We introduced the second-generation Fire Light Detection Algorithm (FILDA-2), an algorithm that has been adopted by the National Aeronautics and Space Administration (NASA) MODIS/VIIRS land team as the next-generation modified combustion efficiency (MCE) product for wildfire monitoring. Key improvements incorporated into FILDA-2 include a new fast algorithm for mapping VIIRS DNB radiances, dynamic thresholds for contextual testing of fire pixels, and pixel-specific estimates for various fire characteristics. Benchmark tests indicate the algorithm's enhanced ability to detect and characterize fires compared to previous fire detection algorithms. The MCE derived by FILDA-2 is in good agreement with limited ground-based observations near the fires.

We designed an algorithm designed to retrieve nighttime AOD from VIIRS DNB observations, particularly during the western U.S. fire seasons. The methodology taps into the UNL-VRTM with new developments for enabling the nighttime capability. The retrieved AOD values show good agreement with spatiotemporal collocated Aerosol Robotic NETwork (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD values. Case studies indicated that the nighttime AOD retrieval plays an indispensable role in describing the nonlinear diurnal movement of smoke transport and discerning the source of smoke plumes heretofore observable only in the daytime.

Building up the land retrieval algorithm, we further launched the Nighttime Ocean Aerosol Optical Depth (AOD) Retrieval algorithm (NOARA) for oceanic applications. Innovations in the NOAR include new optical property models, a refined reflectance model for ocean surfaces, and new cloud and dust masking schemes. Validation against the AERONET and CALIOP shows superior performance of the NOARA retrievals. This algorithm fills the observational gap for a comprehensive understanding of the diurnal cycle of dust transport over the ocean and has the potential to provide vital information to better constrain the chemical transport model.

We delved into the connections between TOA reflectance from VIIRS DNB during the day and night. A theoretical framework, backed by simulations, indicated the strong correlation between solar and lunar irradiance within the VIIRS DNB spectrum. This correlation paved the way for a machine learning model — specifically, multilayer perception — to link day and night knowledge via VIIRS DNB observation for rapid global nighttime AOD retrieval. Comparisons between the MLP AOD models for day and night revealed the impressive potential of this approach to extract global AOD during nighttime.

We first developed a nighttime shortwave radiative transfer model within the UNified and Linearized Radiative Transfer Model (UNL-VRTM) framework. We expanded the representation of light sources for the surface and the top of the atmosphere to handle illuminations from the Moon, fires, and artificial lights. We applied this model to address challenges associated with VIIRS's capability to sense aerosol and fire at night. The results revealed the complexities and nuances of AOD retrieval from DNB, such as the effects of various surface illumination sources on AOD bias.

The expansion of nighttime earth observation by the Visible Infrared Imager-Radiometer Suite (VIIRS) Day/Night Band (DNB) has dramatically broadened our comprehension of the Earth system. This thesis is centered around leveraging high-quality nighttime observations from VIIRS DNB for environmental monitoring, primarily emphasizing aspects of wildfire characterization and retrieval of smoke and dust aerosol optical depth (AOD).

In essence, this thesis has highlighted the invaluable nature of nighttime observations, specifically from VIIRS, in extending our comprehension of Earth's natural processes. Through detailed modeling, innovative algorithm development, and rigorous testing, we have begun to uncover the full potential of these observations, setting the stage for future breakthroughs in this domain.