Evaluation of hyperspectral bio-optical indices for chlorophyll-a mapping in coastal waters of Sechura bay, Peru
DOI:
https://doi.org/10.53554/boletin.v41i2.470Keywords:
Remote sensing reflectance, Chlorophyll-a, bio-optics, linear discriminant analysis, TriOS RamsesAbstract
This study evaluates the potential of hyperspectral bio-optical indices derived from remote-sensing reflectance, Rrs(λ), for classifying chlorophyll-a (Chl-a) concentrations in the optically complex coastal waters of Sechura Bay, Peru. Reflectance measurements were acquired using a TriOS Ramses radiometer during field surveys conducted in April and August 2024 and May 2025. A total of 2,332 hyperspectral reflectance signatures were categorized into three Chl-a concentration classes: low (<2.35 mg/m³), moderate (2.35–7.36 mg/m³), and high (>7.36 mg/m³). Class thresholds were established using the 33rd and 67th percentiles of the observed concentration range (0.36–13.84 mg/m³). Six bio-optical indices based on selected spectral bands (443, 490, 555, 665, and 705 nm) were computed and assessed through Kruskal–Wallis tests and Spearman rank correlation analyses. Among the evaluated metrics, GI exhibited the strongest association with Chl-a concentration (ρ = 0.701, p < 0.001), surpassing the performance of red-edge indices commonly employed in turbid coastal environments. The classification capability of the indices was further examined using three supervised learning approaches: Linear Discriminant Analysis (LDA), Random Forest (RF), and Partial Least Squares Discriminant Analysis (PLS-DA). Model performance was assessed using leave-one-out cross-validation (LOOCV). LDA yielded the highest overall classification accuracy (63.2 %, κ = 0.446) outperforming RF (60.5 %, κ = 0.407) and PLS-DA (60.5 %, κ = 0.404). Across all models, the moderate Chl-a class exhibited substantially lower sensitivity (25.0–41.7%) than the low- and high-concentration classes, indicating considerable spectral overlap within intermediate pigment regimes. The results demonstrate that hyperspectral measurements of can effectively support the classification of Chl-a concentration ranges in coastal waters characterized by complex optical properties. These findings also highlight the importance of concurrent measurements of colored dissolved organic matter and suspended sediments to improve classification performance, particularly for intermediate chlorophyll-a concentrations.
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Ade, C., Hestir, E. L., Avouris, D. M., Burmistrova, J., Nickles, C., Lopez, A. M., Lopez Barreto, B., Vellanoweth, J., Smalldon, R. & Lee, C. M. (2023). SHIFT: Ramses Trios Radiometer Above Water Measurements, Santa Barbara Sea, CA (Version 1). ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/2234
Binding, C. E., Greenberg, T. A. & Bukata, R. P. (2013). The MERIS Maximum Chlorophyll Index; its merits and limitations for inland water algal bloom monitoring. Journal of Great Lakes Research, 39, 100– 107. https://doi.org/10.1016/j.jglr.2013.04.005
Breiman, L. (2001). Random Forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
Carlson, R. E. (1977). A trophic state index for lakes. Limnology and Oceanography, 22(2), 361-369. https://doi.org/10.4319/lo.1977.22.2.0361
Cesar, G. M., Rudorff, N. M., Oliveira, A. L., Valerio, A. M., Chuqui, M. G., Pompeu, M., Gaeta, S. A., Frouin, S. A. & Kampel, M. (2023). Bio-optical properties of South Brazil Bight coastal waters and implications for satellite chlorophyll-a concentration retrieval. International Journal of Remote Sensing, 44(7), 2428–2457. https://doi.org/10.1080/01431161.2023.2201385
Dall’Olmo, G. & Gitelson, A. A. (2005). Effect of biooptical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters. experimental results. Applied Optics, 44(3), 412-422. https://doi.org/10.1364/AO.44.000412
Da Silva, E. F., Novo, E. M., Lobo, F. L., Barbosa, C. C., Noernberg, M., Rotta, L. H., Cairo, C., Maciel, D. & Júnior, R. (2021). Optical water types found in Brazilian waters. Limnology, 22, 57–68. https://doi.org/10.1007/s10201-020-00633-z
Espinoza Villar, R., Martinez, J.-M., Le Texier, M., Guyot, J.-L., Fraizy, P., Meneses, P. R. & de Oliveira, E. (2013). A study of sediment transport in the Madeira River, Brazil, using MODIS remote-sensing images. Journal of South American Earth Sciences, 44, 45–54. https://doi.org/10.1016/j.jsames.2012.11.006
Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x
Gilerson, A. A., Gitelson, A. A., Zhou, J., Gurlin, D., Moses, W., Ioannou, I. & Ahmed, S. A. (2010). Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands. Optics Express, 18(23), 24109-24125. https://doi.org/10.1364/OE.18.024109
Gitelson, A. A., Gurlin, D., Moses, W. J. & Barrow, T. (2009). A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters. Environmental Research Letters, 4, 045003. https://doi.org/10.1088/1748-9326/4/4/045003
Huot, Y., Brown, C. A. & Cullen, J. J. (2005). New algorithms for MODIS sun-induced chlorophyll fluorescence and a comparison with present data products. Limnology and Oceanography: Methods, 3, 108-130. https://doi.org/10.4319/lom.2005.3.108
Knap, A., Michaels, A., Close, A., Ducklow, H. & Dickson, A. (Eds.). (1996). Protocols for the Joint Global Ocean Flux Study (JGOFS) Core Measurements (JGOFS Report Nº. 19). Joint Global Ocean Flux Study (JGOFS). https://epic.awi.de/id/eprint/17559/1/Kna1996a.pdf
Landis, J. R. & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159-174. https://doi.org/10.2307/2529310
Mishra, S. & Mishra, D. R. (2012). Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sensing of Environment, 117, 394-406. https://doi.org/10.1016/j.rse.2011.10.016
Mobley, C. D. (1999). Estimation of the remote-sensing reflectance from above-surface measurements. Applied Optics, 38(36), 7442-7455. https://doi.org/10.1364/AO.38.007442
Morón, O., Velazco, F. & Beltrán L. (2013). Características hidrográficas y sedimentológicas de la bahía de Sechura. Inf Inst Mar Perú, 40(3-4), 150-159. https://hdl.handle.net/20.500.12958/2238
Mueller, J. L., Morel, A., Frouin, R., Davis, C., Arnone, R., Carder, K., Lee, Z. P., Steward, R. G., Hooker, S., Mobley, C. D., McLean, S., Holben, B., Miller, M., Pietras, C., Knobelspiesse, K. D., Fargion, G. S., Porter, J. & Voss, K. (2003). Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4, Volume III: Radiometric Measurements and Data Analysis Methods (NASA Technical Memorandum NASA/TM2003-211621/Rev4-Vol.III). National Aeronautical and Space administration; Goddard Space Flight Center. http://dx.doi.org/10.25607/OBP-62
O’Reilly, J. E., Maritorena, S., Mitchell, B. G., Siegel, D. A., Carder, K. L., Garver, S. A., Kharu, M. & McClain, C. (1998). Ocean color chlorophyll algorithms for SeaWIFS. Journal of Geophysical Research, 103(C11), 24937-24953. https://doi.org/10.1029/98JC02160
O’Reilly, J. E. & Werdell, P. J. (2019). Chlorophyll algorithms for ocean color sensors - OC4, OC5 & OC6. Remote Sensing of Environment, 229, 32-47. https://doi.org/10.1016/j.rse.2019.04.021
Schalles, J. F. (2006). Optical remote sensing techniques to estimate phytoplankton chlorophyll-a concentrations in coastal waters with varying suspended matter and CDOM concentrations. En L. L. Richardson & E. F. LeDrew (Eds.), Remote sensing of aquatic coastal ecosystem processes. Remote sensing and digital image processing (Cap. 3, pp.27-79). https://doi.org/10.1007/1-4020-3968-9_3
Shi, K., Li, Y., Li, L., Lu, H., Song, K., Liu, Z., Xu, Y. & Li, Z. (2013). Remote chlorophyll-a estimates for inland waters based on a cluster-based classification. Science of the Total Environment, 444, 1-15. https://doi.org/10.1016/j.scitotenv.2012.11.058
Tran, M. D., Vantrepotte, V., Loisel, H., Oliveira, E. N., Tran, K. T., Jorge, D., Mériaux, X. & Paranhos, R. (2023). Band ratios combination for estimating chlorophyll-a from Sentinel-2 and Sentinel-3 in coastal waters. Remote Sensing, 15, 1653. https://doi.org/10.3390/rs15061653
Vollenweider, R. A. (1992). Coastal marine eutrophication: principles and control. En R. A. Vollenweider, R. Marchetti & R. Viviani (Eds.), Marine coastal eutrophication. proceedings of an international conference (pp. 1–20). Elsevier Science. https://doi.org/10.1016/B978-0-444-89990-3.50011-0
Vollenweider, R. A. & Kerekes, J. (1982). Eutrophication of waters: Monitoring, assessment and control (Report of the Cooperative Programme on Eutrophication). Organization for Economic Co-Operation and Development (OECD). https://acortar.link/KnjpNe
Watanabe, F. S., Miyoshi, G. T., Rodrigues, T. W., Bernardo, N. M., Rotta, L. H., Alcântara, E. & Imai, N. N. (2020). Inland water’s trophic status classification based on machine learning and remote sensing data. Remote Sensing Applications: Society and Environment, 19, 100326. https://doi.org/10.1016/j.rsase.2020.100326
Wold, S., Sjöström, M. & Eriksson, L. (2001). PLSregression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58, 109–130. https://doi.org/10.1016/S0169-7439(01)00155-1
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