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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 Visible Infrared Imager Radiometer Suite (VIIRS) is not equipped with a fluorescence band, which may affect its ability to detect and quantify harmful algal blooms (HABs) in coastal waters rich in colored dissolved organic matter. Such a deficiency has previously been demonstrated for a bloom of the toxic dinoflagellate Karenia brevis in the northeastern Gulf of Mexico (NEGOM) in summer 2014. Here, using data collected in the field and by VIIRS and Moderate Resolution Imaging Spectroradiometer (MODIS), we show that such a deficiency may be partially overcome using a red-green-chlorophyll-a index (RGCI). A relationship between near-concurrent (±4 hours) VIIRS RGCI (Rrs(672)/Rrs(551)) and field-measured chlorophyll-a (Chla; in mg m −3) was developed and evaluated using calibrated Chla obtained by a flowthrough system. A mean relative uncertainty, which was approximately twofold lower than VIIRS OC3M Chla, was obtained for VIIRS RGCI Chla (mean relative error: ∼56%) over a large range (0.5–20 mg m −3). Similar spatial patterns between near-concurrent MODIS-Aqua (MODISA) normalized fluorescence line height (nFLH) and VIIRS RGCI Chla imagery indicate that VIIRS RGCI may be used as a surrogate for MODISA nFLH in the absence of a fluorescence band. The success of this newly developed data product may be partially attributed to the 20-nm bandwidth of the VIIRS 672-nm band (662–682 nm) that covers a portion of the solar stimulated fluorescence region. However, whether such observations from a simple case study can be extended to other turbid coastal or inland waters still remains to be tested.

Satellite images of reflected sunlight have been used to detect and monitor oil spills in oceans. However, such a capacity is often hindered by the image noise due to either a low signal-to-noise ratio or other image features such as clouds or cloud shadows. The problem is particularly severe for nighttime images captured by the Visible Infrared Imager Radiometer Suite (VIIRS). This letter proposes a practical method to extract oil slick features in a semiautomatic fashion from VIIRS nighttime images and other noisy optical remote sensing images. The method is based on statistical information and morphological operators, and it is demonstrated to be able to effectively remove the noise and identify line features with the appropriate selection of threshold values. Testing this method over VIIRS nighttime images shows the preliminary success of oil slick feature extraction. Experiments on daytime data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) also suggest the applicability of this method to other optical remote sensing images. However, the requirement of human intervention to determine optimal parameters points to the need for improved automation in future works.

Using data collected from two eutrophic lakes located in eastern China (Taihu Lake, 2330 km2 and Chaohu Lake, 760 km2), the influence of variable particle composition on remote sensing reflectance (Rrs, in sr-1) properties and on the Medium Resolution Imaging Spectrometer (MERIS) maximum chlorophyll index (MCI) algorithm for estimating near-surface chlorophyll-a concentrations (Chla, in $\mu\text{g}\cdot\text{L}^{-1} $ ) is demonstrated. Although separated by a distance of only ∼200 km, the two lakes showed dramatic differences in particle composition, with Taihu Lake dominated by inorganic particles and Chaohu Lake dominated by organic particles. Such differences led to variable Rrs spectral slopes in the red and near-IR bands and perturbations to the MCI algorithm. A modified MCI algorithm (MCIT) was then developed to reduce the impact of turbidity caused by inorganic particles. Root-mean-square errors in Chla retrievals decreased from 129.5% to 43.5% when using this new approach compared with the MCI algorithm in Taihu Lake for Chla ranging between ∼5 and 100 $\mu\text{g}\cdot\text{L}^{-1} $ . Application of this approach to other turbid water bodies, on the other hand, requires validation and possibly further tuning.

Ocean color continuity calls for consistent observations from multiple sensors in order to establish a seamless data record to address earth science questions. Currently, both Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua satellites are being operated well beyond their designed five-year mission life, and they have shown signs of sensor degradation. It is thus urgent to evaluate whether the most recently launched Visible Infrared Imager Radiometer Suite (VIIRS) instrument (2011 to present) can provide consistent observations should MODIS instruments stop functioning. In this study, the consistency between MODIS/Aqua and VIIRS measurements over the Tampa Bay estuary ( ~ 1000 km2) is assessed for remote sensing reflectance (Rrs, sr-1), chlorophyll-a concentrations (Chla, mg·m-3), and absorption coefficient of colored dissolved organic matter (ag(443), m-1). While Rrs was derived as a standard National Aeronautics and Space Administration product from the SeaDAS software package (reprocessing version R2013.0), Chla and ag(443) were estimated using the recently developed regional algorithms for Tampa Bay. Time-series analysis and statistics both showed that the two sensors provided consistent measurements for most products evaluated, with unbiased mean percentage differences of 25% and mean annual biases within -9% (except for one of the eight cases) for large dynamic ranges in Chla (1.0-20 mg·m-3) and ag(443) (0.1-1.5 m-1) in all four bay segments. These estimates are comparable or better than those derived from satellite-in situ comparisons, suggesting that VIIRS will provide observations consistent with MODIS, ensuring ocean color continuity and seamless data records for Tampa Bay. Such observations are crucial in establishing a long-term satellite-based water quality decision matrix for Tampa Bay.

Cloud filters developed for high-resolution (1-km) Advanced Very High Resolution Radiometer (AVHRR) satellite-derived sea surface temperature (SST) observations are generally inadequate to capture extreme cold events. Such events impacted shallow waters in Florida Bay and other coastal regions in January 2010 with fatal consequences for large numbers of corals and associated organisms. Raw AVHRR images were reprocessed to understand whether historical knowledge of daily and interannual SST variations could be used to derive a practical cloud-filtering technique. This approach, however, misidentified valid water temperature pixels in nearly 20% of 2703 images collected during the month of January for each year between 1995 and 2010. To create an improved SST climatology, this cloud-filtering method was combined with manually delineated overrides of falsely masked regions. During the January 2010 cold event, this climatology indicated negative SST anomalies of up to 11.6°C in the Big Bend region and 14°C in Florida Bay, with high spatial heterogeneity throughout. Our findings highlight the need for improved autonomous cloud-masking techniques to detect cold events in near real time.

Daily high-resolution Sea-viewing Wide Field-of-view Sensor (SeaWiFS) images of the central North Atlantic Ocean (1998-2003) show that temporal changes in the absorption coefficient of colored dissolved organic matter (CDOM) or "yellow substance" follow changes in phytoplankton pigment absorption coefficient in time. CDOM peaks (between January and March) and troughs (late summer and fall) followed pigment peaks and troughs by approximately two and four weeks, respectively. This phase shift is additional strong evidence that CDOM in the marine environment is derived from phytoplankton degradation. The common assumption of linear covariation between chlorophyll and CDOM is a simplification even in this ocean gyre. Due to the temporal changes in CDOM, chlorophyll concentration estimated based on traditional remote sensing band-ratio algorithms may be overestimated by about 10% during the spring bloom and underestimated by a similar 10% during the fall. These observations are only possible through use of synoptic, precise, accurate, and frequent measurements afforded by space-based sensors because in situ technologies cannot provide the required sensitivity or synoptic coverage to observe these natural phenomena.