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A Broadscale Assessment of Sentinel-2 Imagery and the Google Earth Engine for the Nationwide Mapping of Chlorophyll a

Author

Listed:
  • Richard A. Johansen

    (Environmental Laboratory, US Army Engineer Research and Development Center, 3909 Halls Ferry Rd., Vicksburg, MS 39180, USA)

  • Molly K. Reif

    (Joint Airborne Lidar Bathymetry Technical Center of Expertise, 7225 Stennis Airport Rd., Kiln, MS 39556, USA)

  • Christina L. Saltus

    (Environmental Laboratory, US Army Engineer Research and Development Center, 3909 Halls Ferry Rd., Vicksburg, MS 39180, USA)

  • Kaytee L. Pokrzywinski

    (National Center for Coastal Ocean Science, National Oceanic and Atmospheric Administration, 101 Pivers Island Rd., Beaufort, NC 28516, USA)

Abstract

Harmful algal blooms are a global phenomenon that degrade water quality and can result in adverse health impacts to both humans and wildlife. Monitoring algal blooms at scale is extremely difficult due to the lack of coincident data across space and time. Additionally, traditional field collection methods tend to be labor- and cost-prohibitive, resulting in disparate data collection not capable of capturing the physical and biological variations within waterbodies or regions. This research attempts to help alleviate this issue by leveraging large, public, water quality databases coupled with open-access Google Earth Engine-derived Sentinel-2 imagery to evaluate the practical usability of four common chlorophyll a algorithms as a proxy for detecting and mapping algal blooms nationwide. Chlorophyll a data were aggregated from spatially diverse sites across the continental United States between 2019 and 2022. Data were aggregated via a field method and matched to coincident Sentinel-2 imagery using k-folds cross-validation to evaluate the performance of the band ratio algorithms at the nationwide scale. Additionally, the dataset was portioned to evaluate the influence of temporal windows and annual consistency on algorithm performance. The 2BDA and the NDCI algorithms were the most viable for broadscale mapping of chlorophyll a , which performed moderately well (R 2 > 0.5) across the entire continental united states, encompassing highly diverse spatial, temporal, and physical conditions. Algorithms’ performances were consistent across different field methods, temporal windows, and annually. The most compatible field data acquisition method was the chlorophyll a , water , trichromatic method , uncorrected with R 2 values of 0.63, 0.62, and 0.41 and RMSE values of 15.89, 16.2, and 23.30 for 2BDA, NDCI, and MCI, respectively. These results indicate the feasibility of utilizing band ratio algorithms for broadscale detection and mapping of chlorophyll a as a proxy for HABs, which is especially valuable when coincident data are unavailable or limited.

Suggested Citation

  • Richard A. Johansen & Molly K. Reif & Christina L. Saltus & Kaytee L. Pokrzywinski, 2024. "A Broadscale Assessment of Sentinel-2 Imagery and the Google Earth Engine for the Nationwide Mapping of Chlorophyll a," Sustainability, MDPI, vol. 16(5), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:2090-:d:1350168
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    References listed on IDEAS

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    1. Kim, Ji-Hyun, 2009. "Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3735-3745, September.
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