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Open AccessArticle

A New Algorithm to Estimate Chlorophyll-A Concentrations in Turbid Yellow Sea Water Using a Multispectral Sensor in a Low-Altitude Remote Sensing System

1
Department of Oceanography, Pusan National University, Busan 46241, Korea
2
Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Korea
3
Korea Maritime Institute, Busan 49111, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2257; https://doi.org/10.3390/rs11192257
Received: 16 August 2019 / Revised: 19 September 2019 / Accepted: 25 September 2019 / Published: 27 September 2019
(This article belongs to the Section Ocean Remote Sensing)
In this study, a low-altitude remote sensing (LARS) observation system was employed to observe a rapidly changing coastal environment-owed to the regular opening of the sluice gate of the Saemangeum seawall-off the west coast of South Korea. The LARS system uses an unmanned aerial vehicle (UAV), a multispectral camera, a global navigation satellite system (GNSS), and an inertial measurement unit (IMU) module to acquire geometry information. The UAV system can observe the coastal sea surface in two dimensions with high temporal (1 s−1) and spatial (20 cm) resolutions, which can compensate for the coarse spatial resolution of in-situ measurements and the low temporal resolution of satellite observations. Sky radiance, sea surface radiance, and irradiance were obtained using a multispectral camera attached to the LARS system, and the remote sensing reflectance (Rrs) was accordingly calculated. In addition, the hyperspectral radiometer and in-situ chlorophyll-a concentration (CHL) measurements were obtained from a research vessel to validate the Rrs observed using the multispectral camera. Multi-linear regression (MLR) was then applied to derive the relationship between Rrs of each wavelength observed using the multispectral sensor on the UAV and the in-situ CHL. As a result of applying MLR, the correlation and root mean square error (RMSE) between the remotely sensed and in-situ CHLs were 0.94 and ~0.8 μg L−1, respectively; these results show a higher correlation coefficient and lower RMSE than those of other, previous studies. The newly derived algorithm for the CHL estimation enables us to survey 2D CHL images at high temporal and spatial resolutions in extremely turbid coastal oceans. View Full-Text
Keywords: low-altitude remote sensing system; ocean color; chlorophyll-a; multispectral camera; unmanned aerial vehicle low-altitude remote sensing system; ocean color; chlorophyll-a; multispectral camera; unmanned aerial vehicle
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MDPI and ACS Style

Baek, J.-Y.; Jo, Y.-H.; Kim, W.; Lee, J.-S.; Jung, D.; Kim, D.-W.; Nam, J. A New Algorithm to Estimate Chlorophyll-A Concentrations in Turbid Yellow Sea Water Using a Multispectral Sensor in a Low-Altitude Remote Sensing System. Remote Sens. 2019, 11, 2257.

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