Version 1
: Received: 13 August 2024 / Approved: 14 August 2024 / Online: 14 August 2024 (08:21:08 CEST)
How to cite:
Mohammady, S.; Erratt, K. J.; Creed, I. F. Tracking Phytoplankton Biomass amid Wildfire Interference Using Landsat 8 OLI. Preprints2024, 2024081010. https://doi.org/10.20944/preprints202408.1010.v1
Mohammady, S.; Erratt, K. J.; Creed, I. F. Tracking Phytoplankton Biomass amid Wildfire Interference Using Landsat 8 OLI. Preprints 2024, 2024081010. https://doi.org/10.20944/preprints202408.1010.v1
Mohammady, S.; Erratt, K. J.; Creed, I. F. Tracking Phytoplankton Biomass amid Wildfire Interference Using Landsat 8 OLI. Preprints2024, 2024081010. https://doi.org/10.20944/preprints202408.1010.v1
APA Style
Mohammady, S., Erratt, K. J., & Creed, I. F. (2024). Tracking Phytoplankton Biomass amid Wildfire Interference Using Landsat 8 OLI. Preprints. https://doi.org/10.20944/preprints202408.1010.v1
Chicago/Turabian Style
Mohammady, S., Kevin J. Erratt and Irena F. Creed. 2024 "Tracking Phytoplankton Biomass amid Wildfire Interference Using Landsat 8 OLI" Preprints. https://doi.org/10.20944/preprints202408.1010.v1
Abstract
The increasing frequency and severity of phytoplankton blooms worldwide highlight the need for advancements in monitoring technologies. Optical remote sensors, such as Landsat, have proven to be a cost-effective method for large-scale, near-real-time assessments of phytoplankton biomass in lakes. However, its effectiveness is often compromised by atmospheric interferences like clouds, dust, and wildfire smoke, which can obscure the clear-sky conditions essential for accurate remote sensing. While partial atmospheric correction (removing only the Rayleigh effect) is commonly applied to address these interferences, it remains inadequate for mitigating the impact of wildfire smoke. This study investigates the potential of Landsat's coastal/aerosol band (B1) for assessing wildfire smoke interference effects on Chlorophyll-a (Chl-a) retrieval models, which serve as proxies for phytoplankton biomass. We employed cluster analysis of B1 values to create a screening system based on aerosol reflectance, categorizing smoke interference into low, moderate, and high levels. Subsequently, we applied both partial (Rayleigh-corrected reflectance) and full (Landsat 8 Level 2 surface reflectance) atmospheric corrections before developing the Chl-a retrieval models. Excluding high wildfire smoke interference (B1 > 0.07) from the Chl-a calibration dataset significantly enhanced model performance, increasing the r-squared value from approximately 0.55 to 0.80. Moderate smoke interference (0.05 < B1 < 0.07) yielded results comparable to low-interference conditions. Notably, Rayleigh-corrected reflectance, even without additional aerosol band filtering, achieved higher r-squared values for the Chl-a retrieval model than full atmospheric correction. B1 thus proves to be a valuable tool for identifying low smoke-impacted observations, offering an effective method to monitor phytoplankton biomass amid increasing wildfire activity and improving the capacity to monitor aquatic environments in a changing global landscape.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
