Comparative Analysis of GOCI Ocean Color Products

Amin, Ruhul; Lewis, Mark; Lawson, Adam; Gould, Richard W.; Martinolich, Paul; Li, Rongrong; Ladner, Sherwin; Gallegos, Sonia

doi:10.3390/s151025703

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…For NASA, it is applied as a one-time gain without any in-depth or long-term evaluation [72], whereas the default VC gains processed by SeaDASv7.5 are 0.9726, 0.9520, 0.9258, 0.8974, 0.9007, 0.8719, 0.9430, and 1.0 for each GOCI band [72]. In the present GDPSv2.0, the VC gains for 412-865 nm are 1.0118, 0.9954, 0.9715, 0.93431, 0.9596, 0.9669, 0.96125, and 1.0 [73,74]. The influence of VC on AC between KIOST/KOSC and NASA will be evaluated in future research.…”

Section: Discussionmentioning

confidence: 65%

Evaluation of Four Atmospheric Correction Algorithms for GOCI Images over the Yellow Sea

Huang¹,

Zhu²,

Han³

et al. 2019

Remote Sensing

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Atmospheric correction (AC) for coastal waters is an important issue in ocean color remote sensing. AC performance is fundamental in retrieving reliable water-leaving radiances and then bio-optical parameters. Unlike polar-orbiting satellites, geostationary ocean color sensors allow high-frequency (15–60 min) monitoring of ocean color over the same area. The first geostationary ocean color sensor, i.e., the Geostationary Ocean Color Imager (GOCI), was launched in 2010. Using GOCI data acquired over the Yellow Sea in summer 2017 at three principal overpass times (02:16, 03:16, 04:16 UTC) with ±1 and ±3 h match-up times, this study compared four GOCI AC algorithms: (1) the standard near infrared (NIR) algorithm of NASA (NASA-STD), (2) the Korea Ocean Satellite Center (KOSC) standard algorithm for GOCI (KOSC-STD), (3) the diffuse attenuation coefficient at 490 nm Kd (490)-based NIR correction algorithm (Kd-based), and (4) the Management Unit of the North Sea Mathematical Models (MUMM). The GOCI-estimated remote sensing reflectance (Rrs), aerosol parameters [aerosol optical thickness (AOT), Angström Exponent (AE)], and chlorophyll-a (Chla) were validated using in situ data. For Rrs, AOT, AE, and Chla, GOCI-retrieved results performed well within the ±1 h temporal window, but the number of match-ups was extended within the ±3 h match-up window. For ±3 h GOCI-derived Rrs, all algorithms had an absolute percentage difference (APD) at 490 and 555 nm of <40%, while other bands showed larger differences (APD > 60%). Compared with in situ values, the APD of the Rrs(490)/Rrs(555) band ratio was <20% for all ACs. For AOT and AE, the APD was >40% and >200%, respectively. Of the four algorithms, the KOSC-STD algorithm demonstrated satisfactory performance in deriving Rrs for the region of interest (Rrs APD: 22.23%–73.95%) in the visible bands. The Kd-based algorithm worked well obtaining Ocean Color 3 GOCI Chla because Rrs(443) is more accurate than the KOSC-STD. The poorest Rrs retrievals were achieved using the NASA-STD and the MUMM algorithms. Statistical analysis indicated that all methods had optimal performance at 04:16 UTC.

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Section: Discussionmentioning

confidence: 65%

Evaluation of Four Atmospheric Correction Algorithms for GOCI Images over the Yellow Sea

Huang¹,

Zhu²,

Han³

et al. 2019

Remote Sensing

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“…For biogeochemical variables, Amin et al (2015) assessed GOES satellite-based ocean color products using in situ networks (Amin et al, 2015). Land et al (2018) used a database of satellite in situ matchups to generate a statistical model of satellite uncertainty as a function of its contributing variables for ocean color chlorophyll-a and showed that most errors are correctable biases (Land et al, 2018).…”

Section: Blended Satellite and In Situ Products And Servicesmentioning

confidence: 99%

A Global Ocean Observing System (GOOS), Delivered Through Enhanced Collaboration Across Regions, Communities, and New Technologies

et al. 2019

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“…With the development of computer science, Internet of Things (IoT), communication, signal processing and sensor technologies, a large number of marine monitoring systems based on the source information fusion have been established, forming many integrated monitoring networks that can provide useful information for ship navigation from space, sky and ground Amin et al (2015); Moltmann et al (2019). However, for weak targets with low observability on the sea surface, target detection becomes difficult, even though it can be supported by a marine monitoring system.…”

Section: Introductionmentioning

confidence: 99%

Marine radar monitoring IoT system and case study of target detection based on PPI images

Zhang

Guo

et al. 2023

Expert Systems

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To ensure the ship navigation safety, it is necessary to detect the targets in the background of sea clutter around the ship. The most commonly used target detection equipments for ship navigation are marine radar and automatic identification system (AIS) device. However, marine radar echoes are often mixed with multiple clutter, and weak targets are not always equipped with AIS device, neither provides AIS information with low accuracy, leading to difficulty with target detection. Moreover, existing marine monitoring systems are accustomed to using the traditional information fusion methods for target detection, and the accuracy of identifying the weak targets is relatively low. To make full use of radar echo and AIS information and improve the accuracy of target detection, a Marine radar monitoring Internet of Things system is proposed, in which marine radars work in both scanning and staring modes. By adopting image segmentation and deep learning methods, the proposed design can enable accurate perception of weak target detection based on plan‐position indicator (PPI) images. A case study based on the selected X‐band radar datasets shows that the proposed design can achieve high target identification accuracy.

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Comparative Analysis of GOCI Ocean Color Products

Cited by 7 publications

References 21 publications

Evaluation of Four Atmospheric Correction Algorithms for GOCI Images over the Yellow Sea

Evaluation of Four Atmospheric Correction Algorithms for GOCI Images over the Yellow Sea

A Global Ocean Observing System (GOOS), Delivered Through Enhanced Collaboration Across Regions, Communities, and New Technologies

Marine radar monitoring IoT system and case study of target detection based on PPI images

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