Abstract
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.
Highlights
Ocean color data are critical for monitoring and understanding of water optical, biological, and ecological processes and phenomena, and it is an important source of input data for physical and biogeochemical ocean models [1]
On 18 November 2017, the follow-up Visible Infrared Imaging Radiometer Suite (VIIRS) sensor housed in the National Oceanic and Atmospheric Administration (NOAA)-20 satellite was launched as the first of four sensors in the Joint Polar Satellite System (JPSS) satellite series, and global ocean color data from NOAA-20 are being routinely produced
For both Suomi National Polar-Orbiting Partnership (SNPP) and NOAA-20, chlorophyll-a (Chl-a) concentration [5,6,7], normalized water-leaving radiance spectra nLw(λ) [8,9], including new nLw(λ) data using VIIRS imaging bands [10], and water diffuse attenuation coefficient at the wavelength of 490 nm Kd(490) and at the domain of photosynthetically available radiation (PAR) Kd(PAR) [11,12], are all generated as standard VIIRS ocean color products
Summary
Ocean color data are critical for monitoring and understanding of water optical, biological, and ecological processes and phenomena, and it is an important source of input data for physical and biogeochemical ocean models [1]. For either VIIRS-SNPP or VIIRS-NOAA-20, there are always missing pixels in the VIIRS-measured ocean color data imageries due to cloud cover and various other reasons, e.g., strong sun glint contamination, dust storms, very large solar- and sensor-zenith angles, etc. The overlap of the spatial coverages of the two sensors automatically fills each other’s gaps caused by high sensor-zenith angles and high sun glint contamination, and it significantly reduces the missing pixels in the merged images.
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