Atmospheric Remote Sensing

<p>Section 1: Principles of atmospheric remote sensing<br>1. Satellite Remote Sensing Aerosol Optical Depth (AOD) Retrievals: accuracy assessment, limitations, and usage recommendations over Asia <br>2. Earth Observation Satellites (EOS) for global sustainability: Potentials and boundaries <br>3. Remote Sensing and its implication to air pollution studies <br>4. Global Navigation Satellite System and its Application in Remote Sensing of Atmosphere <br>5. Estimation of Ionospheric Total Electron Content (TEC) from GNSS observations <br>6. Impact of water vapor on atmospheric correction of satellite images </p> <p>Section 2: Application of atmospheric remote sensing<br>7. Identification of fires over the Indian region from space <br>8. Identification of different aerosol types observed at a semi-arid location in southern peninsular India retrieved from the CALIPSO <br>9. Remote sensing of clouds: challenges and way forward <br>10. Study of Aerosol – Cloud Interaction over Indian region using satellite remote sensing <br>11. Aerosol loading over the Northern Indian ocean using spaceborne measurement <br>12. Balloon – based remote sensing of Atmosphere <br>13. Vertical distribution of atmospheric brown clouds using LiDAR remote sensing over Indian region <br>14. Application of Remote Sensing to study forest fires over the Himalayan region <br>15. Study of the atmospheric and ionospheric phenomenon using GPS based remote sensing technique <br>16. Low-latitude lower ionosphere remote sensing using very low frequency (VLF) waves <br>17. Remote Sensing of Ionosphere, Plasmasphere, and Magnetosphere by VLF Wave <br>18. Remote Sensing of Ionospheric Irregularities <br>19. Application of atmospheric remote sensing in disaster management <br>20. Characteristics of tropical cyclones through remote sensing observations and techniques <br>21. Significance of Remote sensing in Tropical Cyclone prediction and disaster management: Indian perspective <br>22. Study of dust storm characteristics over Indo – Gangetic Basin through satellite remote sensing <br>23. Geomorphological mapping of glacial and periglacial landforms using Remote Sensing: Examples from semi-arid and sub-humid Himalayan valleys <br>24. Classification of Remotely sensed Formosat-2 Haridwar data using Deep Learning Artificial Neural Network and Convolutional Neural Network <br>25. Remote sensing-based study of landslide hazard zonation in Namchi and its surrounding area of Sikkim, India</p>