Section 1: Principles of atmospheric remote sensing
1. Satellite Remote Sensing Aerosol Optical Depth (AOD) Retrievals: accuracy assessment, limitations, and usage recommendations over Asia
2. Earth Observation Satellites (EOS) for global sustainability: Potentials and boundaries
3. Remote Sensing and its implication to air pollution studies
4. Global Navigation Satellite System and its Application in Remote Sensing of Atmosphere
5. Estimation of Ionospheric Total Electron Content (TEC) from GNSS observations
6. Impact of water vapor on atmospheric correction of satellite images

Section 2: Application of atmospheric remote sensing
7. Identification of fires over the Indian region from space
8. Identification of different aerosol types observed at a semi-arid location in southern peninsular India retrieved from the CALIPSO
9. Remote sensing of clouds: challenges and way forward
10. Study of Aerosol - Cloud Interaction over Indian region using satellite remote sensing
11. Aerosol loading over the Northern Indian ocean using spaceborne measurement
12. Balloon - based remote sensing of Atmosphere
13. Vertical distribution of atmospheric brown clouds using LiDAR remote sensing over Indian region
14. Application of Remote Sensing to study forest fires over the Himalayan region
15. Study of the atmospheric and ionospheric phenomenon using GPS based remote sensing technique
16. Low-latitude lower ionosphere remote sensing using very low frequency (VLF) waves
17. Remote Sensing of Ionosphere, Plasmasphere, and Magnetosphere by VLF Wave
18. Remote Sensing of Ionospheric Irregularities
19. Application of atmospheric remote sensing in disaster management
20. Characteristics of tropical cyclones through remote sensing observations and techniques
21. Significance of Remote sensing in Tropical Cyclone prediction and disaster management: Indian perspective
22. Study of dust storm characteristics over Indo - Gangetic Basin through satellite remote sensing
23. Geomorphological mapping of glacial and periglacial landforms using Remote Sensing: Examples from semi-arid and sub-humid Himalayan valleys
24. Classification of Remotely sensed Formosat-2 Haridwar data using Deep Learning Artificial Neural Network and Convolutional Neural Network
25. Remote sensing-based study of landslide hazard zonation in Namchi and its surrounding area of Sikkim, India