IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i5p2090-d1350168.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/5/2090/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/5/2090/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mark G E White & Neil E Bezodis & Jonathon Neville & Huw Summers & Paul Rees, 2022. "Determining jumping performance from a single body-worn accelerometer using machine learning," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-25, February.
    2. Airola, Antti & Pahikkala, Tapio & Waegeman, Willem & De Baets, Bernard & Salakoski, Tapio, 2011. "An experimental comparison of cross-validation techniques for estimating the area under the ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1828-1844, April.
    3. Matthias Schmid & Thomas Hielscher & Thomas Augustin & Olaf Gefeller, 2011. "A Robust Alternative to the Schemper–Henderson Estimator of Prediction Error," Biometrics, The International Biometric Society, vol. 67(2), pages 524-535, June.
    4. Luts, Jan & Ormerod, John T., 2014. "Mean field variational Bayesian inference for support vector machine classification," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 163-176.
    5. David Rios Insua & Roi Naveiro & Victor Gallego, 2020. "Perspectives on Adversarial Classification," Mathematics, MDPI, vol. 8(11), pages 1-21, November.
    6. Dietrichson, Jens & Klokker, Rasmus H., 2024. "Predicting preschool problems," Children and Youth Services Review, Elsevier, vol. 161(C).
    7. John J Nay & Yevgeniy Vorobeychik, 2016. "Predicting Human Cooperation," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-19, May.
    8. Kazim Topuz & Behrooz Davazdahemami & Dursun Delen, 2024. "A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases," Annals of Operations Research, Springer, vol. 341(1), pages 673-697, October.
    9. Matthew Tuson & Berwin Turlach & Kevin Murray & Mei Ruu Kok & Alistair Vickery & David Whyatt, 2021. "Predicting Future Geographic Hotspots of Potentially Preventable Hospitalisations Using All Subset Model Selection and Repeated K-Fold Cross-Validation," IJERPH, MDPI, vol. 18(19), pages 1-21, September.
    10. Lauri Nevasalmi, 2022. "Recession forecasting with high‐dimensional data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 752-764, July.
    11. Usta, Ilhan & Kantar, Yeliz Mert, 2011. "On the performance of the flexible maximum entropy distributions within partially adaptive estimation," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2172-2182, June.
    12. Hosseini, Fatemeh & Eidsvik, Jo & Mohammadzadeh, Mohsen, 2011. "Approximate Bayesian inference in spatial GLMM with skew normal latent variables," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1791-1806, April.
    13. Qianying Jin & Kristiaan Kerstens & Ignace Van de Woestyne, 2024. "Convex and nonconvex nonparametric frontier-based classification methods for anomaly detection," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(4), pages 1213-1239, December.
    14. Gonzalo Perez-de-la-Cruz & Guillermina Eslava-Gomez, 2019. "Discriminant analysis for discrete variables derived from a tree-structured graphical model," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 855-876, December.
    15. I. Charvet & A. Suppasri & H. Kimura & D. Sugawara & F. Imamura, 2015. "A multivariate generalized linear tsunami fragility model for Kesennuma City based on maximum flow depths, velocities and debris impact, with evaluation of predictive accuracy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(3), pages 2073-2099, December.
    16. Shusaku Tsumoto & Tomohirno Kimura & Shoji Hirano, 2021. "Determination of Disease from Discharge Summaries," The Review of Socionetwork Strategies, Springer, vol. 15(1), pages 49-66, June.
    17. Khan, Jafar A. & Van Aelst, Stefan & Zamar, Ruben H., 2010. "Fast robust estimation of prediction error based on resampling," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3121-3130, December.
    18. Chen, Weijie & Yousef, Waleed A. & Gallas, Brandon D. & Hsu, Elizabeth R. & Lababidi, Samir & Tang, Rong & Pennello, Gene A. & Symmans, W. Fraser & Pusztai, Lajos, 2012. "Uncertainty estimation with a finite dataset in the assessment of classification models," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1016-1027.
    19. Mark Lown & Michael Brown & Chloë Brown & Arthur M Yue & Benoy N Shah & Simon J Corbett & George Lewith & Beth Stuart & Michael Moore & Paul Little, 2020. "Machine learning detection of Atrial Fibrillation using wearable technology," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-9, January.
    20. Piccarreta, Raffaella, 2010. "Binary trees for dissimilarity data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1516-1524, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:2090-:d:1350168. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.