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

Agricultural and forest meteorology, 2017-05, Vol.237-238, p.105-122

2017

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Earth observations and geographic information science for sustainable development goals

GIScience and remote sensing, 2020-07, Vol.57 (5), p.591-592

Im, Jungho

2020

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

ISPRS journal of photogrammetry and remote sensing, 2019-11, Vol.157, p.155-170

2019

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Support vector machines in remote sensing: A review

ISPRS journal of photogrammetry and remote sensing, 2011-05, Vol.66 (3), p.247-259

2011

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

Agricultural and forest meteorology, 2016-01, Vol.216 (15), p.157-169

2016

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Forest biomass estimation from airborne LiDAR data using machine learning approaches

Remote sensing of environment, 2012-10, Vol.125, p.80-91

2012

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

Remote sensing of environment, 2010-12, Vol.114 (12), p.2875-2887

2010

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Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia

ISPRS journal of photogrammetry and remote sensing, 2022-01, Vol.183, p.253-268

2022

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Mitigating underestimation of fire emissions from the Advanced Himawari Imager: A machine learning and multi-satellite ensemble approach

International journal of applied earth observation and geoinformation, 2024-04, Vol.128, p.103784, Article 103784

2024

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

Remote sensing (Basel, Switzerland), 2016-03, Vol.8 (3), p.215

2016

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A deep learning model using geostationary satellite data for forest fire detection with reduced detection latency

GIScience and remote sensing, 2022-12, Vol.59 (1), p.2019-2035

2022

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A new optimized algorithm for automating endmember pixel selection in the SEBAL and METRIC models

Remote sensing of environment, 2017-07, Vol.196, p.178-192

2017

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Quantitative assessment of the scale conversion from instantaneous to daily GPP under various sky conditions based on MODIS local overpassing time

GIScience and remote sensing, 2024-12, Vol.61 (1)

2024

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

Remote sensing (Basel, Switzerland), 2018-03, Vol.10 (3), p.447

2018

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Building type classification using spatial and landscape attributes derived from LiDAR remote sensing data

Landscape and urban planning, 2014-10, Vol.130, p.134-148

2014

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Improved Ocean-fog Monitoring Using Himawari-8 Geostationary Satellite Data Based on Machine Learning with SHAP-based Model Interpretation

IEEE journal of selected topics in applied earth observations and remote sensing, 2023-01, Vol.16, p.1-24

2023

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

Earth and space science (Hoboken, N.J.), 2020-04, Vol.7 (4), p.n/a

2020

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Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data

Remote sensing (Basel, Switzerland), 2020-01, Vol.12 (1), p.108

2020

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High-Resolution Seamless Daily Sea Surface Temperature Based on Satellite Data Fusion and Machine Learning over Kuroshio Extension

Remote sensing (Basel, Switzerland), 2022-02, Vol.14 (3), p.575

2022

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Improving Local Climate Zone Classification Using Incomplete Building Data and Sentinel 2 Images Based on Convolutional Neural Networks

Remote sensing (Basel, Switzerland), 2020-11, Vol.12 (21), p.3552

2020

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Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

Remote sensing (Basel, Switzerland), 2019-11, Vol.11 (22), p.2612

2019

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Improving Short-Term Prediction of Ocean Fog Using Numerical Weather Forecasts and Geostationary Satellite-Derived Ocean Fog Data Based on AutoML

Remote sensing (Basel, Switzerland), 2024-07, Vol.16 (13), p.2348

2024

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Multi-Platform LiDAR for Non-Destructive Individual Aboveground Biomass Estimation for Changbai Larch (Larix olgensis Henry) Using a Hierarchical Bayesian Approach

Remote sensing (Basel, Switzerland), 2022-09, Vol.14 (17), p.4361

2022

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Short-Term Forecasting of Satellite-Based Drought Indices Using Their Temporal Patterns and Numerical Model Output

Remote sensing (Basel, Switzerland), 2020-11, Vol.12 (21), p.3499

2020

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Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble technique

GIScience and remote sensing, 2020-07, Vol.57 (5), p.633-649

2020

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

Earth observations and geographic information science for sustainable development goals

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

Support vector machines in remote sensing: A review

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

Forest biomass estimation from airborne LiDAR data using machine learning approaches

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

Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia

Mitigating underestimation of fire emissions from the Advanced Himawari Imager: A machine learning and multi-satellite ensemble approach

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

A deep learning model using geostationary satellite data for forest fire detection with reduced detection latency

A new optimized algorithm for automating endmember pixel selection in the SEBAL and METRIC models

Quantitative assessment of the scale conversion from instantaneous to daily GPP under various sky conditions based on MODIS local overpassing time

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

Building type classification using spatial and landscape attributes derived from LiDAR remote sensing data

Improved Ocean-fog Monitoring Using Himawari-8 Geostationary Satellite Data Based on Machine Learning with SHAP-based Model Interpretation

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

Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data

High-Resolution Seamless Daily Sea Surface Temperature Based on Satellite Data Fusion and Machine Learning over Kuroshio Extension

Improving Local Climate Zone Classification Using Incomplete Building Data and Sentinel 2 Images Based on Convolutional Neural Networks

Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

Improving Short-Term Prediction of Ocean Fog Using Numerical Weather Forecasts and Geostationary Satellite-Derived Ocean Fog Data Based on AutoML

Multi-Platform LiDAR for Non-Destructive Individual Aboveground Biomass Estimation for Changbai Larch (Larix olgensis Henry) Using a Hierarchical Bayesian Approach

Short-Term Forecasting of Satellite-Based Drought Indices Using Their Temporal Patterns and Numerical Model Output

Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble technique

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