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Article

Evaluation of GOCI Remote Sensing Reflectance Spectral Quality Based on a Quality Assurance Score System in the Bohai Sea

1
Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
2
School of Mathematical Sciences, Ocean University of China, Qingdao 266100, China
3
Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266100, China
4
College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(5), 1075; https://doi.org/10.3390/rs14051075
Submission received: 17 December 2021 / Revised: 15 February 2022 / Accepted: 18 February 2022 / Published: 22 February 2022

Abstract

:
In the application of ocean color remote sensing, remote sensing reflectance spectral (Rrs(λ)) is the most important and basic parameter for the development of bio-optical algorithms. Atmospheric correction of ocean color data is a key factor in obtaining accurate water Rrs(λ) data. Based on the QA (quality assurance) score spectral quality evaluation system, the quality of Rrs(λ) spectral of GOCI (Geostationary Ocean Color Imager) obtained from four atmospheric-correction algorithms in the Bohai Sea were evaluated and analyzed in this paper. The four atmospheric-correction algorithms are the NASA (National Aeronautics and Space Administration) standard near-infrared atmospheric-correction algorithm (denoted as Seadas—Default), MUMM (Management Unit of the North Sea Mathematical Models, denoted as Seadas—MUMM), and the standard atmospheric-correction algorithms of KOSC GOCI GDPS2.0 (denoted as GDPS2.0) and GDPS1.3 (denoted as GDPS1.3). It is shown that over 90% of the Rrs(λ) data are in good quality with a score ≥4/6 for the GDPS1.3 algorithm. The probability of Rrs(λ) with a QA score of 1 is significantly higher for the GDPS1.3 algorithm (57.36%), compared with Seadas—Default (37.91%), Seadas—MUMM (35.96%), and GDPS2.0 (33.05%). The field and MODIS measurements of Rrs(λ) were compared with simultaneous GOCI Rrs(λ), and they demonstrate that the QA score system is useful in evaluating the spectral shape of Rrs(λ). The comparison results indicate that higher QA scores have higher accuracy of the Rrs band ratio. The QA score system is helpful to develop and evaluate bio-optical algorithms based on the band ratio. The hourly variation of QA score from UTC 00:16 to 07:16 was investigated as well, and it demonstrates that the data quality of GOCI Rrs(λ) can vary in an hour scale. The GOCI data with high quality should be selected with caution when studying the hourly variation of biogeochemical properties in the Bohai Sea from GOCI measurements.

Graphical Abstract

1. Introduction

With the development of ocean color remote sensing, satellite sensors with high sampling frequency and high space coverage play an important role in studying the bio-optical properties and biogeochemical parameters of seawater [1]. For the coastal water areas with complex bio-optical properties, the diurnal variation dynamics are relatively high due to the complex components, such as phytoplankton, suspended particles, and Chromophoric Dissolved Organic Matter (CDOM) [2]. For a long time, the major ocean color observation sensors were mounted on polar-orbiting satellites, such as SeaWiFS (Sea-viewing Wide Field Sensor), MODIS (Moderate-Resolution Imaging Spectroradiometer), MERIS (Medium-Resolution Imaging Spectrometer), VIIRS (Visible Infrared Imager Radiometer Sensor), OLCI (Ocean and Land Color Instrument), and COCTS (Chinese Ocean Color and Temperature Scanner). They all generally covered the world once every 1–2 days or more, which made them unsuitable for studying the temporal and spatial variation of short time series on the coast. COMS (Communication Ocean and Meteorological Satellite), the first geostationary oceanic satellite of the world, was launched in 2010, and GOCI (Geostationary Ocean Color Imager) is the main sensor on it [3]. Compared with traditional polar-orbiting ocean water color satellites, the GOCI can provide 8 observations per day, while the second GOCI(GOCI-II) can provide 10, making it possible to observe hourly variations in biogeochemical parameters [4]. In addition, it can help monitor short-term changes in water quality, red tides, green tides, etc., in the nearshore waters [5,6]. Recently, many researchers used GOCI data to retrieve water environmental parameters, such as chlorophyll, suspended particle matter (SPM), water transparency, CDOM, sea ice, etc. [7,8,9,10,11].
The so-called spectral remote sensing reflectance Rrs(λ) data of waters are defined as the ratio of water-leaving radiance to downwelling irradiance above the sea surface, and are a crucial parameter to retrieve optical properties and biogeochemical parameters of seawater. For ocean color remote sensing, about 90% of the total signal received by the sensor is contributed from the atmosphere, while the water-leaving radiance of seawater contributes less than 10%. Therefore, atmospheric correction is preformed firstly to remove the atmospheric signals and obtain water-leaving radiance or Rrs(λ) [12]. Gordon et al. proposed an atmospheric-correction algorithm for clear oceanic waters, where the assumption of zero water-leaving radiance at the NIR bands is valid (also known as ‘black-pixel’) [13]. However, this black-pixel assumption is invalid for turbid case-2 water bodies [14,15,16,17,18]. To counter this, some researchers proposed several atmospheric-correction algorithms which account for the fact that the water-leaving radiance of case-2 water bodies is not zero in the NIR bands. These algorithms can be summarized as follows: (1) use of SWIR bands [17,19,20] or use of blue or UV bands [21,22]; (2) assuming/modeling the contributions of NIR aerosol or water [18,23,24,25,26,27]; (3) correcting/modeling of the non-negligible ocean in the NIR [14,28,29]; (4) based on a neural network method [30,31,32] or optimization method [33,34,35]. The traditional and commonly used method for evaluating the quality of ocean color data (including Rrs(λ) data) is to compare the satellite measurements with field measurements with statistical analysis. This provides important information on the overall quality of various ocean color observation data [36,37,38,39,40]. However, it has been shown that the actual meaning of the single band contrast scatter plot cannot clearly express the quality of the complete Rrs(λ) spectrum [36,39]. Many bio-optical algorithms are based on the Rrs(λ) spectrum to derive biogeochemical parameters, such as the band ratio algorithm for chlorophyll concentration, which requires more precise quality assurance of the full Rrs(λ) spectrum [41,42]. Wei et al. [43] established a QA (quality assurance) system that could objectively evaluate the spectral quality of Rrs(λ) based on large numbers of high-quality field hyperspectral reflectance data. It could objectively evaluate the spectral quality of Rrs(λ). A further test on the QA system used the NOMAD (NASA bio-Optical Marine Algorithm Dataset) remote sensing reflectance dataset and satellite remote sensing reflectance data from coastal and oceanographic regions. The results show that the QA evaluation system can identify problems or possibly erroneous Rrs(λ) spectra.
GDPS (GOCI Data Processing System) is a data analysis and processing software specially designed for GOCI sensor data. The development of the initial version of GDPS began in 2003 and was completed in 2008. GDPS provides two different modes of operation: the server (real-time) mode, which is used at the GOCI data-processing facility for real-time generation of oceanic color data and information, and the interactive mode, which is open to the scientific community. Up to now, Korea Ocean Satellite Center (KOSC) has provided GDPS1.1, GDPS1.2, GDPS1.3, GDPS1.4, GDPS1.4.1, and GDPS2.0 for global users [3,27,44,45]. Recently, NASA’s SeaDAS software also included specific modules to process GOCI’s data, making the application of alternative processing algorithms easy [46,47,48].
GOCI’s frequent measurements provide an important data source for the monitoring and research of daily changes in water quality. The evaluation and analysis of GOCI Rrs(λ) spectral quality are also particularly important. In this study, the QA score system is used to evaluate the GOCI Rrs(λ) spectrum in the Bohai Sea. We select four atmospheric-correction algorithms, the default atmospheric-correction algorithm by SeaDAS (denoted by Seadas—Default in this study), MUMM (denoted by Seadas—MUMM), the standard atmospheric-correction algorithms of KOSC GDPS2.0 (denoted by GDPS2.0), and GDPS1.3 (denoted by GDPS1.3) to process GOCI L1B data to generate Rrs(λ) spectrum. The QA scores of the four algorithms are calculated. Then, the GOCI Rrs(λ) are compared with in situ and MODIS measurements to analyze the QA score system in evaluating the Rrs(λ) spectrum in the Bohai Sea and the quality of the Rrs(λ) spectrum from different algorithms. At last, the hourly variation of GOCI Rrs(λ) QA score from UTC 00:16 to 07:16 is analyzed.

2. Data and Algorithm

2.1. Study Area

This study is focused on the Bohai Sea, China’s northernmost coastal waters, which is a nearly enclosed inland sea. According to topography and landforms, the Bohai Sea can be divided into five parts: Liaodong Bay, Bohai Bay, Laizhou Bay, Bohai Sea, and Bohai Strait (Figure 1). This sea area is the confluence of many rivers, including the famous Yellow River. Bohai Strait is the only channel for the exchange of water in the Bohai Sea with the Yellow Sea. The surrounding coastal land area is called the Bohai Rim Economic Circle; it is characterized by intense human activity, causing the rapid degradation of the Bohai Sea’s ecosystem and a decline in primary productivity [49]. The real-time dynamic monitoring of the water quality and ecological conditions of this complex sea area is thus of great significance.

2.2. Data

2.2.1. GOCI Data

GOCI is the main sensor on South Korea’s COMS Satellite. Launched in July 2010, COMS is the world’s first geostationary ocean color satellite. It covers China’s Bohai Sea, Yellow Sea, and parts of the East China Sea and captures eight images per day from 8 am to 3 pm in local time, one image per hour. The sweep width of the GOCI image is 2500 km × 2500 km, the orbital altitude is 35,837 km, the spatial resolution is 500 m, and the band range is 412–865 nm (6 visible bands and 2 near-infrared bands, as shown in Table 1). The GOCI L1B data of 27 August 29 to 2015, and September 2015, with a total of 264 images, were used in this paper, as obtained from: http://kosc.kiost.ac.kr/, accessed on 15 June 2021.

2.2.2. MODIS/Aqua Data

Aqua is a solar synchronous polar-orbiting satellite. Moderate-resolution Imaging Spectroradiometer (MODIS) is one of the main sensors mounted on Aqua. Its sweep width is 2330 km, its spectral band range is 140–1440 nm, and it has 36 spectral bands. In this paper, Aqua MODIS L2 daily Rrs data of September 2015, with a total of 30 images, were applied. We obtained those MODIS Rrs data from the NASA Ocean Biology Processing Group (OBPG, http://oceancolor.gsfc.nasa.gov/, accessed on 15 June 2021), which were processed with the most recent updates in calibration and algorithms.
Bands settings of GOCI and MODIS are as follows (Table 1).

2.2.3. In Situ Data

From 27 to 29 August 2015, we conducted an in situ experiment of Marine optics in the Yellow Sea and Bohai Sea. The upward radiance profile data and downward irradiance profile data were measured with a hyperspectral radiometer, Profiler II, whose manufacturer is Satlantic. Its downward irradiance data, Ed(λ, z), and upward radiance data, Lu(λ,z), can be expressed as:
E d ( λ , z ) = E d ( λ , 0 ) exp [ K d ( λ ) × z ]
L u ( λ , z ) = L u ( λ , 0 ) exp [ K L ( λ ) × z ]
where Ed(λ, 0) and Lu(λ, 0) are the downward irradiance and upward radiance just below the sea surface, respectively. Kd(λ) and KL(λ) are diffuse attenuation coefficients of downward irradiance and upward radiance, respectively. According to Equations (1) and (2), the measured data of Ed(λ, z) and Lu(λ, z) were fitted to obtain Ed(λ, 0), Lu(λ, 0), Kd(λ), and KL(λ). Then, Rrs(λ) can be obtained by Equation (3) [1].
rrs ( λ ) = L u ( λ , 0 ) / E d ( λ , 0 ) , R r s ( λ ) = 0.52 × rrs ( λ ) / ( 1 1.7 × rrs ( λ ) )

2.3. Algorithm

2.3.1. Atmospheric-Correction Algorithms of GDPS

The operational algorithm of atmospheric correction for GDPS to process GOCI data adopts the bright pixel method, which is implemented by improving the iterative model for calculating NIR water-leaving reflectance based on the standard SEAWIFS algorithm. Because of the absence of SWIR band in GOCI, GDPS1.3 used the empirical relation between the red band and NIR water-leaving reflectance to calculate the NIR reflectance ρ w (Equations (4) and (5)) [44]. GDPS1.4 mainly updates the modularization of the software based on GDPS1.3 and fixes some minor problems of modularization. The atmospheric-correction algorithm of GDPS1.3 and GDPS1.4 are identical, and the Rrs(λ) obtained from them were the same [44].
ρ w ( 745 ) = n = 1 6 j n ρ w n ( 660 )
ρ w ( 865 ) = n = 1 2 k n ρ w ( 745 )
The atmospheric correction of GDPS2.0 makes use of the SRAMS (spectral relationships in the aerosol multiple-scattering reflectance) among different wavelengths to directly calculate the contribution of near-infrared multiple-scattering reflectance. Then, the reflectance contribution of the near-infrared band to the visible band of the aerosol model is estimated by the SRAMS spectrum [27]. The spectral relation between the reflection spectra of multi-scattered aerosols and different wavelengths is established by a polynomial function (Equation (6)):
ρ w ( λ 2 ) = n = 1 D c n ρ w n ( λ 1 )
For GOCI data, the spectral relations of each GOCI spectral segment are summarized in Table 2. D represents the calculation order.
Compared with the atmospheric-correction algorithm of GDPS1.1 and GDPS1.2, GDPS1.3 adds the calculation order of the empirical relation of the water-leaving reflectance. The atmospheric-correction algorithm of GDPS2.0 is different from the previous version. Then, the default atmospheric-correction algorithms of GDPS1.3 and GDPS2.0 were used in this paper to conduct atmospheric correction on GOCI L1B data. The corrected remote sensing reflectance spectral (Rrs(λ)) data were obtained.

2.3.2. Atmospheric-Correction Algorithms of SeaDAS

SeaDAS provides a variety of atmospheric-correction algorithms for users in the atmospheric correction of remote sensing data. In this paper, the default atmospheric-correction algorithm of SeaDAS 7.5 (i.e., NASA standard atmospheric-correction algorithm, denoted as Seadas—Default in this paper) and MUMM atmospheric-correction algorithm (denoted as Seadas—MUMM in this paper) are used to conduct atmospheric correction on GOCI data. Then, we can obtain the corrected remote sensing reflectance spectral (Rrs(λ)) data.
The default atmospheric-correction algorithm for SeaDAS was originally developed by Gordon and Wang [13] in 1994, and in 2003, Stumpf et al. [28] extended its application to case-2 waters. Bailey et al. revised it in 2010, also exhibiting good performances for complex optical water bodies [50]. This algorithm assumes that the remote sensing reflectance with the removal of the reflection of atmospheric molecules is either only related to aerosols or only related to waters. First, ρ w at 443 nm, 490 nm, and 555 nm are retrieved based on the black-pixel assumption. Then, based on the bio-optical model (Equation (7)), the absorption of particles and CDOM in the red band is determined. Additionally, the particulate backscattering in the red band and NIR band can be computed. At last, ρ w in the NIR bands are generated.
ρ w ( λ ) = f ( λ , C hl )
The MUMM atmospheric-correction algorithm was proposed by Ruddick in 2000 [24]. The assumptions of this algorithm consist of two parts:
(1)
The aerosol multiple-scattering reflectance ratio of the two near-infrared bands of each pixel has a fixed value, defined as ε(745,865), then:
ε ( 745 , 865 ) = ρ A ( 745 ) ρ A ( 865 ) ,
where ρ A includes both Rayleigh and aerosol scatterings, as well as the interaction between them.
(2)
The ratio between reflectance and atmospheric transmission at the two near-infrared bands (α(745,865)) is constant and equal to 1.945.
α ( 745 , 865 ) = ρ w ( 745 ) / t ( 745 ) ρ w ( 865 ) / t ( 865 ) = 1.945 ,
where ρ w is the water-leaving reflectance, and t is the diffuse transmittance from the sun to the ocean atmosphere.
Then, using the set value of α and the estimated values of ε, Equations (10) and (11) are defined. The values of ρ A (745) and ρ A (865) are estimated to select appropriate aerosol models. Finally, the aerosol models are reentered into the black-pixel assumption, and ρ w data are obtained.
ρ A ( 865 ) = α ρ rc ( 865 ) ρ rc ( 745 ) α ε ( 745 , 865 )
ρ A ( 745 ) = ε ( 745 , 865 ) ( α ρ rc ( 865 ) ρ rc ( 745 ) α ε ( 745 , 865 ) )

2.3.3. QA Score System

QA score system is based on the clustering analysis of optical water types, which is also the core point of the QA system. The steps for the establishment and application of the QA system are as follows:
Firstly, the reference Rrs spectra were normalized by their respective root of the sum of squares to obtain nRrs.
n R r s ( λ ) = R r s ( λ ) [ i = 1 N R r s ( λ i ) 2 ] 1 / 2
where N represents the total number of wavelengths equal to 9. λi corresponds to the wavelengths of 412, 443, 488, 510, 531, 547, 555, 667, and 678 nm. The nRrs spectra vary over the range between 0 and 1, while they retain the ‘shapes’ pertaining to the original Rrs spectra, i.e., the band ratios of nRrs(λ) remain the same as Rrs(λ). If the Rrs(λ) data were measured at other wavelengths, it is needed to find the closest wavelength from the nine bands. Finally, the gap method was used to determine the optimal number of clustering k = 23, which was exactly consistent with the number of optical water types.
Secondly, we obtained the spectra of normalized remote sensing reflectance of 9 bands of 23 kinds of optical waters. Through the nRrs spectrum, the upper boundary and lower boundary values of each band’s nRrs spectrum of each water can be obtained, as well as the average value of the nRrs spectrum. Thus, the average value of the nRrs spectrum, the upper boundary of the nRrs spectrum, nRrsU, and the lower boundary of the nRrs spectrum, nRrsL, form the key part of the QA evaluation system [43].
Thirdly, we can give a target Rrs*(λ′) and evaluate it with the QA system. First, we must figure out whether the target Rrs*(λ′) band matches that of nRrs(λ). If the spectral bands of Rrs*(λ′) are more than those of nRrs(λ), only the bands with the same wavelength as nRrs(λ) are selected for further analysis. If the spectral bands of Rrs*(λ′) are fewer than those of nRrs(λ) (i.e., the total number of bands is less than nine), the nRrs(λ′) subset corresponding to λ′ and the corresponding nRrsU(λ′) subset and nRrsL(λ′) subset need to be extracted from nRrs(λ). Then, nRrs*(λ′) is obtained after the normalization of Rrs*(λ′) with Equation (12). According to the similarity equation of SAM spectrum proposed by Kruse [51] (Equation (13)), we can assign a water type for nRrs*(λ′).
cos α = i = 1 N [ n R r s ( λ ) n R r s ( λ ) ] i = 1 N [ n R r s ( λ   i ) ] 2 i = 1 N [ n R r s ( λ   i ) ] 2
where α is the angle formed between the reference spectrum, nRrs, and the normalized target spectrum, nRrs*. SAM can determine the spectral similarity by taking them as the space vector whose dimension is equal to the band number N, and the spectral water type corresponding to the maximum cosine value is determined as the water type of the target spectrum, nRrs*.
Finally, the QA score is estimated by comparing the upper and lower boundary values of the target spectrum nRrs* with the spectra of water types (Equation (14)).
C tot = C ( λ 1 ) + C ( λ 2 ) + + C ( λ N ) N
C(λi) is the score for a particular wavelength, and N is the total number of bands in nRrs*. If the value of nRrs*i) is not in the range of nRrsUi) and nRrsLi), the wavelength score will be assigned 0, that is, C(λi) = 0; otherwise, C(λi) = 1. As can be seen from Equation (14), the total score of nRrs* varies within the range of [0, 1]. Additionally, a higher score means better data quality.

3. Results

3.1. Statistical Analysis of the Rrs(λ) QA Score

The total QA score of all the six GOCI bands is expressed as n/6 (n = 0,1,2,3,4,5,6), and n is the total number of bands where the score of the specific band is 1. Figure 2 shows the frequency distribution of GOCI Rrs(λ) QA scores in the Bohai Sea in Sep. 2015, obtained from the atmospheric-correction algorithms of Seadas—Default, Seadas—MUMM, GDPS2.0, and GDPS1.3, and Table 3 lists the values of the frequencies. More than 48 million data were counted, with about 12.2+ million available pixels for Seadas—Default, 12.2+ million available pixels for Seadas—MUMM, 11.7+ million available pixels for GDPS2.0, and 11.8+ million available pixels for GDPS1.3. It can be seen that the frequency increases with scores, especially for scores from GDPS1.3. A total of 57.36% of the Rrs(λ) data from the GDPS1.3 has a QA score of 1, while for the other three atmospheric-correction algorithms, less than a half of the Rrs(λ) data have a score of 1, i.e., 37.91% for Seadas—Default, 35.96% for Seadas—MUMM, and 33.05% for GDPS2.0. If we take the score of 4/6 (~0.67) as a relatively high score, about 93% of the Rrs(λ) data are of good quality with a score no less than 4/6 for the GDPS1.3 algorithm. For the other three atmospheric-correction algorithms, about 81–88% of the Rrs(λ) data has a relatively high quality. Therefore, the frequency distribution of the QA score reveals that GOCI Rrs(λ) data have good quality in the Bohai Sea, and the atmospheric-correction algorithm embedded in GDPS1.3 is more suitable.
The study region is separated into the ‘Three Bays’ and the ‘Bohai Sea’ (Figure 1). The Three Bays refers to the Bohai Bay, Liaodong Bay, and Laizhou Bay, where the optical properties of seawater are influenced by human activities. The Bohai Sea refers to the central parts of the region where the seawater is relatively cleaner than the bays. Figure 3 shows the frequency distribution of the QA score at the Three Bays and the Bohai Sea, respectively. Table 4 lists the values of the frequencies. It is obvious that the QA score for the Three Bays is higher than that for the Bohai Sea using any atmospheric-correction algorithm. About 90% of the Rrs(λ) data has a QA score ≥4/6 for the Three Bays, while for the Bohai Sea, only the Rrs(λ) from GDPS1.3 has about 90% of the data with a score ≥4/6. The results indicate the atmosphere correction algorithms are more valid in the coastal areas of the Bohai Sea region.

3.2. Comparison of Rrs(λ) with Measured In Situ Data

We can consider that the QA score system is developed for evaluating the quality of an individual Rrs(λ) spectrum. The in situ measurements of Rrs(λ) were used to compare with GOCI Rrs(λ) derived from the four atmospheric-correction algorithms with different QA scores. Due to the frequent cloud cover in the study area, the region for the match-up of in situ and GOCI Rrs(λ) data was extended to the neighboring North Yellow Sea [49]. The spatial and temporal windows of match-up were relaxed to 300 m and 3 h, respectively. Finally, there are seven sets of match-up data in total, and their locations are marked in Figure 1 (red dots). Although the number of match-up data is small, the here is to illustrate the advantage of the QA score system in measuring the spectral shape of GOCI Rrs(λ), where seven sets of match-ups is acceptable. Three examples of in situ and GOCI Rrs(λ) spectra are shown in Figure 4 with captions of QA scores. It was found that the spectra shape of the Rrs(λ) from GDPS1.3 agrees very well with that of in situ Rrs(λ), with the highest QA score of 4/6 or 3/6. However, the magnitude of single-band Rrs(λ) from GDPS1.3 is not always in the best agreement with the in situ data. The QA score is a good indicator for evaluating the spectra shape of Rrs(λ). This is obviously shown in Figure 4c. The QA scores of the Rrs(λ) from GDPS1.3 and Seadas—Default are both 4/6, but their magnitudes differ significantly. The Rrs(λ) from Seadas—Default agrees with the in situ Rrs(λ) very well.
Since the QA score is more valid in measuring Rrs(λ) spectra shape, it is interesting to evaluate the band ratio of GOCI Rrs(λ) by the QA score system. NASA OC2M-HI [42] is a widely used algorithm for retrieving chlorophyll concentration, using the band ratio of Rrs(469)/Rrs(555). Figure 5 exhibits the values of GOCI and in situ match-up Rrs at 469 and 555 nm, as well as the values of the band ratio. Because GOCI does not have a band similar to the wavelength of 469 nm, the value of Rrs(469) was generated by a linear interpolation model from the existing GOCI bands. Table 5 shows the averaged unbiased percentage difference ε, which is defined as Equation (15), the root mean square error RMSE, and the average QA score. It is clear that although the values of GOCI Rrs(λ) at 469 and 555 nm from GDPS1.3 (Figure 5 red circles) do not agree well with the in situ data (black circles), the value of the band ratio Rrs(469)/Rrs(555) is close to the value of in situ data. As seen from Table 5, for GDPS1.3, the value of ε is 59.26% and 51.01% for Rrs(469) and Rrs(555), respectively, while the value of Rrs(469)/Rrs(555) decreases to 10.34%. Accordingly, the average value of the QA score of GDPS1.3 is the highest, i.e., ~0.64. The GDPS2.0 algorithm generates the lowest QA score of Rrs(λ), i.e., ~0.31, and the percentage difference ε of the band ratio Rrs(469)/Rrs(555) between the in situ and GDPS2.0 is the largest, i.e., 43.04%. However, for a single wavelength, the ε between the in situ and GOCI Rrs(λ) from GDPS2.0 is the smallest, i.e., 15.84% at 555 nm. Therefore, if the band ratio of Rrs(λ) spectrum needs to be applied in the development of the bio-optical algorithm, it is suggested to carry out comprehensive quality evaluation using the QA score system to select the most suitable atmospheric-correction algorithm.
ε = 1 n 1 n [ R r s - g o c i R r s - in _ s i t u ] / [ R r s - g o c i + R r s - in _ s i t u ] 200 %

3.3. Hourly Variation of the GOCI Rrs(λ) QA Score from UTC 00:16 to 07:16

The spatial distribution of the GOCI hourly Rrs QA score in the Bohai Sea on 13 September 2015 is presented in Figure 6, Figure 7, Figure 8 and Figure 9 for different atmospheric-correction algorithms. In general, the QA score of GOCI Rrs(λ) spectral data was extremely lower close to the Bohai Strait, especially for the algorithms of GDPS2.0, Seadas—Default and Seadas—MUMM. The QA score of the Rrs generated from GDPS1.3 is high at the eight observation times, except for the data at UTC time 06:16 (Local Time 14:16). Only the score from GDPS2.0 at UTC 06:16 is relatively high. There is a small hourly variation of GOCI spectral quality when we use the atmospheric correction of GDPS2.0. It was noted that the hourly Rrs(λ) measurement of GOCI at different times has a different spectral quality. The quality of the GOCI Rrs(λ) spectra exhibits obvious hourly variations for the Seadas—Default and Seadas—MUMM algorithms. In general, the QA score of GOCI Rrs(λ) is high for the measurements between UTC 02:00 and 04:00. The same characteristic was found in GOCI measurements on 07 September, 19 September, and 20 September 2015 (not shown here).
Considering that the Bohai Strait plays an important role in the water exchange between the Bohai Sea and the Yellow Sea, its waters’ environment and optical properties are more complicated. The QA score of the GOCI Rrs(λ) spectra obtained by different atmospheric-correction algorithms in the Bohai Strait on 13 September 2015 was calculated. Figure 10 shows the average values of the score at eight observation times. The quality of Rrs(λ) spectral from GDPS1.3 is good except for the observation at UTC 06:16. The QA scores of Rrs(λ) from GDPS2.0 are relatively high at the eight observation times. For GDPS2.0 and Seadas MUMM algorithms, the QA scores of Rrs(λ) obtained by them were relatively consistent from UTC 02:16 to 07:16. When using GOCI measurements for the study of hourly variation from UTC 00:16 to 07:16 in the Bohai Strait, the Rrs(λ) spectral data derived by GDPS1.3 or GDPS2.0 are helpful.

3.4. Cross-Comparison between GOCI and MODIS Rrs Data

MODIS data have been extensively used in various ocean studies. Shang et al. [49] validated MODIS Rrs(λ) data by in situ measurements in the Bohai Sea. They showed that MODIS Rrs(λ) is in good agreement with the in situ data, with a percentage difference in a range of 9–31% for different bands. In this study, the MODIS L2 product of Rrs(λ) was used to compare with GOCI Rrs(λ) for statistical analysis in order to overcome the limitation of fewer in situ measurements.
Only the MODIS Rrs(λ) data with a QA score of 1 were used here to ensure the spectral quality of MODIS Rrs(λ). The match-up scheme between MODIS and GOCI data is that the temporal window is no more than 30 min, and the spatial window is no more than 200 m. Finally, a total of 8581 sets of match-up data were obtained in Bohai in September 2015. Figure 11 illustrates the scatter plots of MODIS and GOCI match-ups at 412, 469, and 555 nm, as well as the band ratio of Rrs(469)/Rrs(555). The MODIS L2 Rrs(λ) were processed with the default atmospheric-correction algorithm, the same as Seadas—Default for GOCI. The correlation coefficient r, root mean square error RMSE, the defined averaged unbiased percentage difference ε, and the average QA score are given in Table 6 and Table 7 for statistical analysis.
As seen in Table 6, the QA scores are high for all four algorithms, i.e., >0.9. Similarly, the errors of band ratio between GOCI and MODIS are also small, ~10% (Table 7). However, Figure 11 and Table 6 show that the error and correlation between GOCI and MODIS Rrs are variable with wavelength. For the blue bands, i.e., 412 and 443 nm, the errors between GOCI and MODIS Rrs from the SeaDAS algorithms (Default and MUMM) are relatively smaller than those from the GDPS algorithms (GDPS1.3 and GDPS2.0). The Seadas—Default atmospheric-correction algorithm at 412 nm and 443 nm bands are better than those of the other three algorithms. With a wavelength increase, the relative errors from the GDPS algorithms become smaller. For example, ε is less than 10% for GDPS2.0 and GDPS1.3 at 490, 531, and 555 nm. The correlation between GOCI and MODIS Rrs is good except for that from the Seadas—MUMM algorithm at 412 nm.
Although the QA score is quite good for any atmospheric-correction algorithm, the difference of Rrs(λ) between GOCI and MODIS may be large at a single band (Table 7). For example, the error at 469 nm is in a range of 9.42–23.91% for different atmospheric-correction algorithms, while the error at 555 nm is smaller than that at 469 nm, i.e., 5.65–13.91%. However, the error of the band ratio of Rrs(469)/Rrs(555) is high in a range of 9.70–10.81%. The comparison of Rrs(λ) between GOCI and MODIS also indicates that the QA score system is helpful in measuring the spectral shape of Rrs(λ) and evaluating the band ratio for developing bio-geochemical algorithms.

4. Discussion

It is traditional to validate ocean color measurements of Rrs(λ) by comparing them with field measurements. In 2019, Huang et al. [37] used in situ measured Rrs to statistically analyze the suitability of four atmospheric-correction algorithms for GOCI data in the Yellow Sea. This is useful to evaluate the accuracy of retrieval of Rrs(λ) at a single band. However, it does not evaluate the whole spectrum as a unified spectral curve. The QA score system overcomes the limitation of lack of in situ match-up data and measures the whole Rrs(λ) spectrum. The research results of our study have indicated that even if the Rrs value of a single band is closer to the in situ measured value, there may be a big difference in the band ratio between the ocean color and the in situ measurements, which means a large difference in the Rrs(λ) spectral shape. Therefore, it is of great significance to evaluate the quality of the whole spectrum using the QA score system in developing a bio-optical algorithm. Based on the analysis of this study (Section 3.1 and Section 3.4), the Seadas—Default atmospheric-correction algorithm would be recommended to process GOCI L1B data if only the Rrs values at 412 nm and 443 nm are used in the Bohai Sea areas. The GDPS2.0 atmospheric-correction algorithm would be better when using the Rrs values at 490 nm, 531 nm, 555 nm, and 660 nm. However, if the band-ratio of Rrs(λ) are applied for the development of bio-optical models in the Bohai Sea areas, the GDPS1.3 (or GDPS1.4*) atmospheric-correction algorithm can be considered.
In this study, MODIS L2 Rrs(λ) data were used as the data set to evaluate the spectral quality of GOCI Rrs(λ), rather than operational Rrs(λ) products of other ocean color sensors. Firstly, in 2016, Shang et al. [49] clearly showed that in the Bohai Sea, MODIS L2 Rrs data provided by OBPG have good consistency with the measured in situ Rrs data (refer to Figure 2 and Figure 3 of their paper) by comparing 20 sets of in situ measurements with the match-up MODIS satellite data. Secondly, the atmospheric-correction algorithm used by NASA OBPG to obtain MODIS L2 Rrs includes an optical model for turbidity and optically complex waters, which makes MODIS L2 Rrs data valid in this area [50]. In this study, there are only seven sets of GOCI and in situ match-up measurements in total. However, this is enough for illustrating that the QA score system is more helpful in evaluating the spectral data. A greater amount of field measurements is much better for further statistical analysis.

5. Conclusions

The remote sensing reflectance spectrum is of great importance in the retrieval of bio-geo-optical parameters of seawater from ocean color remote sensing. GOCI can provide eight observations of the Rrs(λ) spectra every day. In practical work, atmospheric correction is the key factor to obtaining accurate spectral data of Rrs(λ). In this study, the adaptability of four atmospheric-correction algorithms for deriving GOCI Rrs(λ) measurements in the Bohai Sea were evaluated and analyzed based on the QA score spectral quality evaluation system. The results demonstrate that in any area of the Bohai Sea, the probability that the QA score of Rrs(λ) equals 1 is higher when using the GDPS1.3 atmospheric-correction algorithm instead of the other three atmospheric-correction algorithms. Over 90% of the Rrs(λ) data are of good quality with a score ≥4/6 for the GDPS1.3 algorithm. For any atmospheric-correction algorithm, the QA score is higher in the Three Bays (i.e., Bohai Bay, Liaodong Bay, and Laizhou Bay) than that in the central parts of the Bohai Sea.
The comparison of GOCI Rrs(λ) with in situ and MODIS measurements of Rrs(λ) indicates that the QA score system is valid in measuring Rrs(λ) spectral shape. Therefore, the QA score has a higher correlation with the accuracy of Rrs band ratio rather than the accuracy of Rrs at a single band. For example, the in situ match-up data illustrate that the Rrs(λ) from GDPS1.3 with a high QA score are not always in the best agreement with the in situ Rrs(λ) at a single band. At the same time, the error of the Rrs(λ) band ratio is the smallest. The actual meaning of scatter plots of GOCI vs. MODIS Rrs(λ) data at a single band cannot express the quality of the complete Rrs(λ) spectrum clearly. It is necessary to evaluate and analyze the spectral quality overall. According to the result of this study, it is suggested that the GDPS1.3 (or GDPS1.4*) atmospheric-correction algorithm be used when using the band ratio of Rrs(λ) for the development of bio-optical models in the Bohai Seas. The results of this study also provide a new idea for the selection of the atmospheric-correction algorithms. The hourly variation of QA score between UTC 00:16–07:16 demonstrates that the data quality of GOCI Rrs(λ) can vary on an hour scale. When using GOCI measurements at eight observation times to study the variation of dynamical changes, the GOCI data with high quality should be selected with caution.

Author Contributions

Data Curation, X.L. and Y.Z.; Methodology, X.L.; Resources, Y.W.; Validation, X.L. and Q.Y.; Writing—Original Draft, X.L. and Q.Y.; Writing—Review and Editing, X.L., Q.Y. and Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China, grant number 2019YFC1408003, and the Natural Science Foundation of Shandong Province, grant number ZR2019PD021.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We express the gratitude of our team to the KOSC for providing us access to GOCI data and GDPS software. Additionally, we are also grateful to the NASA Ocean Biology Processing Group for providing us with the MODIS/Aqua data and SeaDAS software. Finally, we would like to thank Wei et al. for providing the source code of the QA score.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The map of Bohai Bay, Laizhou Bay, Liaodong Bay, Bohai Sea, and Bohai Strait. The red dots indicate the field experimental station used in this study.
Figure 1. The map of Bohai Bay, Laizhou Bay, Liaodong Bay, Bohai Sea, and Bohai Strait. The red dots indicate the field experimental station used in this study.
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Figure 2. Statistical results of QA score of GOCI Rrs(λ) data processed by four atmospheric-correction algorithms in the Bohai area.
Figure 2. Statistical results of QA score of GOCI Rrs(λ) data processed by four atmospheric-correction algorithms in the Bohai area.
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Figure 3. Statistical probability of GOCI Rrs(λ) QA score of four atmospheric-correction algorithms in each sea area of Bohai Sea. (a)Three Bays: Bohai Bay, Liaodong Bay, and Laizhou Bay; (b) Bohai Sea.
Figure 3. Statistical probability of GOCI Rrs(λ) QA score of four atmospheric-correction algorithms in each sea area of Bohai Sea. (a)Three Bays: Bohai Bay, Liaodong Bay, and Laizhou Bay; (b) Bohai Sea.
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Figure 4. GOCI Rrs(λ) spectra obtained from four atmospheric-correction algorithms versus in situ Rrs(λ) spectra in the three matched stations. (ac) respectively correspond to the three station points in Figure 1.
Figure 4. GOCI Rrs(λ) spectra obtained from four atmospheric-correction algorithms versus in situ Rrs(λ) spectra in the three matched stations. (ac) respectively correspond to the three station points in Figure 1.
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Figure 5. Comparison results between GOCI Rrs(469), Rrs(555), and Rrs(469)/Rrs(555) obtained from four atmospheric-correction algorithms and in situ data of the 7 sets of matched data. (a) Rrs(469); (b) Rrs(555); (c) Rrs(469)/Rrs(555).
Figure 5. Comparison results between GOCI Rrs(469), Rrs(555), and Rrs(469)/Rrs(555) obtained from four atmospheric-correction algorithms and in situ data of the 7 sets of matched data. (a) Rrs(469); (b) Rrs(555); (c) Rrs(469)/Rrs(555).
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Figure 6. Spatial distribution diagram of GOCI Rrs(λ) QA score obtained from GDPS1.3—Default atmospheric-correction algorithm on September 13, 2015. (a) 00:16; (b) 01:16; (c) 02:16; (d) 03:16; (e) 04:16; (f) 05:16; (g) 06:16; (h) 07:16.
Figure 6. Spatial distribution diagram of GOCI Rrs(λ) QA score obtained from GDPS1.3—Default atmospheric-correction algorithm on September 13, 2015. (a) 00:16; (b) 01:16; (c) 02:16; (d) 03:16; (e) 04:16; (f) 05:16; (g) 06:16; (h) 07:16.
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Figure 7. Spatial distribution diagram of GOCI Rrs(λ) QA score obtained from GDPS2.0—Default atmospheric-correction algorithm on 13 September 2015. (a) 00:16; (b) 01:16; (c) 02:16; (d) 03:16; (e) 04:16; (f) 05:16; (g) 06:16; (h) 07:16.
Figure 7. Spatial distribution diagram of GOCI Rrs(λ) QA score obtained from GDPS2.0—Default atmospheric-correction algorithm on 13 September 2015. (a) 00:16; (b) 01:16; (c) 02:16; (d) 03:16; (e) 04:16; (f) 05:16; (g) 06:16; (h) 07:16.
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Figure 8. Spatial distribution diagram of GOCI Rrs(λ) QA score obtained from Seadas—Default atmospheric-correction algorithm on 13 September 2015. (a) 00:16; (b) 01:16; (c) 02:16; (d) 03:16; (e) 04:16; (f) 05:16; (g) 06:16; (h) 07:16.
Figure 8. Spatial distribution diagram of GOCI Rrs(λ) QA score obtained from Seadas—Default atmospheric-correction algorithm on 13 September 2015. (a) 00:16; (b) 01:16; (c) 02:16; (d) 03:16; (e) 04:16; (f) 05:16; (g) 06:16; (h) 07:16.
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Figure 9. Spatial distribution diagram of GOCI Rrs(λ) QA score obtained from Seadas—MUMM atmospheric-correction algorithm on 13 September 2015. (a) 00:16; (b) 01:16; (c) 02:16; (d) 03:16; (e) 04:16; (f) 05:16; (g) 06:16; (h) 07:16.
Figure 9. Spatial distribution diagram of GOCI Rrs(λ) QA score obtained from Seadas—MUMM atmospheric-correction algorithm on 13 September 2015. (a) 00:16; (b) 01:16; (c) 02:16; (d) 03:16; (e) 04:16; (f) 05:16; (g) 06:16; (h) 07:16.
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Figure 10. Hourly variations of GOCI Rrs(λ) QA score from UTC 00:16 to 07:16 in Bohai Strait area on 13 September 2015.
Figure 10. Hourly variations of GOCI Rrs(λ) QA score from UTC 00:16 to 07:16 in Bohai Strait area on 13 September 2015.
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Figure 11. The scatter diagram of data comparison between GOCI and MODIS at 412 nm, 469 nm, 555 nm, and Rrs-chla (Rrs-chla = Rrs(469)/Rrs(555)). The abscissa represents MODIS Rrs data obtained from OBPG, and the ordinate represents GOCI Rrs data matching MODIS obtained from the four atmospheric-correction algorithms.
Figure 11. The scatter diagram of data comparison between GOCI and MODIS at 412 nm, 469 nm, 555 nm, and Rrs-chla (Rrs-chla = Rrs(469)/Rrs(555)). The abscissa represents MODIS Rrs data obtained from OBPG, and the ordinate represents GOCI Rrs data matching MODIS obtained from the four atmospheric-correction algorithms.
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Table 1. Common bands of MODIS and GOCI.
Table 1. Common bands of MODIS and GOCI.
BandMODIS Wavelength (nm)BandGOCI Wavelength (nm)
14121412
24432443
34693490
44884555
55315660
65476680
75557745
86678865
9678
10748
11859
12869
Table 2. Spectral relations of each GOCI spectral band.
Table 2. Spectral relations of each GOCI spectral band.
λ1 (nm)555555555745745745865
λ2 (nm)412430490555660680745
D4444332
Table 3. Probability statistical table of QA score distribution of Rrs(λ) data obtained from each atmospheric-correction algorithm over the whole Bohai area.
Table 3. Probability statistical table of QA score distribution of Rrs(λ) data obtained from each atmospheric-correction algorithm over the whole Bohai area.
QA ScoreFrequency
Seadas—DefaultSeadas—MUMMGDPS2.0GDPS1.3
02.48%0.65%0.34%0.29%
1/63.98%1.67%1.28%0.66%
2/65.51%4.28%3.50%1.68%
3/66.68%8.56%6.32%4.42%
4/613.05%16.65%16.36%9.63%
5/630.39%32.24%39.17%25.96%
137.91%35.96%33.05%57.36%
Table 4. Probability statistical table of QA score distribution of Rrs(λ) data obtained from each atmospheric-correction algorithm over the different areas of Bohai.
Table 4. Probability statistical table of QA score distribution of Rrs(λ) data obtained from each atmospheric-correction algorithm over the different areas of Bohai.
AreaQA ScoreFrequency
Seadas—DefaultSeadas—MUMMGDPS2.0GDPS1.3
Three Bays01.36%0.488%0.241%0.22%
1/61.24%1.100%0.621%0.44%
2/61.74%2.155%1.474%1.54%
3/64.45%6.034%3.784%4.39%
4/612.41%15.407%15.612%9.30%
5/634.78%35.613%42.780%25.33%
144.02%39.203%35.487%58.78%
Bohai Sea03.60%0.81%0.43%0.35%
1/66.73%2.23%1.94%0.87%
2/69.28%6.41%5.52%1.82%
3/68.91%11.08%8.85%4.46%
4/613.69%17.89%17.10%9.96%
5/626.00%28.86%35.56%26.60%
131.79%32.72%30.60%55.95%
Table 5. The parameter values of the four GOCI atmospheric-correction algorithms in Rrs(469), Rrs(555), and Rrs-chla = Rrs(469)/Rrs(555) compared with in situ data.
Table 5. The parameter values of the four GOCI atmospheric-correction algorithms in Rrs(469), Rrs(555), and Rrs-chla = Rrs(469)/Rrs(555) compared with in situ data.
RrsAtmospheric
Correction Algorithms
εRMSEMean of QA Score
Rrs(469)Seadas—Default30.31%0.00091 0.595
Seadas—MUMM76.59%0.00309 0.476
GDPS2.054.59%0.00172 0.310
GDPS1.359.26%0.00198 0.643
Rrs(555)Seadas—Default11.13%0.00031 0.595
Seadas—MUMM50.26%0.00212 0.476
GDPS2.015.84%0.00048 0.310
GDPS1.351.01%0.00192 0.643
Rrs(469)/Rrs(555)Seadas—Default22.96%0.26533 0.595
Seadas—MUMM29.82%0.32298 0.476
GDPS2.043.04%0.50643 0.310
GDPS1.310.34%0.10863 0.643
Table 6. The parameter values of the data-comparison scatter diagram between GOCI and MODIS at each band of the four atmospheric-correction algorithms.
Table 6. The parameter values of the data-comparison scatter diagram between GOCI and MODIS at each band of the four atmospheric-correction algorithms.
Band (nm)Atmospheric
Correction Algorithms
rεRMSEQA Score
412Seadas—Default0.832 27.53%0.00133 0.938
Seadas—MUMM0.580 38.72%0.00199 0.922
GDPS2.00.847 73.07%0.00364 0.921
GDPS1.30.800 56.70%0.00247 0.967
443Seadas—Default0.932 15.96%0.00129 0.938
Seadas—MUMM0.817 19.14%0.00186 0.922
GDPS2.00.939 26.39%0.00181 0.921
GDPS1.30.904 19.61%0.00136 0.967
490Seadas—Default0.971 20.78%0.00201 0.938
Seadas—MUMM0.939 19.70%0.00230 0.922
GDPS2.00.978 7.12%0.00087 0.921
GDPS1.30.962 9.38%0.00126 0.967
531Seadas—Default0.985 16.24%0.00197 0.938
Seadas—MUMM0.974 15.46%0.00220 0.922
GDPS2.00.986 5.29%0.00091 0.921
GDPS1.30.980 7.24%0.00113 0.967
555Seadas—Default0.988 13.91%0.00180 0.938
Seadas—MUMM0.979 13.32%0.00204 0.922
GDPS2.00.989 5.65%0.00096 0.921
GDPS1.30.982 7.68%0.00112 0.967
660Seadas—Default0.982 30.57%0.00129 0.938
Seadas—MUMM0.967 29.01%0.00158 0.922
GDPS2.00.983 10.04%0.00063 0.921
GDPS1.30.977 24.88%0.00105 0.967
Table 7. The parameter values of the four GOCI atmospheric-correction algorithms in Rrs(469), Rrs(555), and Rrs-chla compared with MODIS data. Rrs(469) was generated by linear interpolation model from the existing GOCI bands. Rrs-chla = Rrs(469)/Rrs(555).
Table 7. The parameter values of the four GOCI atmospheric-correction algorithms in Rrs(469), Rrs(555), and Rrs-chla compared with MODIS data. Rrs(469) was generated by linear interpolation model from the existing GOCI bands. Rrs-chla = Rrs(469)/Rrs(555).
RrsAtmospheric
Correction Algorithms
εRMSEQA Score
Rrs(469)Seadas—Default23.91%0.00212 0.938
Seadas—MUMM22.62%0.00248 0.922
GDPS2.09.42%0.00102 0.921
GDPS1.311.98%0.00153 0.967
Rrs(555)Seadas—Default13.91%0.00180 0.938
Seadas—MUMM13.32%0.00204 0.922
GDPS2.05.65%0.00096 0.921
GDPS1.37.68%0.00112 0.967
Rrs-chlaSeadas—Default10.65%0.08833 0.938
Seadas—MUMM10.79%0.08751 0.922
GDPS2.010.81%0.09669 0.921
GDPS1.39.70%0.08674 0.967
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Liu, X.; Yang, Q.; Wang, Y.; Zhang, Y. Evaluation of GOCI Remote Sensing Reflectance Spectral Quality Based on a Quality Assurance Score System in the Bohai Sea. Remote Sens. 2022, 14, 1075. https://doi.org/10.3390/rs14051075

AMA Style

Liu X, Yang Q, Wang Y, Zhang Y. Evaluation of GOCI Remote Sensing Reflectance Spectral Quality Based on a Quality Assurance Score System in the Bohai Sea. Remote Sensing. 2022; 14(5):1075. https://doi.org/10.3390/rs14051075

Chicago/Turabian Style

Liu, Xiaoyan, Qian Yang, Yunhua Wang, and Yu Zhang. 2022. "Evaluation of GOCI Remote Sensing Reflectance Spectral Quality Based on a Quality Assurance Score System in the Bohai Sea" Remote Sensing 14, no. 5: 1075. https://doi.org/10.3390/rs14051075

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