The satellite observations of suspended particulate matter (SPM) from the Visible Infrared Imaging Radiometer Suite (VIIRS) and ocean currents from satellite altimetry merged product are used to demonstrate the capability to monitor the SPM flux and characterize and quantify the change of the SPM flux between 2018 and 2023 in the northern Gulf of Mexico (GOM). The sediment was transported out of both the northern GOM and Mississippi River Estuary regions with a peak in the spring season. The zonal alongshore SPM flux dominates the SPM flux, while the meridional SPM flux off the northern GOM is insignificant. In fact, the alongshore SPM flux becomes eastward in summer, while it is westward in the other seasons. Significantly, different SPM fluxes in the northern GOM were found in the 2019 flood year and 2018 drought year. In Mississippi River flood in 2019, the westward zonal SPM flux off Mississippi River Estuary region doubled, and the net SPM flux reached ~100 kg$\cdot~{\mathrm {m}}^{-1}\cdot~{\mathrm {s}}^{-1}$. On the contrary, the SPM flux in the spring 2018 was significantly weaker than that in the normal year. With combination of gap-free satellite ocean color and satellite altimetry ocean current observations, the SPM flux computation can also be extended to the other world major river estuarine and coastal regions to study the sediment dynamics and further address the ocean physical, biogeochemical, and geological processes in these regions.

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the NOAA-21 satellite was launched in November 2022 as a new VIIRS adding to the constellation of the Joint Polar Satellite System (JPSS) mission, which includes the Suomi National Polar-orbiting Partnership (SNPP) (October 2011 to present) and NOAA-20 (November 2017 to present). For satellite ocean color (OC) remote sensing, the on-orbit system vicarious calibration (SVC) for deriving sensor spectral gain factors must be carried out. In this article, we document our work to obtain SVC gains for the three VIIRS sensors covering the spectral bands of visible, near-infrared (NIR), and shortwave infrared (SWIR) using a consistent NIR-SWIR SVC approach. Specifically, we derive SVC gains using the in situ normalized water-leaving radiance$nL_{w}$($\lambda $) spectra from the Marine Optical Buoy (MOBY) in the Hawaii ocean region with the updated NOAA Multi-Sensor Level-1 to Level-2 (MSL12) data processing system. For VIIRS moderate (M) resolution and imaging (I) bands of M1–M4, I1, M5–M8, M10, and M11, VIIRS SVC gain sets for SNPP, NOAA-20, and NOAA-21 are (0.9752, 0.9732, 0.9772, 0.9685, 1.0090, 0.9750, 0.9765, 1.0000, 1.0050, 0.9960, and 1.0230), (1.0044, 1.0098, 1.0051, 1.0073, 1.0301, 1.0136, 1.0052, 1.0000, 1.0435, 1.0235, and 1.0330), and (1.0284, 1.0317, 1.0165, 1.0231, 1.0236, 1.0137, 1.0051, 1.0000, 0.8982, 0.8779, and 0.8434), respectively. The SVC gains derived using the NIR and SWIR data processing approaches are highly consistent. For example, SVC gain differences at VIIRS M2 blue band between using the NIR (M6 and M7) and SWIR (M8 and M10) SVC methods are 0.021%, -0.010%, and 0.010% for SNPP, NOAA-20, and NOAA-21, respectively. With the new SVC gain sets, the three VIIRS mission-long OC data can be reprocessed. Results over the MOBY site show that reprocessed VIIRS OC products are accurate and consistent, compared to those from in situ measurements. In addition, we have used Rayleigh-corrected reflectance over the Hawaii clear ocean region in a 16-day period of November 17–December 2, 2023, to demonstrate and verify the effectiveness of the SVC gains for the three VIIRS sensors.

Uncertainties are an integral part of remote sensing data products in order to quantify changes, yet due to patchiness and spatial heterogeneity, it is difficult to use field measurements to estimate uncertainties in the satellite-derived Ulva prolifera (U. prolifera, also called green tides) amounts in the Yellow Sea. This is perhaps why such estimates are missing in nearly all remote sensing literature on U. prolifera mapping. Here, by comparing all available data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra/Aqua satellites and the MultiSpectral Instrument (MSI) on the Sentinel-2A/ 2B satellites for the period of 2015–2022, we evaluate uncertainties in the MODIS-derived U. prolifera amounts. The relative uncertainties are found to decrease with increasing Ulva amounts in individual images, ranging from 58.8% for Ulva areal coverage (after pixel unmixing) of$>$200 km2. Such uncertainties decrease in the monthly composite data products because of the increased number of observations, reducing to 3% in the total Ulva amount during the peak months. Such uncertainty estimates, in relative terms, are expected to serve as a reference when interpreting temporal changes in long-term Ulva estimates derived from satellite data.

Satellite ocean color products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20, and the Ocean and Land Colour Instrument (OLCI) on the Sentinel-3A (S3A) and Sentinel-3B (S3B) have been widely used for surveillance of the ocean environment and research on ocean physical, biological, biogeochemical, and ecological processes. However, either VIIRS or OLCI daily ocean color images are often incomplete in spatial coverage due to cloud cover, contamination of high sun glint, narrow swath width, high sensor-zenith angle, high solar-zenith angle, and/or other unfavorable retrieval conditions. Although merging daily ocean color images from multiple satellite sensors can help reduce the number of invalid pixels, gap-filling methods such as the Data Interpolating Empirical Orthogonal Function (DINEOF) are often used to reconstruct invalid pixels and generate gap-free images. The 9-km spatial resolution global gap-free ocean color data have been routinely produced by the NOAA Ocean Color Team and distributed through NOAA CoastWatch (https://coastwatch.noaa.gov/cw/index.html). In this study, we aim to develop and produce improved spatial resolution gap-free products, including chlorophyll-a (Chl-a) concentration, diffuse attenuation coefficient at the wavelength of 490 nm [Kd(490)], and suspended particulate matter (SPM) concentration for spatial resolutions of 0.5-km, 1-km, and 2-km. Two-sensor (VIIRS-SNPP and VIIRS-NOAA-20), three-sensor (two-sensor + OLCI-S3A), and four-sensor (three-sensor + OLCI-S3B) daily merged global Level-3 ocean color data are created and compared. It is found that by merging data from the two VIIRS sensors, ~38% more valid ocean product data are retrieved compared with a single sensor from either SNPP or NOAA-20. Adding OLCI-S3A to the two-sensor merged data can increase the number of valid pixels by ~12%; and adding OLCI-S3B to the three-sensor merged data can further increase the number of valid pixels by ~8%. The DINEOF method is applied to daily two-sensor, three-sensor, and four-sensor merged data to generate global 2-km resolution gap-free images. Results show that 2-km resolution gap-free images are able to resolve fine ocean features like coastal eddies and filaments, which are not available in the 9-km resolution images. While adding OLCI-S3A data significantly improves the three-sensor derived gap-free images over the two-sensor images, no significant enhancement is found in the four-sensor derived gap-free images by adding OLCI-S3B data. The DINEOF method is also applied to 1-km and 0.5-km resolution four-sensor merged Chl-a,Kd(490), and SPM images in the Gulf of Mexico and U.S. west coast region. It is found that both 0.5-km and 1-km resolution images show more detailed ocean structures and features in coastal regions.

We employ a previously developed statistical method to evaluate the performance of the Sentinel-3 OLCI (Ocean and Land Colour Instrument) global ocean color data relying on the temporal stability of the retrievals. We analyze the normalized water-leaving reflectance ρwN(λ) spectra generated by the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system from the OLCI measurements, as well as EUMETSAT-IPF-OL-2 OLCI reflectance ρwN(λ) spectra. The deviations in ρwN(λ) spectra from temporally and spatially averaged baseline data are statistically evaluated corresponding to various parameters, including the solar-sensor geometry, various ancillary data (i.e., surface wind speed, sea-level atmospheric pressure, water vapor amount, and ozone concentration), and other related parameters. Our results show that, under most conditions, both NOAA-MSL12 and EUMETSAT-IPF-OL-2 data processing systems produce statistically consistent ocean color products in the open ocean with respect to all corresponding parameters analyzed, but with some underestimates of ρwN(λ) spectra by EUMETSAT retrievals in moderate sun glint conditions being the notable exception.

Measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) from 2012 to 2019 are used to derive the normalized water-leaving radiance spectra$nL_{w}(\lambda)$in high-altitude lakes over the Tibetan Plateau (TP) (elevation of ~4 km).$nL_{w}(\lambda)$spectra in various lakes in the TP show a significant water optical property diversity in terms of$nL_{w}(\lambda)$spectral shapes, their magnitudes, and their spatial and temporal variations. In the TP major lakes,$nL_{w}(\lambda)$spectra in Lake Namtso and Siling Lake show enhancements at the blue wavelengths of 410 and 443 nm [$nL_{w}$(410) and$nL_{w}$(443)], and peak at the wavelength of 486 nm [$nL_{w}$(486)]. In Lake Ngangze, the$nL_{w}(\lambda)$spectrum shows the optical feature of typical eutrophic waters. Its$nL_{w}(\lambda)$peaks at the green wavelength of 551 nm [$nL_{w}$(551)] with its value ~2.5 mW cm$^{-2}_{\mathrm {}}\,\,\mu \text{m}^{-1}$sr$^{-1}$. In Dogai Coring Lake, the typical$nL_{w}(\lambda)$spectrum is similar to those of many smaller and medium size lakes in the TP, and$nL_{w}(\lambda)$spectra are featured with the peak at the green band$nL_{w}$(551) with another notable red band$nL_{w}$(671) over ~1.0 mW cm$^{-2} \mu \text{m}^{-1}$sr$^{-1}$, as well as a nonnegligible contribution at the near-infrared (NIR) band$nL_{w}$(745). This implies that the suspended particle matter (SPM) in the lake plays a significant role in water$nL_{w}(\lambda)$spectra in Dogai Coring Lake and other similar lakes in the TP. The diversity of water optical property in the TP lakes reflects the fact of the regional diversification of processes in various TP lakes and poses potential challenges to the development of remote sensing algorithms for water biological and biogeochemical products in the TP lakes.

Since its launch in October 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has provided high quality global ocean color products, which include normalized water-leaving radiance spectra nLw(λ) of six moderate (M) bands (M1-M6) at the wavelengths of 410, 443, 486, 551, 671, and 745 nm with a spatial resolution of 750-m, and one imagery (I) band at a wavelength of 638 nm with a spatial resolution of 375-m. Because the high-resolution I-band measurements are highly correlated spectrally to those of M-band data, it can be used as a guidance to super-resolve the M-band nLw(λ) imagery from 750-to 375-m spatial resolution. Super-resolving images from coarse spatial resolution to finer ones have been a field of very active research in recent years. However, no previous studies have been applied to satellite ocean color remote sensing, in particular, for VIIRS ocean color applications. In this study, we employ the deep convolutional neural network (CNN) technique to glean the high-frequency content from the VIIRS I1 band and transfer to super-resolved M-band ocean color images. The network is trained to super-resolve each of the VIIRS six M-bands nLw(λ) separately. In our results, the super-resolved (375-m) nLw(λ) images are much sharper and show finer spatial structures than the original images. Quantitative evaluations show that biases between the super-resolved and original nLw(λ) images are small for all bands. However, errors in the super-resolved nLw(λ) images are wavelength-dependent. The smallest error is found in the superresolved nLw(551) and nLw(671) images, and error increases as the wavelength decreases from 486 to 410 nm. The results show that the networks have the capability to capture the correlations of the M-band and the I1 band images to super-resolved M-band images.

Nearly all calibration/validation activities for the satellite ocean color missions have focused on data quality to produce data products of the highest quality (i.e., science quality) for climate-related research. Little attention, however, has been paid to data quantity, particularly on how data quality control during data processing impacts downstream data quality and data quantity. In this letter, we attempt to fill this knowledge gap using measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). For this sensor, the same level-1B data are processed independently using different quality control methods by NASA and NOAA, respectively, allowing for an in-depth evaluation of the interplay between data quantity and quality. The results indicate that the methods to identify stray light and sun glint are the two primary quality control procedures affecting data quantity, where the criteria for flagging pixels “contaminated” by stray light and sun glint may be relaxed in the NASA ocean color data processing to increase data quantity without compromising data quality.

The on-orbit calibration performance of the Ocean and Land Color Instrument (OLCI) onboard the Sentinel-3A satellite, launched on February 16, 2016, is evaluated via a radiometric intersensor comparison with reference to the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. Among the 21 OLCI bands (designated as “Oa” bands), which are reflective solar bands (RSBs), seven OLCI bands match up sufficiently well with the seven shortest wavelength SNPP VIIRS bands (M1-M7)-they are Oa02 at 412.5 nm, Oa03 at 442.5 nm, Oa04 at 490 nm, Oa06 at 560 nm, Oa08 at 665 nm, Oa12 at 754 nm, and Oa17 at 865 nm. The radiometric comparison adopts a “nadir-only” refinement of the simultaneous nadir overpass (SNO) approach and uses the official SNPP VIIRS RSB data processed by the Interface Data Processing Segment (IDPS). The time-series result for bands Oa02, Oa03, Oa08, and Oa17, with spectral coverage that well represents the spectral range of OLCI, shows two-year stability at the level of 0.3% that supports nominally correct on-orbit calibration. The result for Oa08, Oa09, and Oa10, the three spectrally adjacent bands matching M5, demonstrates the effects of spectral mismatch-different radiometric ratio baselines and seasonally modulating patterns. Lastly, this result clarifies some key findings of earlier studies involving SNPP VIIRS and illustrates great potential for significantly more radiometric evaluation activities in the new era of Earth observations with many more powerful multispectral sensors coming into operation.

In open ocean and coastal/inland waters, the normalized water-leaving radiance spectra nLw(λ) are determined by water inherent optical properties (IOPs). However, for coastal/inland waters, the complex feature of the water IOPs makes it challenging for accurate retrieval of IOPs from satellite measurements. In this paper, we overview the recent progress for deriving accurate water IOPs and suspended particle size distribution (PSD) over global turbid coastal and inland waters. We show that water reflectance model in coastal and inland waters can be significantly simplified at the near-infrared (NIR) wavelengths, thus particle backscattering coefficient bbp(λ), phytoplankton absorption coefficient aph(λ), and dissolved and detrital absorption coefficient adg(λ) can be derived from nLw(λ) spectra from the Visible Infrared Imaging Radiometer Suite (VIIRS) observations. Specifically, the coefficient bbp(λ) and PSD in global highly turbid water can be characterized and quantified. We show that bbp(λ), aph(λ), and adg(λ) values derived using the NIR IOP algorithm match well with the true values in turbid coastal and inland waters. An IOP algorithm that combines the NIR-based IOP algorithm for coastal/inland waters and the Quasi-Analytical Algorithm (QAA) IOP algorithm for the open ocean is proposed to derive bbp(λ), aph(λ), and adg(λ) for global ocean. Using China's east coastal region, Amazon River Estuary, and La Plata River Estuary as examples, we demonstrate that the combined IOP algorithm can produce high-quality bbp(λ), aph(λ), and adg(λ) data, as well as PSD products for both turbid coastal/inland waters and open ocean from VIIRS-derived nLw(λ) spectra.

We provide an overview of the progress on producing accurate global ocean color products from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites. VIIRS global ocean color products include normalized water-leaving radiance spectra nLw(l) at VIIRS five spectral bands, chlorophyll-a (Chl-a) concentration, water diffuse attenuation coefficients at 490 nm, Kd(490), and at the domain of photosynthetically available radiation (PAR), Kd(PAR). However, VIIRS-derived daily ocean color images on either SNPP or NOAA-20 have some limitations in ocean coverage due to its swath width, high sensor-zenith angle, sun glint, and cloud, etc. Merging VIIRS ocean color products derived from the SNPP and NOAA-20 significantly increases the spatial coverage of daily data images. The two VIIRS sensors on SNPP and NOAA-20 have similar sensor characteristics, and ocean color products are derived using the same Multi-Sensor Level-1 to Level2 (MSL12) ocean color data processing system. Therefore, the merged VIIRS ocean color data from the two sensors have high data quality with consistent statistical property and accuracy globally. We describe an approach to remove data gaps of missing pixels from the SNPP and NOAA-20 merged global ocean color data. Some applications using the new complete global data coverage of daily ocean color product are also presented and discussed.

A methodological variant of the standard on-orbit calibration of reflective solar bands (RSBs) is presented for the Visible Imaging Infrared Radiometer Suite (VIIRS) housed in the Suomi National Polar-orbiting Partnership (SNPP) satellite. The new variant uses the full profile of direct solar illumination of the solar diffuser (SD), including both full and partial illuminations, to characterize the on-orbit gain change of the RSBs, differing from the standard approach that uses a smaller “sweet spot” subinterval within the full-illumination stage. The extended incident angular range of the solar light requires a new characterization analysis of the impact of the transmission function of the SD screen, SD bidirectional reflectance factor (BRF), and other affected calibration steps. Instead of the standard a priori derivation of the known characterization functions for the wider range, this analysis directly characterizes their manifested impact in the instrument data through a step-by-step extraction from a selected three-year period to build up a series of intermediate functions that are applicable mission-long. This newly adopted procedure presents a significant simplification as well as more clarity of the characterization analysis. The new RSB calibration coefficient of the full-profile approach is extracted for all 14 RSBs of SNPP VIIRS and is shown to be stable and smooth at the level of 0.1%. For bands M5 and above, the full-profile result achieves excellent agreement with the standard result, whereas results for bands M1-M4 diverge, in particular up to 2% for band M1, the shortest wavelength RSB at 410 nm. The finding elucidates a key challenge of the on-orbit RSB calibration arising from the nontrivial angular dependence of the on-orbit degradation of SD that introduces calibration error into any SD-based approach, such that the on-orbit RSB calibration result is not stable with different choices of the angular range of incident and outgoing light with respect to the SD. A detailed discussion of the nontrivial angular dependence in SD degradation is provided in the context of the known on-orbit RSB calibration results and recent findings, including discrepancy with the lunar-based calibration for bands M1-M4.

In this paper, we provide an overview of the progress on producing accurate global ocean color products from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites. SNPP and NOAA-20 were launched on October 28, 2011 and November 18, 2017, respectively. VIIRS global ocean color products include normalized water-leaving radiance spectra nLw(λ) at VIIRS five spectral bands, chlorophyll-a (Chl-a) concentration, water diffuse attenuation coefficients at the wavelength of 490 nm, Kd(490), and at the domain of photosynthetically available radiation (PAR), Kd (PAR). In addition, new products of nLw(λ) at VIIRS I-band (638 nm for SNPP and 642 nm for NOAA-20) and quality assurance (QA) score are now included. VIIRS global ocean color products are being routinely produced using the Multi-Sensor Level-l to Level-2 (MSL12) ocean color data processing system. Specifically, we describe our effort for the improvements of MSL12, particularly over coastal and inland waters, as well as some evaluations with in situ data from the Marine Optical Buoy (MOBY). Furthermore, we provide VIIRS ocean color data from both SNPP and NOAA-20, as well as from merged global Chl-a data from two VIIRS sensors, showing significantly improved data coverage. Some examples from the recently developed data gap-filling technique for VIIRS-SNPP are also presented and discussed.

Ocean color data are critical for the monitoring and understanding of biological and ecological processes and phenomena, and the data are also important sources of input data for physical and biogeochemical ocean models. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership has continued to provide global ocean color data since its launch in October 2011. However, there are always many missing pixels in the original VIIRS-measured ocean color images due to clouds and various other reasons. The data interpolating empirical orthogonal functions (DINEOF) is a method to reconstruct (gap filling) missing data in geophysical data sets based on the empirical orthogonal function. In this paper, the DINEOF is applied to VIIRS-derived global Level-3 binned ocean color data of 9-km spatial resolution, and the DINEOF-reconstructed ocean color data are used to fill the gaps of missing data. In particular, daily, eight-day, and monthly VIIRS global Level-3 binned ocean color data, including chlorophyll-a concentration, diffuse attenuation coefficient at 490 nm [Kd(490)], and normalized water-leaving radiance spectra [nLw(λ)] at the five VIIRS visible bands, are tested and evaluated. To validate and evaluate the gap-filled data, a set of original valid nonmissing pixels in the VIIRS images are selected randomly and treated as missing pixels in the DINEOF process, so that the reconstructed pixels can be compared with the original data. Results show that the DINEOF method can successfully reconstruct and gap-fill meso-scale and large-scale spatial ocean features in the global VIIRS Level-3 images, as well as capture the temporal variations of these features.

We describe our effort to use normalized water-leaving radiance at the short blue band 410 nm,nLw(410), derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership to improve the VIIRS ocean color products over coastal and inland waters, in particular, over the regions contaminated by strongly absorbing aerosols. The current standard atmospheric correction algorithm has significant issues when dealing with cases of strongly absorbing aerosols, e.g., dust, smoke, and air pollution from nearby cities. For such cases, satellite-derived normalized water-leaving radiance spectranLw($\lambda $) are usually biased low and may be negative, particularly for the short blue bands, e.g., for the VIIRSnLw(410). In addition, for extremely turbid waters and waters dominated by colored dissolved organic matter (CDOM) that is strongly absorbing at the short blue band, slightly negativenLw(410) data are also sometimes observed. Obviously, cases withnLw(410)nLw(410)nLw(410) information. Specifically, for cases withnLw(410)nLw(410)$\ge 0$[e.g., assumingnLw(410) = 0], thereby removing unphysical retrievals for VIIRS-derivednLw($ \lambda $) spectra. Using this technique, VIIRS-derivednLw($ \lambda $) spectra are now all ≥ 0, showing considerable improvements for VIIRS ocean color products, particularly for biological and biogeochemical products [e.g., chlorophyll-a concentration and diffuse attenuation coefficient at 490 nm$K_{d}$(490)] which are derived using VIIRSnLw($\lambda $) spectra. Several detailed examples from VIIRS measurements over various coastal and inland waters are provided and discussed. The new technique has been implemented in the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system, which has been used for the routine production of VIIRS global ocean color products.

In this paper, we provide an overview of the progress on producing accurate and consistent ocean color products from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). To meet requirements from broad users (e.g., operational, research, modeling, etc.), we have proposed and now been routinely producing two VIIRS ocean color data streams, i.e., the near-real-time (NRT) and delayed science quality ocean color product data. The implementation details for the two data streams will be discussed. In addition, with significantly improved satellite data processing algorithms, as well as considerably improved sensor on-orbit radiometer calibration using both solar and lunar methods, VIIRS mission-long ocean color data have been successfully reprocessed using the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system. VIIRS ocean color data have been significantly improved over the global open ocean, as well as turbid coastal and inland waters. In particular, VIIRS-derived water property data over global high altitude lakes (e.g., Lake Victoria in South Africa) are now very much improved, providing a significant progress for satellite remote sensing of inland water properties. Some detailed evaluation of VIIRS ocean color products, as well as sensor performance, will be discussed. Results show that VIIRS can provide high-quality global ocean color products in support of the science researches and operational applications.

In this paper, we provide an overview of ocean color products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP), which was launched on October 28, 2011. VIIRS ocean color products include normalized water-leaving radiance spectra nLw(λ) at VIIRS five spectral bands, chlorophyll-a concentration (Chl-a), water diffuse attenuation coefficients at the wavelength of 490 nm, Kd(490), and at the domain of photosynthetically available radiation (PAR), Kd(PAR). In addition, new products of nLw(λ) at VIIRS I-band 638 nm nLw(638) and QA Score are now included. VIIRS ocean color products derived from the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system are evaluated and compared routinely with in situ data from the Marine Optical Buoy (MOBY) and several AERONET-OC sites. Our evaluation results show that VIIRS has been providing high-quality global ocean color products in support of scientific research and operational applications. Some application examples using satellite ocean color data are provided. VIIRS-derived ocean color data can be used for water property monitoring and evaluation, habitat characterization, and refining stock assessments.

Hourly measurements during 9:00 to 16:00 local time are a unique capability of the Korean Geostationary Ocean Color Imager (GOCI) in monitoring fast-evolving ocean features. In this paper, we derive ocean surface currents in the Bohai Sea from GOCI data using the maximum cross correlation (MCC) feature tracking method and compare the results with altimetry-inversed tidal currents produced from the Oregon State University Tidal Inversion Software (OTIS) after the latter is validated using in situ measurements. The performance of the GOCI-based MCC method is assessed and the discrepancies between the GOCI and OTIS currents are evaluated through a series of sensitivity studies, including studies of different ocean color products, MCC and filter parameters, and time interval between image pairs, in order to find the best setups for optimal MCC performance. Our results show that a box size of 10-15 km and a search range of 4-8 km for every 1 h of time difference, combined with subsequent filters that use the cutoff correlation coefficient of 0.5 and 3 or 4 neighbors within 15 cm/s difference, gives optimal results. Under these setups, the bias errors of GOCI-derived current maps are about 8 cm/s for current vectors and 3° for current directions, respectively. The noise errors are about 20 cm/s in current velocity and 20° in current direction with correlation coefficients of ~0.75 and ~0.9 for the current velocity and current direction, respectively. These findings are useful in developing an hourly surface current product from GOCI measurements for the Bohai Sea.

Satellite remote-sensing data are essential for monitoring and quantifying water properties in the Great Lakes, providing useful monitoring and management tools for understanding water optical, biological, and ecological processes and phenomena. However, during the winter season, large parts of the Great Lakes are often covered by ice, which can cause significant uncertainties in satellite-measured water quality products. Although some developed radiance-based ice-detection algorithms for satellite ocean color data processing can eliminate most of the ice pixels in a region, there are still some significant errors due to misidentification of ice-contaminated pixels, particularly for the thin ice-covered regions. Therefore, it is necessary to improve the ice-detection methods for satellite ocean color data processing in the Great Lakes. In this paper, impacts of ice contamination on satellite-derived ocean color products in the Great Lakes are investigated, and a refined regional ice-detection algorithm which is based on the radiance spectra and normalized water-leaving radiance at the wavelength of 551 nm, nLw(551), is developed and assessed for satellite ocean color data processing in the Great Lakes. Results show that this proposed ice-detection method can reasonably identify ice-contaminated pixels, including those in very thin ice-covered regions, and provide accurate satellite ocean color products for the winter season in the Great Lakes.

In this paper, we provide overview of the progress on the evaluations of the Visible Infrared Imaging Radiometer Suite (VIIRS) ocean color products with long-term time series and more in situ data for matchup analysis. Specifically, VIIRS ocean color products include normalized water-leaving radiance spectra nLw(λ) at VIIRS five spectral bands, chlorophyll-a concentration (Chl-a), water diffuse attenuation coefficients at the wavelength of 490 nm, Kd(490), and at the domain of photosynthetically available radiation (PAR), Kd(PAR). VIIRS ocean color products derived from the NOAA Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system are evaluated and compared routinely with in situ data from the Marine Optical Buoy (MOBY) and several AERONET-OC sites. In order to meet requirements from all users, we propose to routinely produce two ocean color data streams, i.e., the near-real-time (NRT) data and delayed science quality data. The NRT ocean color data stream has the advantage of quick data turn around with data latency −12–24 hours, while the science quality data stream has high consistence (with the mission-long data reprocessing) and accuracy of ocean color products. In addition, we have significantly improved on-orbit sensor calibration by combining the lunar calibration into the current solar calibration method. Our results show that VIIRS is now providing high-quality global ocean color products in support of the scientific research and operational applications.

The Visible Infrared Imager Radiometer Suite (VIIRS) has been on-orbit for more than three and half years. It has been scheduled to view the moon approximately monthly since its nadir door open on November 21, 2011. The scheduled lunar observations have been used to monitor the VIIRS reflective solar bands (RSB) on-orbit gain changes. The VIIRS RSB are primarily calibrated by an onboard Solar Diffuser (SD) panel and an accompanying Solar Diffuser Stability Monitor (SDSM). Due to non-uniformity of the SD degradation, the SD/SDSM calibration may have non-negligible errors, especially for the short wavelength bands. Since lunar surface is very stable, the Moon can be used to provide more reliable on-orbit long-term gain changes of the RSB. The RSB calibration coefficients derived from the lunar calibration are generally consistent with those derived from the SD/SDSM calibration, but clear differences in trend are seen, especially for the short wavelength bands.

The reflective solar bands (RSB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (SNPP) satellite are primarily calibrated on-orbit by a solar diffuser (SD) panel whose performance is monitored by an accompanying solar diffuser stability monitor (SDSM). In this paper, the SD and SDSM calibration methodology is reviewed and the results from the analysis of the up-to-date three and half years of mission data are presented. With the newly derived product of the SD bidirectional reflectance factors, the vignetting functions for the screens in the SD/SDSM system and the carefully selected “sweet spots”, and the fully illumination region, the artificial seasonal patterns and noises in the derived SD degradation and the RSB calibration coefficients are removed or significantly reduced. The result shows that the SD degrades faster at short wavelengths while the RSB degrades in a much complex pattern.

In this paper, we provide evaluations and assessments of the Visible Infrared Imaging Radiometer Suite (VIIRS) ocean color products, including normalized water-leaving radiance spectra nLw(λ) at VIIRS five spectral bands, chlorophyll-a concentration (Chl-a), water diffuse attenuation coefficients at the wavelength of 490 nm, Kd(490), and at the domain of photosynthetically available radiation (PAR), Kd(PAR). Specifically, VIIRS ocean color products derived from the NOAA Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system are evaluated and compared with in situ data from the Marine Optical Buoy (MOBY) and measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS). In general, VIIRS ocean color products are matched well with MOBY in situ measurements, and are also consistent with those from MODIS-Aqua. Ocean color products were found to be highly sensitive to some operational sensor calibration issues. We have improved sensor calibration by combining the lunar calibration into the current calibration method. Here, the ocean color products based on the new sensor calibration are evaluated. Our results show that VIIRS is capable of providing high-quality global ocean color products in support of the scientific research and operational applications.

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a whisk broom scanning radiometer, which is onboard the Terra and Aqua spacecraft. Both MODIS instruments have successfully completed more than 12 years of on-orbit flight. The long-wave infrared (LWIR) photovoltaic bands (bands 27-30, 6.72-9.73 μm) on the LWIR focal plane assembly in Terra MODIS have contamination due to electronic crosstalk. In this paper, we examine Terra MODIS band 28 (7.33 μm) crosstalk effects, their impact, and mitigation. The crosstalk signal is identified and characterized using the regular lunar observations acquired by MODIS. It is evident from the derived crosstalk coefficients that the contamination was mainly from bands 27 (6.72 μm), 29 (8.55 μm), and 30 (9.73 μm). The crosstalk coefficients are generally a small positive quantity in the early to middle part of the mission with a few exceptions, and then changing directions. A linear correction algorithm is applied to both L1B calibration and retrieval to qualitatively and quantitatively assess the impact and improvements in this paper. It is shown that the crosstalk correction improved the imagery and radiometric fidelity of this band.

The Visible Infrared Imaging Radiometer Suite (VIIRS) Raw Data Records (or Level-0 data) are processed using the current standard Algorithm Development Library (ADL) to produce Sensor Data Records (SDR; or Level-1B data). The ocean color Environmental Data Records (EDR), one of the most important product sets derived from VIIRS, are processed from the SDR of the visible and near-infrared moderate resolution (M) bands. As the ocean color EDR are highly sensitive to the quality of the SDR, the bands from which the EDR data arise must be accurately calibrated. These bands are calibrated on-orbit using the onboard Solar Diffuser, and the derived calibration coefficients are called F-factors. The F-factors used in the forward operational process may have large uncertainty due to various reasons, and thus, to obtain high-quality ocean color EDR, the SDR needs to be regularly reprocessed with improved F-factors. The SDR reprocessing, however, requires tremendous computational power and storage space, which is about 27 TB for one year of ocean-color-related SDR data. In this letter, we present an efficient and robust method for reduction of the computational demand and storage requirement. The method is developed based on the linear relationship between the SDR radiance/reflectance and the F-factors. With this linear relationship, the new SDR radiance/reflectance can be calculated from the original SDR radiance/reflectance and the ratio of the updated and the original F-factors at approximately 100th or less of the original central processing unit requirement. The produced SDR with this new approach fully agrees with those generated using the ADL package. This new approach can also be implemented to directly update the SDR in the EDR data processing, which eliminates the hassle of a huge data storage requirement as well as that of intensive computational demand. This approach may also be applied to other remote sensors for data reprocessing from raw instrument data to science data.

The Ocean Color CAL/VAL team is evaluating the VIIRS bio-optical products for real-time operations. VIIRS ocean data are being processed using standard government algorithms, and channel calibration and product validation evaluation activities are ongoing. A network of 27 global “Golden Regions” has been established to evaluate and validate bio-optical products. Satellite inter-comparison for data consistency with current ocean color products, and real time vicarious adjustment calculation are performed using in situ water leaving radiance propagated to Top of Atmosphere in coastal and open ocean regions. In addition, routine matchups with VIIRS and MODIS-Aqua are done with in situ data collection from ships and real time coastal AERONET-OC sites. The above activities, product evaluation and tracking of channel stability, are being contributed to the JPSS Team to evaluate the overall mission, including calibration and inter-satellite product consistency. Initial NPP VIIRS ocean bio-optical products are demonstrated with other ocean color satellites.

The results of this study demonstrate the effects of sensor noise in the shortwave infrared (SWIR) bands on the satellite-derived ocean color products, in particular, normalized water-leaving radiance spectra data. A simple radiance smoothing technique is used for reducing sensor noise equivalent radiance NEΔL(λ) (or increasing the sensor signal-to-noise ratio) values for the SWIR radiances measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite. Specifically, a simple 5 × 5 box running mean approach for MODIS-Aqua-measured SWIR radiances has been proposed. We show that the noise errors in MODIS-Aqua-derived ocean color products using the SWIR-based atmospheric correction algorithm are mainly from large noise errors in the MODIS-Aqua-measured SWIR radiances due to their significantly larger sensor NEΔL(λ) values. Using the proposed SWIR radiance smoothing method, SWIR-derived ocean color products can be improved with considerably reduced product noise. In addition, a practical method in estimation of the MODIS-Aqua SWIR NEΔL(λ) values has been developed. We show that, for future ocean color satellite sensors, it will require NEΔL(λ) values of less than ~5 × 10-4and ~9 × 10-5mW · cm-2· μm-1· sr-1for SWIR bands at 1240 and 2130 nm, respectively.

This paper investigates the improvements to the quality of ocean color data using an appropriate choice of ancillary data for National Oceanic and Atmospheric Administration (NOAA) operational ocean color data processing, which requires routine ocean color product production in near real time. The ancillary data, such as the total column ozone amount, sea surface wind speed, atmospheric pressure, and total column water-vapor amount, are required for satellite ocean color data processing for deriving ocean color products, e.g., normalized water-leaving radiance spectra data, chlorophyll-a concentration, water diffuse attenuation coefficient, etc. Currently, NOAA's CoastWatch program uses the climatology ancillary data for the near-real-time ocean color data processing. Alternative ancillary data sets that can replace the climatology data for the near-real-time ocean color data processing have been investigated and studied. The studies were carried out from four selected NOAA CoastWatch regions covering the U.S. coastal and Hawaii regions and for four months (January, April, July, and October), representing the four seasons. Based on the evaluation results, we propose to use the ancillary data produced from the Global Forecast System (GFS) model for the NOAA operational ocean color data processing, as well as for any other near-real-time data processing that requires ancillary data inputs. The effects of using the GFS model data on the accuracy of the derived ocean color products are also investigated and discussed.

Current data processing for deriving ocean color products from the Moderate Resolution Imaging Spectroradiometer (MODIS) has no specific ice and mixed ice-water pixel detection procedure. The near-infrared (NIR) reflectance threshold at the MODIS 869-nm band, which has been used to discriminate clear sky from clouds (cloud masking) for standard ocean color data processing, can eliminate most of the ice pixels. However, there are still many cases for which the ice and mixed ice-water pixels have been misidentified as ocean waters in current ocean color data processing, leading to errors in the MODIS-derived ocean color product (e.g., chlorophyll-a concentration). This is particularly true for most of the mixed ice-water cases. For atmospheric correction using the short-wave infrared (SWIR) method, which also uses SWIR reflectance for cloud masking, the problem of ice misidentification is even worse. In this paper, we describe a method for detection of ice and mixed ice-water pixels for MODIS ocean color data processing. Using the MODIS-derived normalized water-leaving radiances at 412, 555, and 859 nm, a scheme for ice and mixed ice-water detection has been developed and tested for producing MODIS global ocean color products. In fact, the proposed algorithm is a by-product calculated from the MODIS-derived normalized water-leaving radiance spectra data. Thus, the MODIS-derived ice surface radiance data can be used to study sea ice physical and optical properties. With the new ice detection scheme, pixels with ice and/or mixed ice-water can be discriminated, flagged, or masked out. The ice detection results are compared with the MODIS ice map product produced from the MODIS land discipline team, as well as the ice product data obtained from the NOAA National Ice Center. We show improved results from the new masking algorithm for the purpose of MODIS ocean color data processing, particularly for detection of mixed ice-water pixels.

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) use the near-infrared (NIR) reflectance threshold at 865 nm (869 nm for MODIS) to discriminate clear sky from clouds for processing of the ocean color products. Such a simple scheme generally works well over the open oceans where Case-1 waters and maritime aerosols are usually the case. However, in coastal regions, there are often cases with significant ocean contributions at the NIR wavelengths from the turbid waters. In addition, aerosols are likely to be dominated with small particles (large Aringngstrom exponent). In these cases, the cloud-masking scheme using the NIR reflectance threshold often mistakenly identifies these scenes as clouds, leading to significant loss of coverage in coastal regions. In this paper, we propose to use the MODIS short wave infrared (SWIR) bands at either 1240 or 1640 nm for detecting clouds. Ocean is black for turbid waters at SWIR wavelengths due to much stronger water absorption. The aerosol contribution in the SWIR bands is also significantly lower for nonabsorbing and weakly absorbing aerosols with small aerosol particle size. Thus, using the SWIR reflectance threshold, the performance of the cloud-masking algorithm in the coastal region is much better than that of using the NIR band. For sensors that do not have SWIR bands (e.g., SeaWiFS), we propose to use the Rayleigh-corrected (RC) reflectance ratio value from two NIR bands in addition to the reflectance threshold at 865 nm. The clouds are spectrally flat and have lower reflectance ratio values from two NIR measurements than cases with reflectance contributions from ocean and aerosols. It was found that, corresponding to the RC reflectance threshold of 2.7% at 869 nm, the RC threshold reflectances for 1240 and 1640 nm are 2.35% and 2.15%, respectively. The cloud-masking performance with the SWIR bands in the coastal region can usually be achieved using the RC reflectance ratio value (ges 1.15 as clear atmosphere) between two NIR bands in addition to the reflectance threshold at 869 nm

It is well known that, to accurately retrieve the spectrum of the water-leaving radiance and derive the ocean color products from satellite sensors, a vicarious calibration procedure, which performs sensor in-orbit calibration for a whole system (the sensor and algorithms) is necessary. Both Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) have employed in-orbit vicarious calibration procedure that uses the in situ measurements with the Marine Optical Buoy (MOBY) in the waters off Hawaii. Such method can also be applied to vicarious inter-calibrate other sensors. In addition to the ocean color products, aerosol optical property data over ocean are routinely retrieved from both SeaWiFS and MODIS measurements. The aerosol retrieval algorithm uses radiances measured at two near-infrared (NIR) wavelengths, at which the ocean appears black due to strong absorption by water, to estimate the aerosol optical properties and extrapolate these into the visible. The spectral information from two band measurements is used to retrieve the most appropriate aerosol models. With the derived aerosol models, the aerosol optical thickness can then be estimated using the measured signal at 865 nm. In this paper, I outline the procedure for the in-orbit sensor vicarious calibration for the ocean color and aerosol products. Simulations that demonstrate the effectiveness of the vicarious calibration method on the derived ocean color and aerosol products are presented and discussed. Results of sensitivity studies that show effects of the calibration error at 865 nm, appropriateness of aerosol models, and the solar-sensor viewing geometry on the accuracy of the retrieved ocean color and aerosol optical properties are presented.

The Modular Optoelectronic Scanner (MOS) was launched in the spring of 1996 on the Indian IRS-P3 satellite. With the successful launch of NASA's Sea-viewing Wide Field of-view Sensor (SeaWiFS) in the summer of 1997, there are now two ocean color missions in concurrent operation, and there is interest to compare data from these two sensors. In this paper, we describe our efforts to retrieve ocean-optical properties from both SeaWiFS and MOS using consistent methods. We first briefly review the atmospheric correction, which removes more than 90% of the observed radiances in the visible, and then we describe how the atmospheric-correction algorithm used for the SeaWiFS data can be modified for application to other ocean color sensors. Next, since the retrieved water-leaving radiances in the visible between MOS and SeaWiFS are significantly different, we developed a vicarious intercalibration method to recalibrate the MOS spectral bands based on the optical properties of the ocean and atmosphere derived from the coincident SeaWiFS measurements. Furthermore, because of the strange calibration behavior of the MOS 750 nm band, we modified the atmospheric correction such that the MOS 685 and 868 nm bands can also be used. We present and discuss the MOS-retrieved, ocean-optical properties before and after the vicarious calibration using both the MOS 685 and 750 nm coupled with 868 nm bands in comparison with results from SeaWiFS and demonstrate the efficacy of this approach. We show that it is possible and efficient to vicariously intercalibrate sensors between one and another.