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

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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Assessing Diurnal Variability of Biogeochemical Processes Using the Geostationary Ocean Color Imager (GOCI)

10.20944/preprints201811.0042.v1 ◽

2018 ◽

Author(s):

Javier Concha ◽

Antonio Mannino ◽

Bryan Franz ◽

Wonkook Kim

Keyword(s):

Remote Sensing ◽

Ocean Color ◽

Biogeochemical Processes ◽

Short Term ◽

Diurnal Variability ◽

Water Properties ◽

Water Region ◽

The Republic ◽

Polar Orbiting Satellite ◽

Hourly Observation

Short-term (hours) 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 geostationary orbits permit the collection of multiple images throughout each day. To assess the capability to detect changes in water properties caused by these processes, however, requires an understaning of the uncertainties introduced by the instrument and/or geophysical retrieval algorithms. This work presents a study of the variability during the day over a water region of low-productivity with the assumption that only small changes in the water properties occur during the day over the area of study. The complete GOCI mission data were processed using the SeaDAS/l2gen package. Filtering criteria were applied to assure the quality of the data. Relative differences with respect to the midday value were calculated for each hourly observation of the day. Also, the influence of the solar zenith angle in the retrieval of remote sensing reflectances and derived products was analyzed. We determined that the uncertainties in water-leaving “remote-sensing” reflectance ($R_\text{rs}$) for the 412, 443, 490, 555, 660 and 680 nm bands on GOCI are 8.05$\times10^{-4}$, 5.49$\times10^{-4}$, 4.48$\times10^{-4}$, 2.51$\times10^{-4}$, 8.83$\times10^{-5}$, and 1.36$\times10^{-4}$ sr$^{-1}$, respectively, and 1.09$\times10^{-2}$ mg m$^{-3}$ for the chlorophyll-a concentration (Chl-{\it a}), 2.09$\times10^{-3}$ m$^{-1}$ for the absorption coefficient of chromophoric dissolved organic matter at 412 nm ($a_{\text{g}}(412)$), and 3.7 mg m$^{-3}$ for particulate organic carbon (POC). We consider these to be the floor values for detectable changes in the water properties due to biological, physical or chemical processes.

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Atmospheric Trace Gas (NO2 and O3) Variability in South Korean Coastal Waters, and Implications for Remote Sensing of Coastal Ocean Color Dynamics

Remote Sensing ◽

10.3390/rs10101587 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1587 ◽

Cited By ~ 5

Author(s):

Maria Tzortziou ◽

Owen Parker ◽

Brian Lamb ◽

Jay Herman ◽

Lok Lamsal ◽

...

Keyword(s):

Remote Sensing ◽

Satellite Imagery ◽

Coastal Waters ◽

High Frequency ◽

Ocean Color ◽

Atmospheric Correction ◽

Coastal Ocean ◽

Short Term ◽

Diurnal Variability ◽

Air Mass

Coastal environments are highly dynamic, and are characterized by short-term, local-scale variability in atmospheric and oceanic processes. Yet, high-frequency measurements of atmospheric composition, and particularly nitrogen dioxide (NO2) and ozone (O3) dynamics, are scarce over the ocean, introducing uncertainties in satellite retrievals of coastal ocean biogeochemistry and ecology. Combining measurements from different platforms, the Korea-US Ocean Color and Air Quality field campaign provided a unique opportunity to capture, for the first time, the strong spatial dynamics and diurnal variability in total column (TC) NO2 and O3 over the coastal waters of South Korea. Measurements were conducted using a shipboard Pandora Spectrometer Instrument specifically designed to collect accurate, high-frequency observations from a research vessel, and were combined with ground-based observations at coastal land sites, synoptic satellite imagery, and air-mass trajectory simulations to assess source contributions to atmospheric pollution over the coastal ocean. TCO3 showed only small (<20%) variability that was driven primarily by larger-scale meteorological processes captured successfully in the relatively coarse satellite imagery from Aura-OMI. In contrast, TCNO2 over the ocean varied by more than an order of magnitude (0.07–0.92 DU), mostly affected by urban emissions and highly dynamic air mass transport pathways. Diurnal patterns varied widely across the ocean domain, with TCNO2 in the coastal area of Geoje and offshore Seoul varying by more than 0.6 DU and 0.4 DU, respectively, over a period of less than 3 h. On a polar orbit, Aura-OMI is not capable of detecting these short-term changes in TCNO2. If unaccounted for in atmospheric correction retrievals of ocean color, the observed variability in TCNO2 would be misinterpreted as a change in ocean remote sensing reflectance, Rrs, by more than 80% and 40% at 412 and 443 nm, respectively, introducing a significant false variability in retrievals of coastal ocean ecological processes from space.

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Spectral interdependence of remote-sensing reflectance and its implications on the design of ocean color satellite sensors

Applied Optics ◽

10.1364/ao.53.003301 ◽

2014 ◽

Vol 53 (15) ◽

pp. 3301 ◽

Cited By ~ 26

Author(s):

Zhongping Lee ◽

Shaoling Shang ◽

Chuanmin Hu ◽

Giuseppe Zibordi

Keyword(s):

Remote Sensing ◽

Ocean Color ◽

Remote Sensing Reflectance ◽

Satellite Sensors

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Spatio-Temporal Variability in Bio-Optical Properties of the Southern Caspian Sea: A Historic Analysis of Ocean Color Data

Remote Sensing ◽

10.3390/rs12233975 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3975

Author(s):

Bonyad Ahmadi ◽

Mehdi Gholamalifard ◽

Tiit Kutser ◽

Stefano Vignudelli ◽

Andrey Kostianoy

Keyword(s):

Remote Sensing ◽

Optical Properties ◽

Attenuation Coefficient ◽

Temporal Variability ◽

Caspian Sea ◽

Ocean Color ◽

Time Lags ◽

Chl A ◽

Remote Sensing Reflectance ◽

Southern Caspian Sea

Currently, satellite ocean color imageries play an important role in monitoring of water properties in various oceanic, coastal, and inland ecosystems. Although there is a long-time and global archive of such valuable data, no study has comprehensively used these data to assess the changes in the Caspian Sea. Hence, this study assessed the variability of bio-optical properties of the upper-water column in the Southern Caspian Sea (SCS) using the archive of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). The images acquired from SeaWiFS (January 1998 to December 2002) and MODIS Aqua (January 2003 to December 2015) satellites were used to investigate the spatial–temporal variability of bio-optical properties including Chlorophyll-a (Chl-a), attenuation coefficient, and remote sensing reflectance, and environmental parameters such as sea surface temperature (SST), wind stress and the El Nino-southern oscillation (ENSO) phenomena at different time lags in the study area. The trend analysis demonstrated an overall increase of 0.3358 mg m−3 in the Chl-a concentration during 1998–2015 (annual increase rate of 0.018 mg m−3 year−1) and four algal blooms and in turn an abnormal increase in Chl-a concentration were occurred in August 2001, September 2005, 2009, and August 2010. The linear model revealed that Chl-a in the northern and middle part of the study area had been influenced by the attenuation coefficient after a one-month lag time. The analysis revealed a sharp decline in Chl-a concentration during 2011–2015 and showed a high correlation with the turbidity and attenuation coefficient in the southern region, while Kd_490nm and remote sensing reflectance did a low one. Generally, Chl-a concentration exhibited a positive correlation with the attenuation coefficient (r = 0.63) and with remote sensing reflectance at the 555 nm (r = 0.111). This study can be used as the basis for predictive modeling to evaluate the changes of water quality and bio-optical indices in the Southern Caspian Sea (SCS).

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Empirical Relationships between Remote-Sensing Reflectance and Selected Inherent Optical Properties in Nordic Sea Surface Waters for the MODIS and OLCI Ocean Colour Sensors

Remote Sensing ◽

10.3390/rs12172774 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2774

Author(s):

Marta Konik ◽

Piotr Kowalczuk ◽

Monika Zabłocka ◽

Anna Makarewicz ◽

Justyna Meler ◽

...

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Field Work ◽

The Arctic ◽

Significant Shift ◽

Form Complex ◽

Water Properties ◽

Remote Sensing Reflectance ◽

Phytoplankton Communities

The Nordic Seas and the Fram Strait regions are a melting pot of a number of water masses characterized by distinct optical water properties. The warm Atlantic Waters transported from the south and the Arctic Waters from the north, combined with the melt waters contributing to the Polar Waters, mediate the dynamic changes of the year-to-year large-scale circulation patterns in the area, which often form complex frontal zones. In the last decade, moreover, a significant shift in phytoplankton phenology in the area has been observed, with a certain northward expansion of temperate phytoplankton communities into the Arctic Ocean which could lead to a deterioration in the performance of remote sensing algorithms. In this research, we exploited the capability of the satellite sensors to monitor those inter-annual changes at basin scales. We propose locally adjusted algorithms for retrieving chlorophyll a concentrations Chla, absorption by particles ap at 443 and 670 nm, and total absorption atot at 443 and 670 nm developed on the basis of intensive field work conducted in 2013–2015. Measured in situ hyper spectral remote sensing reflectance has been used to reconstruct the MODIS and OLCI spectral channels for which the proposed algorithms have been adapted. We obtained MNB ≤ 0.5% for ap(670) and ≤3% for atot(670) and Chla. RMS was ≤30% for most of the retrieved optical water properties except ap(443) and Chla. The mean monthly mosaics of ap(443) computed on the basis of the proposed algorithm were used for reconstructing the spatial and temporal changes of the phytoplankton biomass in 2013–2015. The results corresponded very well with in situ measurements.

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Diurnal Variability of Turbidity Fronts Observed by Geostationary Satellite Ocean Color Remote Sensing

Remote Sensing ◽

10.3390/rs8020147 ◽

2016 ◽

Vol 8 (2) ◽

pp. 147 ◽

Cited By ~ 16

Author(s):

Zifeng Hu ◽

Delu Pan ◽

Xianqiang He ◽

Yan Bai

Keyword(s):

Remote Sensing ◽

Ocean Color ◽

Geostationary Satellite ◽

Ocean Color Remote Sensing ◽

Diurnal Variability ◽

Satellite Ocean Color

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Uncertainty estimates of remote sensing reflectance derived from comparison of ocean color satellite data sets

Remote Sensing of Environment ◽

10.1016/j.rse.2016.02.014 ◽

2016 ◽

Vol 177 ◽

pp. 107-124 ◽

Cited By ~ 29

Author(s):

F. Mélin ◽

G. Sclep ◽

T. Jackson ◽

S. Sathyendranath

Keyword(s):

Remote Sensing ◽

Satellite Data ◽

Ocean Color ◽

Data Sets ◽

Remote Sensing Reflectance ◽

Uncertainty Estimates

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Ocean Color Analytical Model Explicitly Dependent on the Volume Scattering Function

Applied Sciences ◽

10.3390/app8122684 ◽

2018 ◽

Vol 8 (12) ◽

pp. 2684 ◽

Cited By ~ 7

Author(s):

Michael Twardowski ◽

Alberto Tonizzo

Keyword(s):

Remote Sensing ◽

Analytical Model ◽

Phase Function ◽

Ocean Color ◽

Atmospheric Effects ◽

Data Sets ◽

Scattering Function ◽

Volume Scattering ◽

Remote Sensing Reflectance ◽

Function Shape

An analytical radiative transfer (RT) model for remote sensing reflectance that includes the bidirectional reflectance distribution function (BRDF) is described. The model, called ZTT (Zaneveld-Twardowski-Tonizzo), is based on the restatement of the RT equation by Zaneveld (1995) in terms of light field shape factors. Besides remote sensing geometry considerations (solar zenith angle, viewing angle, and relative azimuth), the inputs are Inherent Optical Properties (IOPs) absorption a and backscattering bb coefficients, the shape of the particulate volume scattering function (VSF) in the backward direction, and the particulate backscattering ratio. Model performance (absolute error) is equivalent to full RT simulations for available high quality validation data sets, indicating almost all residual errors are inherent to the data sets themselves, i.e., from the measurements of IOPs and radiometry used as model input and in match up assessments, respectively. Best performance was observed when a constant backward phase function shape based on the findings of Sullivan and Twardowski (2009) was assumed in the model. Critically, using a constant phase function in the backward direction eliminates a key unknown, providing a path toward inversion to solve for a and bb. Performance degraded when using other phase function shapes. With available data sets, the model shows stronger performance than current state-of-the-art look-up table (LUT) based BRDF models used to normalize reflectance data, formulated on simpler first order RT approximations between rrs and bb/a or bb/(a + bb) (Morel et al., 2002; Lee et al., 2011). Stronger performance of ZTT relative to LUT-based models is attributed to using a more representative phase function shape, as well as the additional degrees of freedom achieved with several physically meaningful terms in the model. Since the model is fully described with analytical expressions, errors for terms can be individually assessed, and refinements can be readily made without carrying out the gamut of full RT computations required for LUT-based models. The ZTT model is invertible to solve for a and bb from remote sensing reflectance, and inversion approaches are being pursued in ongoing work. The focus here is with development and testing of the in-water forward model, but current ocean color remote sensing approaches to cope with an air-sea interface and atmospheric effects would appear to be transferable. In summary, this new analytical model shows good potential for future ocean color inversion with low bias, well-constrained uncertainties (including the VSF), and explicit terms that can be readily tuned. Emphasis is put on application to the future NASA Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission.

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