The suspended particulate matter (SPM) concentration (unit: mg l⁻¹) in surface waters is an essential measure of water quality and clarity. Satellite remote sensing provides a powerful tool to derive the SPM with synoptic and repeat coverage. In this study, we developed a new global SPM algorithm utilizing the remote sensing reflectance (R_rs(λ)) at near‐infrared (NIR), red, green, and blue bands (NIR‐RGB) as input. The evaluations showed that the NIR‐RGB algorithm could predict SPM with the median absolute percentage difference of ∼35%–39% over a wide range from ∼0.01 to >2,000 mg l⁻¹. The uncertainty is smaller (29%–37%) for turbid waters where R_rs(671) ≥ 0.0012 sr⁻¹ and slightly higher (41%–44%) for clear waters where R_rs(671) < 0.0012 mg l⁻¹. The algorithm was implemented with the global R_rs(λ) data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar‐orbiting Partnership (SNPP) satellite. We provided a brief characterization of the spatial distribution and temporal trends of the SPM products in global waters based on the monthly SPM composites. Case studies of the SPM time series in coastal and inland waters suggest that the satellite SPM estimations registered spatial and seasonal variation and episodic events in regional scales as well. The VIIRS‐generated global SPM maps provide a valuable addition to the existing ocean color products for environmental and climate applications.

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 spectra nLw(±bλ) are usually biased low and may be negative, particularly for the short blue bands, e.g., for the VIIRS nLw(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 negative nLw(410) data are also sometimes observed. Obviously, cases with nLw(410) < 0 are unphysical and incorrect. However, VIIRS-derived nLw(410) < 0 do provide useful information to further improve satellite-derived ocean color products in such cases. In this paper, we develop a technique to improve VIIRS ocean color products in cases with strongly absorbing aerosols, CDOM-dominated and extremely turbid waters using VIIRS-derived nLw(410) information. Specifically, for cases with nLw(410) < 0, atmospheric correction can be carried out again to ensure that nLw(410) ≥ 0 [e.g., assuming nLw(410) = 0], thereby removing unphysical retrievals for VIIRS-derived nLw(±bλ) spectra. Using this technique, VIIRS-derived nLw(±bλ) 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 Kd(490)] which are derived using VIIRS nLw(±bλ) 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.

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.