: A review G Mountrakis, J Im, C Ogole ISPRS journal of photogrammetry and remote sensing 66 (3), 247-259, 2011 | 3545 | 2011 |
| Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data J Rhee, J Im, GJ Carbone Remote Sensing of environment 114 (12), 2875-2887, 2010 | 728 | 2010 |
| Object‐based change detection using correlation image analysis and image segmentation J Im, JR Jensen, JA Tullis International journal of remote sensing 29 (2), 399-423, 2008 | 512 | 2008 |
| Forest biomass estimation from airborne LiDAR data using machine learning approaches CJ Gleason, J Im Remote Sensing of Environment 125, 80-91, 2012 | 381 | 2012 |
| A change detection model based on neighborhood correlation image analysis and decision tree classification J Im, JR Jensen Remote Sensing of Environment 99 (3), 326-340, 2005 | 381 | 2005 |
| Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification Y Ke, LJ Quackenbush, J Im Remote Sensing of Environment 114 (6), 1141-1154, 2010 | 380 | 2010 |
| Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations Y Ke, J Im, J Lee, H Gong, Y Ryu Remote sensing of environment 164, 298-313, 2015 | 300 | 2015 |
| Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions S Park, J Im, E Jang, J Rhee Agricultural and forest meteorology 216, 157-169, 2016 | 298 | 2016 |
| Meteorological drought forecasting for ungauged areas based on machine learning: Using long-range climate forecast and remote sensing data J Rhee, J Im Agricultural and Forest Meteorology 237, 105-122, 2017 | 206 | 2017 |
| Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images C Yoo, D Han, J Im, B Bechtel ISPRS Journal of Photogrammetry and Remote Sensing 157, 155-170, 2019 | 193 | 2019 |
| Machine learning approaches to coastal water quality monitoring using GOCI satellite data YH Kim, J Im, HK Ha, JK Choi, S Ha GIScience & Remote Sensing 51 (2), 158-174, 2014 | 190 | 2014 |
| Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches J Im, S Park, J Rhee, J Baik, M Choi Environmental Earth Sciences 75, 1-19, 2016 | 184 | 2016 |
| Downscaling of MODIS one kilometer evapotranspiration using Landsat-8 data and machine learning approaches Y Ke, J Im, S Park, H Gong Remote Sensing 8 (3), 215, 2016 | 152 | 2016 |
| Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest M Li, J Im, C Beier GIScience & Remote Sensing 50 (4), 361-384, 2013 | 150 | 2013 |
| Comparative assessment of various machine learning‐based bias correction methods for numerical weather prediction model forecasts of extreme air temperatures in urban areas D Cho, C Yoo, J Im, DH Cha Earth and Space Science 7 (4), e2019EA000740, 2020 | 147 | 2020 |
| Classification and mapping of paddy rice by combining Landsat and SAR time series data S Park, J Im, S Park, C Yoo, H Han, J Rhee Remote Sensing 10 (3), 447, 2018 | 146 | 2018 |
| Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data C Yoo, J Im, S Park, LJ Quackenbush ISPRS journal of photogrammetry and remote sensing 137, 149-162, 2018 | 143 | 2018 |
| Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula S Park, J Im, S Park, J Rhee Agricultural and Forest Meteorology 237, 257-269, 2017 | 140 | 2017 |
| Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate N Bhattarai, SB Shaw, LJ Quackenbush, J Im, R Niraula International Journal of Applied Earth Observation and Geoinformation 49, 75-86, 2016 | 139 | 2016 |
| A review of remote sensing of forest biomass and biofuel: options for small-area applications CJ Gleason, J Im GIScience & Remote Sensing 48 (2), 141-170, 2011 | 125 | 2011 |
