Search Results (13)

As a key component of inherent optical properties (IOPs) in ocean color remote sensing, phytoplankton absorption coefficient ($a_{phy}$), especially in hyperspectral, greatly enhances our understanding of phytoplankton community composition (PCC). The recent launches of NASA’s hyperspectral missions, such as EMIT and PACE, have generated an urgent need for hyperspectral algorithms for studying phytoplankton. Retrieving $a_{phy}$ from ocean color remote sensing in coastal waters has been extremely challenging due to complex optical properties. Traditional methods often fail under these circumstances, while improved machine-learning approaches are hindered by data scarcity, heterogeneity, and noise from data collection. In response, this study introduces a novel machine learning framework for hyperspectral retrievals of $a_{phy}$ based on the mixture-of-experts (MOEs), named PhA-MOE. Various preprocessing methods for hyperspectral training data are explored, with the combination of robust and logarithmic scalers identified as optimal. The proposed PhA-MOE for $a_{phy}$ prediction is tailored to both past and current hyperspectral missions, including EMIT and PACE. Extensive experiments reveal the importance of data preprocessing and improved performance of PhA-MOE in estimating $a_{phy}$ as well as in handling data heterogeneity. Notably, this study marks the first application of a machine learning–based MOE model to real PACE-OCI hyperspectral imagery, validated using match-up field data. This application enables the exploration of spatiotemporal variations in $a_{phy}$ within an optically complex estuarine environment. Full article

The multi-band atmospheric correction algorithms, now referred to as remote sensing reflectance (R_rs) algorithms, have been implemented on a NASA computing facility for global remote sensing of ocean color and atmospheric aerosol parameters from data acquired with several satellite instruments, including the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi spacecraft platform. These algorithms are based on the 2-band version of the SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) algorithm. The bands centered near 0.75 and 0.865 μm are used for atmospheric corrections. In order to obtain high-quality R_rs values over Case 1 waters (deep clear ocean waters), strict masking criteria are implemented inside these algorithms to mask out thin clouds and very turbid water pixels. As a result, R_rs values are often not retrieved over bright Case 2 waters. Through our analysis of VIIRS data, we have found that spatial features of bright Case 2 waters are observed in VIIRS visible band images contaminated by thin cirrus clouds. In this article, we describe methods of combining cirrus and aerosol corrections to improve spatial coverage in R_rs retrievals over Case 2 waters. One method is to remove cirrus cloud effects using our previously developed operational VIIRS cirrus reflectance algorithm and then to perform atmospheric corrections with our updated version of the spectrum-matching algorithm, which uses shortwave IR (SWIR) bands above 1 μm for retrieving atmospheric aerosol parameters and extrapolates the aerosol parameters to the visible region to retrieve water-leaving reflectances of VIIRS visible bands. Another method is to remove the cirrus effect first and then make empirical atmospheric and sun glint corrections for water-leaving reflectance retrievals. The two methods produce comparable retrieved results, but the second method is about 20 times faster than the spectrum-matching method. We compare our retrieved results with those obtained from the NASA VIIRS R_rs algorithm. We will show that the assumption of zero water-leaving reflectance for the VIIRS band centered at 0.75 μm (M6) over Case 2 waters with the NASA R_rs algorithm can sometimes result in slight underestimates of water-leaving reflectances of visible bands over Case 2 waters, where the M6 band water-leaving reflectances are actually not equal to zero. We will also show conclusively that the assumption of thin cirrus clouds as ‘white’ aerosols during atmospheric correction processes results in overestimates of aerosol optical thicknesses and underestimates of aerosol Ångström coefficients. Full article

Remote sensing data play a crucial role in capturing and evaluating eutrophication, providing a comprehensive view of spatial and temporal variations in water quality parameters. Chlorophyll-a concentration time series analysis aids in understanding the current trophic state of coastal waters and tracking changes over time, enabling the evaluation of water bodies’ trophic status. This research presents a novel and replicable methodology able to derive accurate phenological patterns using remote sensing data. The methodology proposed uses the two-decade MODIS-Aqua surface reflectance dataset, analyzing data from 30-point stations and calculating chlorophyll-a concentrations from NASA’s Ocean Color algorithm. Then, a correction process is implemented through a robust, simple statistical analysis by applying LOESS smoothing to detect and remove outliers from the extensive dataset. Different scenarios are reviewed and compared with field data to calibrate the proposed methodology accurately. The results demonstrate the methodology’s capacity to produce consistent chlorophyll-a time series and to present phenological patterns that can effectively identify key indicators and trends, resulting in valuable insights into the coastal body’s trophic state. The case study of the Ambracian Gulf is characterized as hypertrophic since algal bloom during August reaches up to 5 mg/m³, while the replicate case study of Aitoliko shows algal bloom reaching up to 2.5 mg/m³. Finally, the proposed methodology successfully identifies the positive chlorophyll-a climate tendencies of the two selected Greek water bodies. This study highlights the value of integrating statistical methods with remote sensing data for accurate, long-term monitoring of water quality in aquatic ecosystems. Full article

The current operational multi-band atmospheric correction algorithms implemented by NASA and NOAA for global remote sensing of ocean color from VIIRS (Visible Infrared Imaging Radiometer Suite) data are mostly based on the 2-band version of the SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) algorithm. These algorithms generally use two NIR bands, one centered near 0.75 μm and the other near 0.865 μm, and a band ratio method for deriving aerosol information. The algorithms work quite well over open ocean waters. However, water leaving reflectances over turbid coastal waters are frequently not derived. We describe here a spectrum-matching algorithm using shortwave IR (SWIR) bands above 1 μm for retrieving water leaving reflectances in the visible from VIIRS data. The SWIR bands centered near 1.24, 1.61, and 2.25 μm are used in a spectrum-matching process to obtain spectral aerosol information, which is subsequently extrapolated to the visible region for the derivation of water leaving reflectances of visible bands. We present retrieval results for four VIIRS scenes acquired over turbid waters. We demonstrate that the spatial coverages of our retrieving results can be improved significantly in comparison with those retrieved with the current NOAA operational algorithm. If our SWIR algorithm is implemented for operational data processing, the algorithm can potentially be complimentary to current NASA and NOAA VIIRS algorithms over turbid waters to increase spatial coverages. Full article

The Sentinel-2A and Sentinel-2B satellites, with on-board Multi-Spectral Instrument (MSI), and launched on 23 June 2015 and 7 March 2017, respectively, are very useful tools for studying ocean color, even if they were designed for land and vegetation applications. However, the use of these satellites requires a process called “atmospheric correction”. This process aims to remove the contribution of the atmosphere from the total top of atmosphere reflectance measured by the remote sensors. For the purpose of assessing this processing, seven atmospheric correction algorithms have been compared over two French coastal regions (English Channel and French Guiana): Image correction for atmospheric effects (iCOR), Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (C2RCC), Sentinel 2 Correction (Sen2Cor), Polynomial-based algorithm applied to MERIS (Polymer), the standard NASA atmospheric correction (NASA-AC) and the Ocean Color Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART). The satellite-estimated remote-sensing reflectances were spatially and temporally matched with in situ measurements collected by an ASD FieldSpec4 spectrophotometer. Results, based on 28 potential individual match-ups, showed that the best performance processor is OC-SMART with the highest values for the total score S_tot (16.89) and for the coefficient of correlation R² (ranging from 0.69 at 443 nm to 0.92 at 665 nm). iCOR and Sen2Cor show the less accurate performances with total score S_tot values of 2.01 and 7.70, respectively. Since the size of the in situ observation platform can be significant compared to the pixel resolution of MSI onboard Sentinel-2, it can create bias in the pixel extraction process. Thus, to study this impact, we used different methods of pixel extraction. However, there are no significant changes in results; some future research may be necessary. Full article

In the application of ocean color remote sensing, remote sensing reflectance spectral (R_rs(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 R_rs(λ) data. Based on the QA (quality assurance) score spectral quality evaluation system, the quality of R_rs(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 R_rs(λ) data are in good quality with a score ≥4/6 for the GDPS1.3 algorithm. The probability of R_rs(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 R_rs(λ) were compared with simultaneous GOCI R_rs(λ), and they demonstrate that the QA score system is useful in evaluating the spectral shape of R_rs(λ). The comparison results indicate that higher QA scores have higher accuracy of the R_rs 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 R_rs(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. Full article

The coverage of valid pixels of remote-sensing reflectance (Rrs) from ocean color imagery is relatively low due to the presence of clouds. In fact, it is also related to the presence of high aerosol optical depth (AOD) and other factors. In order to increase the valid coverage of satellite-retrieved products, a layer removal scheme for atmospheric correction (LRSAC) has been developed to process the ocean color data. The LRSAC used a five-layer structure including atmospheric absorption layer, Rayleigh scattering layer, aerosol scattering layer, sea surface reflection layer, and water-leaving reflectance layer to deal with the relationship of the components of the atmospheric correction. A nonlinear approach was used to solve the multiple reflections of the interface between two adjoining layers and a step-by-step procedure was used to remove effects of each layer. The LRSAC was used to process data from the sea-viewing wide field-of-view sensor (SeaWiFS) and the results were compared with standard products. The average of valid pixels of the global daily Rrs images of the standard products from 1997 to 2010 is only 11.5%, while it reaches up to 30.5% for the LRSAC. This indicates that the LRSAC recovers approximately 1.65 times of invalid pixels as compared with the standard products. Eight-day standard composite images exhibit many large areas with invalid values due to the presence of high AOD, whereas these areas are filled with valid pixels wusing the LRSAC. The ratio image of the mean valid pixel of the LRSAC to that of the standard products indicates that the number of valid pixels of the LRSAC increases with an increase of AOD. The LRSAC can increase the number of valid pixels by more than two times in about 33.8% of ocean areas with high AOD values. The accuracy of Rrs from the LRSAC was validated using the following two in situ datasets: the Marine Optical BuoY (MOBY) and the NASA bio-Optical Marine Algorithm Dataset (NOMAD). Most matchup pairs are distributed around the 1:1 line indicating that the systematic bias of the LRSAC is relatively small. The global mean relative error (MRE) of Rrs is 7.9% and the root mean square error (RMSE) is 0.00099 sr⁻¹ for the MOBY matchups. Similarly, the MRE and RMSE are 2.1% and 0.0025 sr⁻¹ for the NOMAD matchups, respectively. The accuracy of LRSAC was also evaluated by different groups of matchups according to the increase of AOD values, indicating that the errors of Rrs were little affected by the presence of high AOD values. Therefore, the LRSAC can significantly improve the coverage of valid pixels of Rrs with a similar accuracy in the presence of high AOD. Full article

Presently, operational ocean color satellite sensors are designed with a legacy perspective for sampling the open ocean primarily in the visible domain, while high spatial resolution sensors such as Sentinel-2, Sentinel-3, and Landsat8 are increasingly used for observations of coastal and inland water quality. Next-generation satellites such as the NASA Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) and Surface Biology and Geology (SBG) sensors are anticipated to increase spatial and/or spectral resolution. An important consideration is determining the minimum signal-to-noise ratio (SNR) needed to retrieve typical biogeochemical products, such as biomass, in aquatic systems, and whether legacy sensors can be used for algorithm development. Here, we evaluate SNR and remote-sensing reflectance (R_rs) uncertainty for representative bright and dim targets in coastal California, USA. The majority of existing sensors fail to meet proposed criteria. Despite these limitations, uncertainties in retrieved biomass as chlorophyll or normalized difference vegetation index (NDVI) remain well below a proposed threshold of 17.5%, suggesting that existing sensors can be used in coastal systems. Existing commercially available in-water and airborne instrument suites can exceed all proposed thresholds for SNR and R_rs uncertainty, providing a path forward for collection of calibration and validation data for future satellite missions. Full article

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. Full article

The NASA’s Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) satellite has been making multiple observations of the entire sunlit Earth in a given day from the Sun-Earth Largangian L1 point since the summer of 2015. EPIC contains 10 narrow channels in the 317–780 nm solar spectral range. The data acquired with EPIC have already been used in a variety of scientific investigations, including the study of the global ozone levels, aerosol index and aerosol optical depth, UV reflectivity of clouds over land and ocean, cloud height over land and ocean, and vegetation indices. In this article, we report that EPIC data, particularly for the data measured with narrow channels centered near 443, 551, and 680 nm, can also have important applications in remote sensing of ocean color in different geographical regions. We have modified a version of a multi-channel atmospheric correction algorithm for Moderate Resolution Imaging SpectroRadiometer (MODIS) ocean color applications and adapted the algorithm for processing EPIC data. We present three case studies on water leaving reflectance retrievals from EPIC data acquired over a large turbid river, inland lakes, and oceans. We conclude that a future ocean color instrument on board a satellite at the L1 point, which provides continuous view of the full sunlit disk of the Earth, will complement and extend ocean color observations with the low Earth observing polar orbital and geostationary satellite instruments in both the spatial and time domains. Full article

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 (R_rs) 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 (a_g (412)), and 3.7 mg m⁻³ for particulate organic carbon (POC). These R_rs values can be considered the threshold values for detectable changes of the in-water properties due to biological, physical or biogeochemical processes from GOCI. Full article

Ocean color remote sensing significantly contributes to our understanding of phytoplankton distribution and abundance and primary productivity in the Southern Ocean (SO). However, the current SO in situ optical database is still insufficient and unevenly distributed. This limits the ability to produce robust and accurate measurements of satellite-based chlorophyll. Based on data collected on cruises around the Antarctica Peninsula (AP) on January 2014 and 2016, this research intends to enhance our knowledge of SO water and atmospheric optical characteristics and address satellite algorithm deficiency of ocean color products. We collected high resolution in situ water leaving reflectance (±1 nm band resolution), simultaneous in situ chlorophyll-a concentrations and satellite (MODIS and VIIRS) water leaving reflectance. Field samples show that clouds have a great impact on the visible green bands and are difficult to detect because NASA protocols apply the NIR band as a cloud contamination threshold. When compared to global case I water, water around the AP has lower water leaving reflectance and a narrower blue-green band ratio, which explains chlorophyll-a underestimation in high chlorophyll-a regions and overestimation in low chlorophyll-a regions. VIIRS shows higher spatial coverage and detection accuracy than MODIS. After coefficient improvement, VIIRS is able to predict chlorophyll a with 53% accuracy. Full article

Retrievals of ocean surface chlorophyll-a concentration (Chla) by multiple ocean color satellite sensors (SeaWiFS, MODIS-Terra, MODIS-Aqua, MERIS, VIIRS) using standard algorithms were evaluated in the California Current using a large archive of in situ measurements. Over the full range of in situ Chla, all sensors produced a coefficient of determination (R²) between 0.79 and 0.88 and a median absolute percent error (MdAPE) between 21% and 27%. However, at in situ Chla > 1 mg m⁻³, only products from MERIS (both the ESA produced algal_1 and NASA produced chlor_a) maintained reasonable accuracy (R² from 0.74 to 0.52 and MdAPE from 23% to 31%, respectively), while the other sensors had R² below 0.5 and MdAPE higher than 36%. We show that the low accuracy at medium and high Chla is caused by the poor retrieval of remote sensing reflectance. Full article