Correction of Stray-Light-Driven Interslot Radiometric Discrepancy (ISRD) Present in Radiometric Products of Geostationary Ocean Color Imager (GOCI)

Kim, Wonkook; Ahn, Jae-Seong; Park, Young-Je

doi:10.1109/tgrs.2015.2422831

Cited by 15 publications

(21 citation statements)

References 16 publications

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“…It primarily has an impact on the AE [29]. To minimize its influence, an image-based method called the minimum noise fraction transform has been proposed for the partial removal of the slot border [71]. In the future, this method will be applied in GDPS Software.…”

Section: Discussionmentioning

confidence: 99%

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: 99%

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 this region, the range of SZA during the acquisition time varies between 0 • and 90 • through the year and from approximately 29 • to 37 • for the sensor (viewing) zenith angle. The boundaries of the GCWS region were selected to be inside the slot located in the lower right corner of the L1B image (slot number 13 in Reference [37]) to avoid the Stray-Light-Driven Interslot Radiometric Discrepancy (ISRD) at the near-boundary of the interslot areas [37,38]. It has been estimated that the stray-light-driven radiometric anomalies could reach up to 20% in some bands and therefore, the potentially affected areas were excluded in this study.…”

Section: Area Of Studymentioning

confidence: 99%

Uncertainties in the Geostationary Ocean Color Imager (GOCI) Remote Sensing Reflectance for Assessing Diurnal Variability of Biogeochemical Processes

et al. 2019

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Short-term (sub-diurnal) biological and biogeochemical processes cannot be fully captured by the current suite of polar-orbiting satellite ocean color sensors, as their temporal resolution is limited to potentially one clear image per day. Geostationary sensors, such as the Geostationary Ocean Color Imager (GOCI) from the Republic of Korea, allow the study of these short-term processes because their orbit permit the collection of multiple images throughout each day for any area within the sensor’s field of regard. Assessing the capability to detect sub-diurnal changes in in-water properties caused by physical and biogeochemical processes characteristic of open ocean and coastal ocean ecosystems, however, requires an understanding of the uncertainties introduced by the instrument and/or geophysical retrieval algorithms. This work presents a study of the uncertainties during the daytime period for an ocean region with characteristically low-productivity with the assumption that only small and undetectable changes occur in the in-water properties due to biogeochemical processes during the daytime period. The complete GOCI mission data were processed using NASA’s SeaDAS/l2gen package. The assumption of homogeneity of the study region was tested using three-day sequences and diurnal statistics. This assumption was found to hold based on the minimal diurnal and day-to-day variability in GOCI data products. Relative differences with respect to the midday value were calculated for each hourly observation of the day in order to investigate what time of the day the variability is greater. Also, the influence of the solar zenith angle in the retrieval of remote sensing reflectances and derived products was examined. Finally, we determined that the uncertainties in water-leaving “remote-sensing” reflectance (Rrs) for the 412, 443, 490, 555, 660 and 680 nm bands on GOCI are 8.05 × 10−4, 5.49 × 10−4, 4.48 × 10−4, 2.51 × 10−4, 8.83 × 10−5, and 1.36 × 10−4 sr−1, respectively, and 1.09 × 10−2 mg m−3 for the chlorophyll-a concentration (Chl-a), 2.09 × 10−3 m−1 for the absorption coefficient of chromophoric dissolved organic matter at 412 nm (ag (412)), and 3.7 mg m−3 for particulate organic carbon (POC). These Rrs values can be considered the threshold values for detectable changes of the in-water properties due to biological, physical or biogeochemical processes from GOCI.

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“…Extremely high SPM values occurred at the lower portion of the 12th slot. This is inferred to be related to the effect of instrumental correction problem such as the previously reported stray light issues induced by inner slot response (Oh et al 2011(Oh et al , 2013Kim et al 2015), which will be discussed in later section. Thus, we termed this type of speckle a 'slot-related speckle.…”

Section: Slot-related Specklementioning

confidence: 76%

“…Similar to previous ocean colour satellite images, numerous speckles are shown in GOCI images. One of the significant errors originates from the stray light effect of the GOCI sensor, which should be considered for a proper estimation of optical properties to enhance the data quality for further scientific analysis (Oh et al 2011(Oh et al , 2013Kim et al 2015). The SPM is one of the ocean colour variables with higher possibility of speckle than any other parameters as inferred from general formula of the estimation of SPM, unlike chlorophyll-a concentration and CDOM.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal characteristics and removal methodology of suspended particulate matter speckles from Geostationary Ocean Color Imager data

Lee

Park

Moon

et al. 2018

International Journal of Remote Sensing

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Reviewing six years of Geostationary Ocean Color Imager (GOCI) suspended particulate matter (SPM) concentration images from 2011 to 2016 revealed unexpected and some enormously high or low values. These speckles are randomly scattered throughout the entire study area or congregated at a certain part, which has strongly restricted the scientific applications of GOCI data thus far. They can be classified into four types: isolated, near-cloud, patchtype, and slot-related speckles, based on spatial distribution and potential causes. These types are investigated. The speckles are induced by a moving-cloud during a complete observation of 8 bands for each slot of GOCI, partly by an incomplete cloud masking procedure near cloud edges, by relatively low reflectance of pixels corresponding to a cloud shadow, by imperfect atmospheric corrections related to water vapor after cloud passage, or by sensor-related troubles related to the effects of stray light. Spatial and temporal variabilities of the identified speckles are investigated and used to develop a methodology for their removal. The SPM concentration values of the error-free pixels, passing through the post-processing of the speckle removal procedure, are compared to those previously with speckles. As a result, the enormously large values of SPM, occupying 6.06% of the pixel numbers, are all eliminated. Typical seasonality of SPM, unrevealed using the speckled images, is clearly presented through the removal procedure. This study addresses the importance of preprocessing for speckle error in SPM concentrations from GOCI data and implies more reliable SPM data without speckles can be used in scientific application research.

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Correction of Stray-Light-Driven Interslot Radiometric Discrepancy (ISRD) Present in Radiometric Products of Geostationary Ocean Color Imager (GOCI)

Cited by 15 publications

References 16 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

Uncertainties in the Geostationary Ocean Color Imager (GOCI) Remote Sensing Reflectance for Assessing Diurnal Variability of Biogeochemical Processes

Spatial and temporal characteristics and removal methodology of suspended particulate matter speckles from Geostationary Ocean Color Imager data

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