Nombre de documents archivés : 109.
    Roy, Kaushik; Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356 et Zhang, Ying
  
(2025).
Self-Supervised Deep Learning for Urban Land Cover Classification from Very High Resolution Imagery  / Apprentissage profond auto-supervisé pour la classification de la couverture terrestre urbaine à partir d’images à très haute résolution spatiale.
    Canadian Journal of Remote Sensing
	  , vol. 51
	  , nº 1.
	
     p. 253528.
     DOI: 10.1080/07038992.2025.2532528.
ORCID: https://orcid.org/0000-0002-0214-5356 et Zhang, Ying
  
(2025).
Self-Supervised Deep Learning for Urban Land Cover Classification from Very High Resolution Imagery  / Apprentissage profond auto-supervisé pour la classification de la couverture terrestre urbaine à partir d’images à très haute résolution spatiale.
    Canadian Journal of Remote Sensing
	  , vol. 51
	  , nº 1.
	
     p. 253528.
     DOI: 10.1080/07038992.2025.2532528. 
  
  
    Rahimpour, Morteza; Rahimzadegan, Majid; Nosratpour, Reza; Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356 et Behrangi, Ali
  
(2025).
A Novel Machine Learning-Based Clustering-Merging Method for Improving Extreme Precipitation Estimation.
    Advances in Atmospheric Sciences
	  , vol. 42
	  , nº 8.
	
     pp. 1693-1714.
     DOI: 10.1007/s00376-024-4315-3.
ORCID: https://orcid.org/0000-0002-0214-5356 et Behrangi, Ali
  
(2025).
A Novel Machine Learning-Based Clustering-Merging Method for Improving Extreme Precipitation Estimation.
    Advances in Atmospheric Sciences
	  , vol. 42
	  , nº 8.
	
     pp. 1693-1714.
     DOI: 10.1007/s00376-024-4315-3. 
  
  
    Cherif, Mouna; Saidi, Salwa; Ezzine, Ahmed; Darragi, Fadila et Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356
  
(2025).
Assessing hydrological erosion estimation using the Revised Universal Soil Loss Equation (RUSLE) model in Google Earth Engine: a case study of Medjerda River Catchment, Tunisia.
    Euro-Mediterranean Journal for Environmental Integration
	  , vol. 10
	  , nº 4.
	
     pp. 2453-2471.
     DOI: 10.1007/s41207-025-00767-5.
ORCID: https://orcid.org/0000-0002-0214-5356
  
(2025).
Assessing hydrological erosion estimation using the Revised Universal Soil Loss Equation (RUSLE) model in Google Earth Engine: a case study of Medjerda River Catchment, Tunisia.
    Euro-Mediterranean Journal for Environmental Integration
	  , vol. 10
	  , nº 4.
	
     pp. 2453-2471.
     DOI: 10.1007/s41207-025-00767-5. 
  
  
    Bahrami, Hazhir  ORCID: https://orcid.org/0000-0003-1371-7337; Chokmani, Karem
ORCID: https://orcid.org/0000-0003-1371-7337; Chokmani, Karem  ORCID: https://orcid.org/0000-0003-0018-0761; Homayouni, Saeid
ORCID: https://orcid.org/0000-0003-0018-0761; Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356; Adamchuk, Viacheslav I.
ORCID: https://orcid.org/0000-0002-0214-5356; Adamchuk, Viacheslav I.  ORCID: https://orcid.org/0000-0001-7279-3597; Saifuzzaman, Md; Albasha, Rami
ORCID: https://orcid.org/0000-0001-7279-3597; Saifuzzaman, Md; Albasha, Rami  ORCID: https://orcid.org/0000-0001-5709-3760 et Leduc, Maxime
ORCID: https://orcid.org/0000-0001-5709-3760 et Leduc, Maxime  ORCID: https://orcid.org/0000-0001-7097-1636
  
(2025).
Alfalfa stem count estimation using remote sensing imagery and machine learning on Google Earth Engine.
    International Journal of Applied Earth Observation and Geoinformation
	  , vol. 142
	  .
	
     p. 104729.
     DOI: 10.1016/j.jag.2025.104729.
ORCID: https://orcid.org/0000-0001-7097-1636
  
(2025).
Alfalfa stem count estimation using remote sensing imagery and machine learning on Google Earth Engine.
    International Journal of Applied Earth Observation and Geoinformation
	  , vol. 142
	  .
	
     p. 104729.
     DOI: 10.1016/j.jag.2025.104729. 
  
  
    Farhangi, Mahdi  ORCID: https://orcid.org/0009-0001-5545-8604; Milan, Asghar
ORCID: https://orcid.org/0009-0001-5545-8604; Milan, Asghar  ORCID: https://orcid.org/0000-0003-0187-6074; Shokri, Danesh et Homayouni, Saeid
ORCID: https://orcid.org/0000-0003-0187-6074; Shokri, Danesh et Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356
  
(2025).
Enhancing U-Net performance for high-resolution land cover classification using a dynamic epoch-centric optimizer (DECO).
    Remote Sensing Applications: Society and Environment
	  , vol. 39
	  .
	
     p. 101668.
     DOI: 10.1016/j.rsase.2025.101668.
ORCID: https://orcid.org/0000-0002-0214-5356
  
(2025).
Enhancing U-Net performance for high-resolution land cover classification using a dynamic epoch-centric optimizer (DECO).
    Remote Sensing Applications: Society and Environment
	  , vol. 39
	  .
	
     p. 101668.
     DOI: 10.1016/j.rsase.2025.101668. 
  
  
    Nakhostin Rouhi, Mahsa; Saberioon, Mohammadmehdi; Makki, Mohsen; Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356; Kiavarz, Majid et Alavipanah, Seyed Kazem
  
(2025).
Unlocking urban soil secrets: machine learning and spectrometry in Berlin’s heavy metal pollution study considering spatial data.
    Environmental Monitoring and Assessment
	  , vol. 197
	  , nº 8.
	
     p. 930.
     DOI: 10.1007/s10661-025-14378-8.
ORCID: https://orcid.org/0000-0002-0214-5356; Kiavarz, Majid et Alavipanah, Seyed Kazem
  
(2025).
Unlocking urban soil secrets: machine learning and spectrometry in Berlin’s heavy metal pollution study considering spatial data.
    Environmental Monitoring and Assessment
	  , vol. 197
	  , nº 8.
	
     p. 930.
     DOI: 10.1007/s10661-025-14378-8. 
  
  
    Mukherjee, Sukanya; Dey, Subhadip; Kamilya, Dibyendu; Mondal, Sandipan; Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356 et Mahdianpari, Masoud
  
(2025).
Joint use of the Sentinel-1–derived Kennaugh parameters and Sentinel-2 data for temporal landcover dynamics over the Indian Sundarbans region.
    Journal of Applied Remote Sensing
	  , vol. 19
	  , nº 3.
	
     034504.
     DOI: 10.1117/1.JRS.19.034504.
ORCID: https://orcid.org/0000-0002-0214-5356 et Mahdianpari, Masoud
  
(2025).
Joint use of the Sentinel-1–derived Kennaugh parameters and Sentinel-2 data for temporal landcover dynamics over the Indian Sundarbans region.
    Journal of Applied Remote Sensing
	  , vol. 19
	  , nº 3.
	
     034504.
     DOI: 10.1117/1.JRS.19.034504. 
  
  
    Awad, Mohamad M.  ORCID: https://orcid.org/0000-0002-8419-5786 et Homayouni, Saeid
ORCID: https://orcid.org/0000-0002-8419-5786 et Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356
  
(2025).
High-Resolution Daily XCH4 Prediction Using New Convolutional Neural Network Autoencoder Model and Remote Sensing Data.
    Atmosphere
	  , vol. 16
	  , nº 7.
	
     p. 806.
     DOI: 10.3390/atmos16070806.
ORCID: https://orcid.org/0000-0002-0214-5356
  
(2025).
High-Resolution Daily XCH4 Prediction Using New Convolutional Neural Network Autoencoder Model and Remote Sensing Data.
    Atmosphere
	  , vol. 16
	  , nº 7.
	
     p. 806.
     DOI: 10.3390/atmos16070806. 
  
  
    Kanani-Sadat, Yousef; Safari, Abdolreza; Nasseri, Mohsen et Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356
  
(2025).
A novel explainable stacking ensemble model for estimating design floods: A data-driven approach for ungauged regions.
    Advanced Engineering Informatics
	  , vol. 66
	  .
	
     p. 103429.
     DOI: 10.1016/j.aei.2025.103429.
ORCID: https://orcid.org/0000-0002-0214-5356
  
(2025).
A novel explainable stacking ensemble model for estimating design floods: A data-driven approach for ungauged regions.
    Advanced Engineering Informatics
	  , vol. 66
	  .
	
     p. 103429.
     DOI: 10.1016/j.aei.2025.103429. 
  
  
    Chaabani, Chayma; Salvó, Constanza Sofía; Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356; Chokmani, Karem
ORCID: https://orcid.org/0000-0002-0214-5356; Chokmani, Karem  ORCID: https://orcid.org/0000-0003-0018-0761 et Adebanjo, Hannah M.
  
(2025).
Cloud computing-based freshwater ice mapping using synthetic aperture radar imagery.
    Earth Science Informatics
	  , vol. 18
	  , nº 2.
	
     p. 411.
     DOI: 10.1007/s12145-025-01892-z.
ORCID: https://orcid.org/0000-0003-0018-0761 et Adebanjo, Hannah M.
  
(2025).
Cloud computing-based freshwater ice mapping using synthetic aperture radar imagery.
    Earth Science Informatics
	  , vol. 18
	  , nº 2.
	
     p. 411.
     DOI: 10.1007/s12145-025-01892-z. 
  
  
    Bahrami, Hazhir  ORCID: https://orcid.org/0000-0003-1371-7337; Chokmani, Karem
ORCID: https://orcid.org/0000-0003-1371-7337; Chokmani, Karem  ORCID: https://orcid.org/0000-0003-0018-0761; Homayouni, Saeid
ORCID: https://orcid.org/0000-0003-0018-0761; Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356; Adamchuk, Viacheslav I.
ORCID: https://orcid.org/0000-0002-0214-5356; Adamchuk, Viacheslav I.  ORCID: https://orcid.org/0000-0001-7279-3597; Albasha, Rami
ORCID: https://orcid.org/0000-0001-7279-3597; Albasha, Rami  ORCID: https://orcid.org/0000-0001-5709-3760; Saifuzzaman, Md
ORCID: https://orcid.org/0000-0001-5709-3760; Saifuzzaman, Md  ORCID: https://orcid.org/0000-0001-8494-8971 et Leduc, Maxime
ORCID: https://orcid.org/0000-0001-8494-8971 et Leduc, Maxime  ORCID: https://orcid.org/0000-0001-7097-1636
  
(2025).
Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery.
    Remote Sensing
	  , vol. 17
	  , nº 10.
	
     p. 1759.
     DOI: 10.3390/rs17101759.
ORCID: https://orcid.org/0000-0001-7097-1636
  
(2025).
Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery.
    Remote Sensing
	  , vol. 17
	  , nº 10.
	
     p. 1759.
     DOI: 10.3390/rs17101759. 
  
  
    Khosravi, Younes; Ouarda, Taha B.M.J.  ORCID: https://orcid.org/0000-0002-0969-063X et Homayouni, Saeid
ORCID: https://orcid.org/0000-0002-0969-063X et Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356
  
(2025).
Developing an ensemble machine learning framework for enhanced climate projections using CMIP6 data in the Middle East.
    npj Climate and Atmospheric Science
	  , vol. 8
	  , nº 1.
	
     p. 174.
     DOI: 10.1038/s41612-025-01033-9.
ORCID: https://orcid.org/0000-0002-0214-5356
  
(2025).
Developing an ensemble machine learning framework for enhanced climate projections using CMIP6 data in the Middle East.
    npj Climate and Atmospheric Science
	  , vol. 8
	  , nº 1.
	
     p. 174.
     DOI: 10.1038/s41612-025-01033-9. 
  
  
    Bahrami, Hazhir  ORCID: https://orcid.org/0000-0003-1371-7337; Chokmani, Karem
ORCID: https://orcid.org/0000-0003-1371-7337; Chokmani, Karem  ORCID: https://orcid.org/0000-0003-0018-0761; Homayouni, Saeid
ORCID: https://orcid.org/0000-0003-0018-0761; Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356; Adamchuk, Viacheslav I.; Saifuzzaman, Md et Leduc, Maxime
  
(2025).
Alfalfa detection and stem count from proximal images using a combination of deep neural networks and machine learning.
    Computers and Electronics in Agriculture
	  , vol. 232
	  .
	
     p. 110115.
     DOI: 10.1016/j.compag.2025.110115.
ORCID: https://orcid.org/0000-0002-0214-5356; Adamchuk, Viacheslav I.; Saifuzzaman, Md et Leduc, Maxime
  
(2025).
Alfalfa detection and stem count from proximal images using a combination of deep neural networks and machine learning.
    Computers and Electronics in Agriculture
	  , vol. 232
	  .
	
     p. 110115.
     DOI: 10.1016/j.compag.2025.110115. 
  
  
    Ansari, Mohsen; Knudby, Anders et Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356
  
(2025).
River salinity mapping through machine learning and statistical modeling using Landsat 8 OLI imagery.
    Advances in Space Research
	  , vol. 75
	  , nº 10.
	
     pp. 6981-7002.
     DOI: 10.1016/j.asr.2025.03.037.
ORCID: https://orcid.org/0000-0002-0214-5356
  
(2025).
River salinity mapping through machine learning and statistical modeling using Landsat 8 OLI imagery.
    Advances in Space Research
	  , vol. 75
	  , nº 10.
	
     pp. 6981-7002.
     DOI: 10.1016/j.asr.2025.03.037. 
  
  
    Hamed Alizadeh Moghaddam, Sayyed  ORCID: https://orcid.org/0000-0003-2992-4277; Gazor, Saeed
ORCID: https://orcid.org/0000-0003-2992-4277; Gazor, Saeed  ORCID: https://orcid.org/0000-0003-4368-6682; Homayouni, Saeid
ORCID: https://orcid.org/0000-0003-4368-6682; Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356 et Karami, Fahime
  
(2025).
Integrating Local and Global Features in a Convolutional Vision Transformer for Wetland Mapping.
    IEEE Sensors Journal
	  , vol. 25
	  , nº 5.
	
     pp. 8674-8683.
     DOI: 10.1109/JSEN.2025.3529762.
ORCID: https://orcid.org/0000-0002-0214-5356 et Karami, Fahime
  
(2025).
Integrating Local and Global Features in a Convolutional Vision Transformer for Wetland Mapping.
    IEEE Sensors Journal
	  , vol. 25
	  , nº 5.
	
     pp. 8674-8683.
     DOI: 10.1109/JSEN.2025.3529762. 
  
  
    Fortin Faubert, Maxime  ORCID: https://orcid.org/0000-0001-5816-0523; Pastore, Émilie; Lhissou, Rachid; Roy, Kaushik; Rasooli Zadeh, Aliyeh; Homayouni, Saeid
ORCID: https://orcid.org/0000-0001-5816-0523; Pastore, Émilie; Lhissou, Rachid; Roy, Kaushik; Rasooli Zadeh, Aliyeh; Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356; Chokmani, Karem
ORCID: https://orcid.org/0000-0002-0214-5356; Chokmani, Karem  ORCID: https://orcid.org/0000-0003-0018-0761; Ola, Anne
ORCID: https://orcid.org/0000-0003-0018-0761; Ola, Anne  ORCID: https://orcid.org/0000-0001-7271-3630 et Hénault-Ethier, Louise
ORCID: https://orcid.org/0000-0001-7271-3630 et Hénault-Ethier, Louise  ORCID: https://orcid.org/0000-0003-0849-3666
  
(2025).
Carbonature : modèle d’estimation des stocks de carbone à l’échelle des municipalités du Québec.
    
    
      Rapport de recherche
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    INRS Centre Eau Terre Environnement, Québec.
ORCID: https://orcid.org/0000-0003-0849-3666
  
(2025).
Carbonature : modèle d’estimation des stocks de carbone à l’échelle des municipalités du Québec.
    
    
      Rapport de recherche
       (R2269).
    
    INRS Centre Eau Terre Environnement, Québec.
    
  
  
    Patridge, Pasha April; Quintal-Marineau, Magalie  ORCID: https://orcid.org/0000-0003-2763-8955; Homayouni, Saeid
ORCID: https://orcid.org/0000-0003-2763-8955; Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356; Poulin, Jimmy; Risi, Lydia
ORCID: https://orcid.org/0000-0002-0214-5356; Poulin, Jimmy; Risi, Lydia  ORCID: https://orcid.org/0000-0003-3710-7955; Marmette, Marie-Christine; Bernier, Monique
ORCID: https://orcid.org/0000-0003-3710-7955; Marmette, Marie-Christine; Bernier, Monique  ORCID: https://orcid.org/0000-0002-7812-4965 et Gauthier, Yves
  
(2025).
Camera's at Marine Infrastructures in Nunavik (Caiman) / Caméras aux infrastructures Maritimes du Nunavik
    [Vidéo]
ORCID: https://orcid.org/0000-0002-7812-4965 et Gauthier, Yves
  
(2025).
Camera's at Marine Infrastructures in Nunavik (Caiman) / Caméras aux infrastructures Maritimes du Nunavik
    [Vidéo]
  
  
    Nouraki, Atefeh; Golabi, Mona; Albaji, Mohammad; Naseri, Abd Ali et Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356
  
(2025).
Estimation of surface soil moisture using the Thermal-Optical TRApezoid Model with Landsat-8 data.
    Journal of Water and Irrigation Management
	  , vol. 14
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     pp. 827-844.
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ORCID: https://orcid.org/0000-0002-0214-5356
  
(2025).
Estimation of surface soil moisture using the Thermal-Optical TRApezoid Model with Landsat-8 data.
    Journal of Water and Irrigation Management
	  , vol. 14
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     pp. 827-844.
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    Alizadeh Moghaddam, Sayyed Hamed  ORCID: https://orcid.org/0000-0003-2992-4277; Gazor, Saeed
ORCID: https://orcid.org/0000-0003-2992-4277; Gazor, Saeed  ORCID: https://orcid.org/0000-0003-4368-6682; Homayouni, Saeid
ORCID: https://orcid.org/0000-0003-4368-6682; Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356 et Karami, Fahime
ORCID: https://orcid.org/0000-0002-0214-5356 et Karami, Fahime  ORCID: https://orcid.org/0000-0003-1051-9351
  
(2025).
Multiscale Deformable DenseNet for Wetland Mapping Using Hyperspectral Images.
    IEEE Geoscience and Remote Sensing Letters
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(2025).
Multiscale Deformable DenseNet for Wetland Mapping Using Hyperspectral Images.
    IEEE Geoscience and Remote Sensing Letters
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    Shokri, Danesh  ORCID: https://orcid.org/0000-0003-3555-7645; Larouche, Christian
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ORCID: https://orcid.org/0000-0002-6288-4169 et Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356
  
(2025).
Real-Time Moving Vehicle Counting and Speed Estimation Toward Efficient Traffic Surveillance.
    IEEE Access
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     pp. 36687-36700.
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(2025).
Real-Time Moving Vehicle Counting and Speed Estimation Toward Efficient Traffic Surveillance.
    IEEE Access
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    Farhadiani, Ramin; Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356; Bhattacharya, Avik et Mahdianpari, Masoud
  
(2024).
Crop Classification Using Multi-Temporal RADARSAT Constellation Mission Compact Polarimetry SAR Data: Classification des cultures à l’aide de données multitemporelles radar à synthèse d’ouverture en mode polarimétrie compacte de la Mission de la Constellation RADARSAT.
    Canadian Journal of Remote Sensing
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(2024).
Crop Classification Using Multi-Temporal RADARSAT Constellation Mission Compact Polarimetry SAR Data: Classification des cultures à l’aide de données multitemporelles radar à synthèse d’ouverture en mode polarimétrie compacte de la Mission de la Constellation RADARSAT.
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    Shahabi, Hejar; Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356; Perret, Didier et Giroux, Bernard
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(2024).
Mapping Complex Landslide Scars Using Deep Learning and High-Resolution Topographic Derivatives from LiDAR Data in Quebec, Canada.
    Canadian Journal of Remote Sensing
	  , vol. 50
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     p. 2418087.
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(2024).
Mapping Complex Landslide Scars Using Deep Learning and High-Resolution Topographic Derivatives from LiDAR Data in Quebec, Canada.
    Canadian Journal of Remote Sensing
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    Khosravi, Younes; Homayouni, Saeid  ORCID: https://orcid.org/0000-0002-0214-5356 et Ouarda, Taha B. M. J.
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(2024).
Spatio-temporal evaluation of MODIS temperature vegetation dryness index in the Middle East.
    Ecological Informatics
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     p. 102894.
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(2024).
Spatio-temporal evaluation of MODIS temperature vegetation dryness index in the Middle East.
    Ecological Informatics
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     p. 102894.
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(2024).
Enhanced root zone soil moisture monitoring using multitemporal remote sensing data and machine learning techniques.
    Remote Sensing Applications: Society and Environment
	  , vol. 36
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