Multi-Sensor Level-1 To Level-2 Research Articles

Statistical Evaluation of Sentinel-3 OLCI Ocean Color Data Retrievals

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 ρ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wN</i> (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 ρ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wN</i> (λ) spectra. The deviations in ρ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wN</i> (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 ρ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wN</i> (λ) spectra by EUMETSAT retrievals in moderate sun glint conditions being the notable exception.

Global land mask for satellite ocean color remote sensing

We develop a methodology to derive a global medium resolution (250 m) land mask from several existing data sources. In particular, a number of improved land mask data sets have been developed from satellite measurements recently, though some artifacts and omissions still remain. We show how combining global land mask data from multiple independent data sources can decrease the frequency of artifacts, and improve the data consistency and quality. We use the ocean color product imagery derived from measurements of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) to evaluate and validate the new global land mask implemented in the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system, and demonstrate the improvements in the derived global ocean color data coverage. Results show that when using the new proposed land mask the accuracy of global ocean color data coverage is significantly improved over coastal and inland waters. The new land mask more accurately represents the current global land coverage status, providing more complete and consistent global land/water coverage data set for ocean color remote sensing and for various other satellite Earth observing applications.

Satellite-measured water properties in high altitude Lake Tahoe

It has been difficult in satellite remote sensing to derive accurate water optical, biological, and biogeochemical products over high-altitude inland waters due to issues in satellite data processing (i.e., atmospheric correction). In this study, we demonstrate that accurate normalized water-leaving radiance spectra nLw(λ) can be derived for a high-altitude lake, Lake Tahoe, using improved Rayleigh radiance computations (Wang, M., Opt. Express, 24, 12414–12429, 2016) which accurately account for water surface altitude effects in the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system. Satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) between 2012 and 2018 are used to evaluate and validate satellite-derived nLw(λ) spectra, and to quantitatively characterize water properties in the lake. Results show that VIIRS-derived nLw(λ) spectra are quite comparable with those from the in situ measurements. Subsequent retrievals of water biological and biogeochemical products show that chlorophyll-a (Chl-a) concentration and Secchi depth (SD) are reasonably well-estimated, and captured normal seasonal variations in the lake, e.g., the annual highest Chl-a and SD normally occur in the winter while the lowest occur in the summer, which is consistent with in situ measurements. Interannual variability of these water quality parameters is also observed. In particular, Lake Tahoe experienced a significant environmental anomaly associated with an extreme weather condition event in 2017, showing considerably decreased nLw(λ) at the spectral bands of 410, 443, and 486 nm, and significantly reduced SD values in the entire lake. The low SD measurements from VIIRS are consistent with in situ observations. Following the event in the 2017–2018 winter, Lake Tahoe recovered and returned to its typical conditions in spring 2018.

Statistical evaluation of satellite ocean color data retrievals

We develop a statistical approach to evaluate the performance of the ocean color data processing system for satellite-derived ocean color data products based on temporal stability of retrievals. We use the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system to obtain the normalized water-leaving reflectance ρwN(λ) spectra from the Visible Infrared Imaging Radiometer Suite (VIIRS) measurements. The deviations of ρwN(λ) spectra from temporally and spatially averaged values are investigated, and the statistics with respect to various retrieval parameters are collected, including the solar-sensor geometry (solar-zenith, sensor-zenith, and relative azimuth angles), and various ancillary data (surface wind speed, surface atmospheric pressure, water vapor amount, and ozone concentration). The performance of MSL12 is also evaluated with respect to other intermediate retrieval parameters. The study shows that MSL12 produces statistically consistent VIIRS ocean color retrievals in the global open ocean, with respect to retrieval geometry parameters, as well as the ancillary inputs.

Filling the Gaps of Missing Data in the Merged VIIRS SNPP/NOAA-20 Ocean Color Product Using the DINEOF Method

The Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) and National Oceanic and Atmospheric Administration (NOAA)-20 has been providing a large amount of global ocean color data, which are critical for monitoring and understanding of ocean optical, biological, and ecological processes and phenomena. 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, high sun glint, and cloud, etc. Merging VIIRS ocean color products derived from the SNPP and NOAA-20 significantly increases the spatial coverage of daily images. The two VIIRS sensors on the SNPP and NOAA-20 have similar sensor characteristics, and global ocean color products are generated using the same Multi-Sensor Level-1 to Level-2 (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. Merging VIIRS SNPP and NOAA-20 ocean color data almost removes the gaps of missing pixels due to high sensor-zenith angles and high sun glint contamination, and also significantly reduces the gaps due to cloud cover. However, there are still gaps of missing pixels in the merged ocean color data. In this study, the Data Interpolating Empirical Orthogonal Functions (DINEOF) are applied on the merged VIIRS SNPP/NOAA-20 global Level-3 ocean color data to completely reconstruct the missing pixels. Specifically, DINEOF is applied to 30 days of daily merged global Level-3 chlorophyll-a (Chl-a) data of 9-km spatial resolution from 19 June to 18 July 2018. To quantitatively evaluate the accuracy of the DINEOF reconstructed data, a set of valid pixels are intentionally treated as “missing pixels”, so that reconstructed data can be compared with the original data. Results show that mean ratios of the reconstructed/original are 1.012, 1.012, 1.015, and 0.997 for global ocean, oligotrophic waters, deep waters, and coastal and inland waters, respectively. The corresponding standard deviation (SD) of the ratios are 0.200, 0.164, 0.182, and 0.287, respectively. Gap-filled daily Chl-a images reveal many large-scale and meso-scale ocean features that are invisible in the original SNPP or NOAA-20 Chl-a images. It is also demonstrated that the gap-filled data based on the merged products show more details in the dynamic ocean features than those based on SNPP or NOAA-20 alone.

Atmospheric Correction Using the Information From the Short Blue Band

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 ( $\lambda $ ) 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) nLw (410) nLw (410) information. Specifically, for cases with nLw (410) nLw (410) $\ge 0$ [e.g., assuming nLw (410) = 0], thereby removing unphysical retrievals for VIIRS-derived nLw ( $ \lambda $ ) spectra. Using this technique, VIIRS-derived nLw ( $ \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 VIIRS nLw ( $\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.

VIIRS-derived ocean color product using the imaging bands

A technique and implementation for deriving ocean color product using the imaging band (I-band) measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) have been developed. The three VIIRS-SNPP image bands (638, 862, and 1600nm) with spatial resolution of 375m are much better than the VIIRS moderate (M) resolution bands of 750m, which have been used for routinely producing VIIRS global ocean color products. In particular, the high spatial resolution of VIIRS-derived normalized water-leaving radiance at the wavelength of 638nm (I1 band) nLw(638) can provide useful information about water optical, biological, and biogeochemical properties over turbid coastal and inland waters. In addition, VIIRS-derived nLw(638) data provide an important spectral point and coverage along with nLw(λ) from the seven VIIRS M-bands at wavelengths of 410, 443, 486, 551, 671, 745, and 862nm, particularly over turbid coastal and inland waters. In this paper, we describe our effort to develop a technique for deriving VIIRS nLw(λ) spectra for both M-bands and I-bands using the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system. Some examples of VIIRS-derived nLw(638) are provided and discussed, showing the usefulness of this added capability for providing water properties over global turbid coastal and inland waters. Therefore, MSL12 is now capable of routinely and efficiently producing VIIRS nLw(638) for both spatial resolutions of 375 and 750m, along with nLw(λ) spectra at VIIRS M1–M7 with the spatial resolution of 750m.

VIIRS-derived chlorophyll-a using the ocean color index method

Abstract An implementation approach using the ocean color index (OCI)-based chlorophyll-a (Chl-a) algorithm for the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) has been developed. The OCI Chl-a algorithm for satellite-derived Chl-a data was originally developed by Hu, Lee, and Franz (2012) (J. Geophys. Res., 117, C01011, doi: 01010.01029/02011JC007395) for the Moderate Resolution Imaging Spectroradiometer (MODIS). It uses two Chl-a algorithms, i.e., the color index (CI)-based (reflectance difference-based) algorithm for oligotrophic waters and the usual ocean chlorophyll-type (OCx)-based (reflectance ratio-based) algorithm (e.g., OC3M for MODIS and OC3V for VIIRS), and merges the two algorithms for different Chl-a range applications (named OCI algorithm). In this study, we use the in situ Marine Optical Buoy (MOBY) optics data to demonstrate conclusively that using the CI-based Chl-a algorithm can significantly improve VIIRS Chl-a data over oligotrophic waters with much reduced data noise from instrument calibration and the imperfect atmospheric correction. Using the VIIRS-measured global Chl-a data derived from the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system, we have developed the CI-based algorithm specifically for VIIRS, and further improved the two Chl-a algorithms merging method using the blue-green reflectance ratio values. Extensive evaluation results show that the new OCI Chl-a algorithm for VIIRS can produce consistent Chl-a data compared with those from the OC3V algorithm. In particular, the data transition between the CI-based and OC3V-based Chl-a algorithm is quite smooth, and there are no obvious discontinuities in VIIRS-derived Chl-a data. The new OCI-based Chl-a algorithm has been implemented in MSL12 for routine production of VIIRS global Chl-a data.

Bering Sea optical and biological properties from MODIS

The Bering Sea is characterized by unique bio-optical properties, which cause unsatisfactory performance of global ocean color algorithms for retrieval of chlorophyll-a (Chl-a). This study evaluates the normalized water-leaving radiance nLw(λ) and Chl-a in the eastern Bering Sea that are derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua by comparing them to in situ data. MODIS-Aqua ocean color products were derived using the NOAA Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system. The MODIS-derived nLw(λ) showed good agreement with in situ-measured nLw(λ). The mean ratios between them for wavelengths 412, 443, 488, and 551nm ranged from 1.097 to 1.280, with reasonably accurate blue-green radiance ratios in nLw(λ) that were used as input for deriving Chl-a. However, compared to in situ data, existing global and regional Chl-a algorithms either overestimate or underestimate Chl-a in the eastern Bering Sea. Therefore, we propose a new algorithm for estimating Chl-a using a blended approach that was tested and applied to MODIS-Aqua images. The histogram distributions of MODIS-Aqua-derived and in situ-measured Chl-a data show that Chl-a data derived using the new algorithm agree reasonably well to in situ measurements. Annual, seasonal, and monthly composite nLw(λ) and Chl-a images are produced for the period of 2003 to 2013 in order to interpret the long-term spatial and temporal patterns of nLw(λ) and Chl-a. The nLw(λ) spectra show strong spectral dependence on seasonal variability with distinct spatial patterns. Although strong seasonal and interannual variability has been observed in Chl-a, there is no apparent trend of either increase or decrease in phytoplankton biomass associated with variability in the physical environment for the 11years of the study period.