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

Abstract

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.

Evaluation of Unoccupied Aircraft System (UAS) Remote Sensing Reflectance Retrievals for Water Quality Monitoring in Coastal Waters

“…Mobley (1999) provides the recommendation of collecting radiance measurements at viewing directions of θ 40°from nadir and ɸ 135°from the sun to minimize the effects of sun glint and nonuniform sky radiance with a ⍴ value of 0.028. The suggested viewing geometries and ⍴ value from Mobley (1999) have been used to estimate and remove L SR in UAS remote sensing studies (Ruddick et al, 2006;Shang S. et al, 2017;Baek et al, 2019;Kim et al, 2020). However, more recent studies have shown that ⍴ can be spectrally dependent due to the degree of sky polarization and the ratio of diffuse to direct light; assuming a spectally constant ⍴ in a roughened sea surface with multi-angular wave facets can lead to erroneous estimates of L SR (Lee et al, 2010;Mobley, 2015).…”

“…UAS fill an operational gap between in situ and satellite remote sensing methods. While the current available commercial multispectral UAS sensor technology is geared toward terrestrial applications, mostly precision agriculture, the spectral bands have been useful in retrieving water quality parameters in aquatic water bodies (Choo et al, 2018;Arango and Nairn, 2019;Baek et al, 2019;Castro et al, 2020;Olivetti et al, 2020). A UAS sensor package designed for aquatic environments would undoubtedly improve remotely sensed retrievals and water quality measurements.…”

Water-Quality Monitoring with a UAV-Mounted Multispectral Camera in Coastal Waters

“…However, the recent surge in coastal activities such as aquaculture, tourism, and in blue economy sectors, has led to the disturbance of the ecological structure of the coastal marine environment as well as to a progressive nutrient enrichment of coastal waters. In turn, this is promoting phenomena such as eutrophication, hypoxia, or the death of aquatic animal and plant species [2][3][4]. This is due to the fact that the increase in levels of dissolved phosphorus and nitrogen in coastal waters can lead to excessive growth of algae, phytoplankton, and vegetation close to the water surface [5].…”

“…Traditional methods for water-quality monitoring required the collection of in situ water samples and a subsequent laboratory analysis for the extraction of the optical properties of water [1,3,4,10]. However, despite their high degree of analytical accuracy, these techniques are time-consuming, labor-intensive, and expensive [4,10,11].…”

“…With technological advances in remote sensing, ocean colour studies from satellite imagery have made global observations of coastal and oceanic regions possible [12]. These techniques are based on algorithms that utilize remote sensing reflectance (Rrs) values to derive waterquality parameters such as turbidity, photosynthetic pigment concentration, and coloured, dissolved organic matter (CDOM) [3,[13][14][15]. Several studies (such as those by [4,11,[16][17][18][19][20][21][22][23][24]) have proposed the use of such a tool to complement conventional techniques as it could help in operational water-quality monitoring due to its high efficiency, convenient acquisition time, low cost, and wide coverage.…”