In situ Measurements

Much work has been done to develop remote sensing models for water quality parameters in inland and coastal waters, including Chl-a, turbidity, CDOM, and Kd (e.g., Mouw et al., 2013; Mouw et al., 2017; Wynne et al., 2010; Mishra & Mishra, 2012; Pahlevan et al., 2022; Nechad 2009; Dogliotti et al., 2015; Lee et al., 2021; Lopez-Baretto et al., 2024; Chen et al., 2019; Fichot et al., 2019; Lee et al., 2002), however, the work is still on-going, as many of the current models are developed for a limited geographical area or water type. It is important, therefore, to have the capability to characterize all optically active water constituents to fully understand the dynamics of aquatic systems and their associated reflectance spectra. Ideally, in situ measurements for the characterizations of an aquatic system will include quantifying IOPs, direct measurements of water quality parameters, and measurements of phytoplankton pigments, nutrients and contaminants, and phytoplankton community composition (PCC).

References

Mouw, C.B., Ciochetto, A.B., Grunert, B., Yu, A., 2017. Expanding understanding of optical variability in Lake Superior with a four-year dataset. https://doi.org/10.5194/essd-2017-10

Mouw, C.B., Chen, H., McKinley, G.A., Effler, S., O’Donnell, D., Perkins, M.G., Strait, C., 2013. Evaluation and optimization of bio‐optical inversion algorithms for remote sensing of Lake Superior’s optical properties. JGR Oceans 118, 1696–1714. https://doi.org/10.1002/jgrc.20139

Nechad, B., Ruddick, K.G., Park, Y., 2010. Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters. Remote Sensing of Environment 114, 854–866. 1403 https://doi.org/10.1016/j.rse.2009.11.022

Wynne, T.T., Stumpf, R.P., Tomlinson, M.C., Dyble, J., 2010. Characterizing a cyanobacterial bloom in Western 1566 Lake Erie using satellite imagery and meteorological data. Limnology & Oceanography 55, 2025–2036.  https://doi.org/10.4319/lo.2010.55.5.2025

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

Wynne, T.T., Stumpf, R.P., Tomlinson, M.C., Dyble, J., 2010. Characterizing a cyanobacterial bloom in Western Lake Erie using satellite imagery and meteorological data. Limnology & Oceanography 55, 2025–2036. 1567 https://doi.org/10.4319/lo.2010.55.5.2025

Pahlevan, N., Smith, B., Alikas, K., Anstee, J., Barbosa, C., Binding, C., Bresciani, M., Cremella, B., Giardino, C., 1429 Gurlin, D., Fernandez, V., Jamet, C., Kangro, K., Lehmann, M.K., Loisel, H., Matsushita, B., Hà, N.,  Olmanson, L., Potvin, G., Simis, S.G.H., VanderWoude, A., Vantrepotte, V., Ruiz-Verdù, A., 2022.  Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. Remote Sensing of Environment 270, 112860. https://doi.org/10.1016/j.rse.2021.112860

Dogliotti, A., Ruddick, K.G., Nechad, B., Doxaran, D., Knaeps, E., 2015. A single algorithm to retrieve turbidity from remotely-sensed data in all coastal and estuarine waters. Remote Sensing of Environment 156, 157–1200 168. https://doi.org/10.1016/j.rse.2014.09.020

Lee, C.M., Hestir, E.L., Tufillaro, N., Palmieri, B., Acuña, S., Osti, A., Bergamaschi, B.A., Sommer, T., 2021. Monitoring Turbidity in San Francisco Estuary and Sacramento–San Joaquin Delta Using Satellite Remote 1300 Sensing. J American Water Resour Assoc 57, 737–751. https://doi.org/10.1111/1752-1688.12917

Lopez Barreto, B.N., Hestir, E.L., Lee, C.M., Beutel, M.W., 2024. Satellite Remote Sensing: A Tool to Support 1328 Harmful Algal Bloom Monitoring and Recreational Health Advisories in a California Reservoir. GeoHealth, e2023GH000941. https://doi.org/10.1029/2023GH000941

Fichot, C.G., Tzortziou, M., Mannino, A., 2023. Remote sensing of dissolved organic carbon (DOC) stocks, fluxes and transformations along the land-ocean aquatic continuum: advances, challenges, and opportunities. Earth-Science Reviews 242, 104446. https://doi.org/10.1016/j.earscirev.2023.104446

Chen, J., Zhu, W., Pang, S., Cheng, Q., 2022. Applicability evaluation of Landsat-8 for estimating low concentration  colored dissolved organic matter in inland water. Geocarto International 37, 1–15. https://doi.org/10.1080/10106049.2019.1704071

Lee, Z., Carder, K.L., Arnone, R.A., 2002. Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters. Appl. Opt. 41, 5755. https://doi.org/10.1364/AO.41.005755