Files and links (1)
Zhou_uiowa_0096D_18377-o9.75 MB
Embargoed Access, Embargo ends: 01/26/2026
Dissertation
Lighting the night: application of VIIRS DNB for nighttime wildfire and aerosol monitoring
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Autumn 2023
DOI: 10.25820/etd.006983
Abstract
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).
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.
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.
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.
Details
- Title: Subtitle
- Lighting the night: application of VIIRS DNB for nighttime wildfire and aerosol monitoring
- Creators
- Meng Zhou
- Contributors
- Jun Wang (Advisor)Gregory R. Carmichael (Committee Member)Peter R. Colarco (Committee Member)Joe S. Gomes (Committee Member)Marc A. Linderman (Committee Member)Steven D. Miller (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Informatics
- Date degree season
- Autumn 2023
- Publisher
- University of Iowa
- DOI
- 10.25820/etd.006983
- Number of pages
- xxvi, 261 pages
- Copyright
- Copyright 2023 Meng Zhou
- Grant note
- Financial support from the Iowa Informatics Initiative program fellowship and the Future Investigators in NASA Earth and Space Science and Technology (FINESST) program (grant #: 80NSSC21K1628).
- Comment
This thesis has been optimized for improved web viewing. If you require the original version, contact the University Archives at the University of Iowa: https://www.lib.uiowa.edu/sc/contact/.
- Language
- English
- Date submitted
- 09/29/2023
- Description illustrations
- Illustrations, tables, graphs, charts
- Description bibliographic
- Includes bibliographical references (pages 194-224).
- Public Abstract (ETD)
The advancements in nighttime earth observation, facilitated by the Visible Infrared Imager- Radiometer Suite (VIIRS) Day/Night Band (DNB), have opened new frontiers in environmental monitoring. This thesis delves deep into VIIRS DNB’s nighttime observations, specifically targeting wildfire characterization and the tracking of nocturnal smoke and dust transport.
We first developed the numerical model within the framework of the UNified and Linearized Radiative Transfer Model (UNL-VRTM) to extend its ability to describe the interaction between nighttime light sources, such as moonlight, firelight, and artificial light with the surface and atmosphere. This foundational work paved the way for the Fire Light Detection Algorithm version 2 (FILDA-2). Integrating visible light data from the VIIRS DNB, FILDA-2 not only achieves superior wildfire detection but also provides a deeper understanding of wildfire phases, holding potential for refining fire emission assessments in the future. To track the movement of smoke and dust at night, we introduced three physics-based and machine-learning-based algorithms to retrieve the aerosol optical depth (AOD), a parameter used to describe the total amount of aerosol suspended in the air, utilizing the reflected moonlight observation made by the VIIRS DNB over land and ocean on regional and global scales. Evaluations were made with multiple sources of aerosol measurements from the space and ground, which are commonly treated as the ground truth.
In summary, this thesis highlights the potential of nighttime observations in offering a more comprehensive understanding of Earth’s natural activities.
- Academic Unit
- IDGP in Informatics
- Record Identifier
- 9984546944002771
Metrics
63 Record Views