Shallow coastal water turbidity monitoring using Planet Dove satellites

Shallow coastal water turbidity monitoring using Planet Dove satellites. Li, J., Carlson, R. R., Knapp, D. E., & Asner, G. P. Remote Sensing in Ecology and Conservation, 8(4):521–535, August, 2022.

Paper doi abstract bibtex

Turbidity monitoring in shallow coastal waters is fundamental to marine ecosystem research, management and protection. Satellite-based water turbidity monitoring can be conducted at a greater spatial extent and higher temporal frequency than ﬁeld measurements. The new Planet Dove satellite constellation has a daily revisit frequency and higher spatial resolution than Sentinel or Landsat satellites, allowing Planet Dove to track water turbidity dynamics in greater detail when suitable atmospheric correction is provided. We developed a new shallow coastal water turbidity estimation algorithm for Planet Dove and similar multi-spectral satellites. Our algorithm accounts for bottom reﬂectance in total water-leaving radiance to derive turbidity values in shallow coastal waters. We tested the algorithm with data from 235 Dove satellite images at ﬁve sites with different water conditions (Pelekane Bay, Big Island, Hawai‘i; Hilo Bay, Big Island, Hawai‘i; Kilo Nalu and Ala Wai, O‘ahu, Hawai‘i; Fagatele Bay, American Samoa; Vieques Island, Puerto Rico). We then validated satellitederived turbidity results (RMSE = 0.79–1.12 FNU [Formazin Nephelometric Unit]) using 75 days of ﬁeld-measured data, ranging in turbidity from 0.1 to 11.6 FNU in the ﬁve sites. Results show that our algorithm accurately detects turbidity in critical nearshore environments. In Hawai‘i, we used ~6700 Dove images to support a weekly turbidity monitoring study at a large geographic scale. We found this new, shallow-water algorithm can be effectively applied to Dove satellite data to monitor water turbidity at high temporal resolution.

@article{li_shallow_2022,
	title = {Shallow coastal water turbidity monitoring using {Planet} {Dove} satellites},
	volume = {8},
	issn = {2056-3485, 2056-3485},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/rse2.259},
	doi = {10.1002/rse2.259},
	abstract = {Turbidity monitoring in shallow coastal waters is fundamental to marine ecosystem research, management and protection. Satellite-based water turbidity monitoring can be conducted at a greater spatial extent and higher temporal frequency than ﬁeld measurements. The new Planet Dove satellite constellation has a daily revisit frequency and higher spatial resolution than Sentinel or Landsat satellites, allowing Planet Dove to track water turbidity dynamics in greater detail when suitable atmospheric correction is provided. We developed a new shallow coastal water turbidity estimation algorithm for Planet Dove and similar multi-spectral satellites. Our algorithm accounts for bottom reﬂectance in total water-leaving radiance to derive turbidity values in shallow coastal waters. We tested the algorithm with data from 235 Dove satellite images at ﬁve sites with different water conditions (Pelekane Bay, Big Island, Hawai‘i; Hilo Bay, Big Island, Hawai‘i; Kilo Nalu and Ala Wai, O‘ahu, Hawai‘i; Fagatele Bay, American Samoa; Vieques Island, Puerto Rico). We then validated satellitederived turbidity results (RMSE = 0.79–1.12 FNU [Formazin Nephelometric Unit]) using 75 days of ﬁeld-measured data, ranging in turbidity from 0.1 to 11.6 FNU in the ﬁve sites. Results show that our algorithm accurately detects turbidity in critical nearshore environments. In Hawai‘i, we used {\textasciitilde}6700 Dove images to support a weekly turbidity monitoring study at a large geographic scale. We found this new, shallow-water algorithm can be effectively applied to Dove satellite data to monitor water turbidity at high temporal resolution.},
	language = {en},
	number = {4},
	urldate = {2023-08-11},
	journal = {Remote Sensing in Ecology and Conservation},
	author = {Li, Jiwei and Carlson, Rachel R. and Knapp, David E. and Asner, Gregory P.},
	editor = {Scales, Kylie and Jones, Alice},
	month = aug,
	year = {2022},
	pages = {521--535},
}

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