NASA NTRS · 20140017187
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Meister, Gerhard. 2012-12-19. Instrument Characterization for Ocean Color Remote Sensing. https://ntrs.nasa.gov/citations/20140017187
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The Ocean Color Instrument (OCI) on NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a hyperspectral Earth imager with a spatial resolution of 1 km x 1 km and a spectral resolution of 5 nm in 2.5 nm steps over 342-887 nm. In addition, OCI provides 7 discrete bands in the 940-2260 nm Short-Wave InfraRed (SWIR) range. The front-end optical imager is a rotating mirror-based system that images the ground scene onto a slit with an instantaneous field of view of 16 km x 1 km. For the SWIR bands, the slit-image is re-imaged onto a 16x1 micro-lens array that effectively acts as the focal plane since each lens element is fiber coupled to wavelength filtered InGaAs and HgCdTe Photo Diodes (PDs). The pulse response of the detection system is critical to OCI SWIR performance. We find that PDs introduce an inherent slow tail in the pulse response due to slow diffusion moving carriers in their n and p regions. We show that this introduces response errors ranging from 1 down to 0.01 % for up to tens of science pixels after the pulse depending on the PD design and materials. It is shown that the response is distinctly different for the InGaAs and HgCdTe PDs. We explain how the front-end design can further increase this error. Finally, we detail the cause of the slow pulse response tail, how to model it, its impact on OCI performance and how it is characterized and corrected to meet OCI requirements.
A global in situ data set for validation of ocean colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient, and total suspended matter. Data were obtained from multi-project archives acquired via open internet services or from individual projects acquired directly from data providers. Methodologies were implemented for homogenization, quality control, and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points, after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 148 432 rows, with each row representing a unique station in space and time (cf. 136 250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented and it is also possible to analyse each set of data separately.
In 2018, a working group sponsored by the NASA Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) project, in conjunction with the International Ocean Colour Coordinating Group (IOCCG), European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and Japan Aerospace Exploration Agency (JAXA), was assembled with the aim to develop community consensus on multiple methods for measuring aquatic primary productivity used for satellite validation and model synthesis. A workshop to commence the working group efforts was held December 5–7, 2018, at the University Space Research Association headquarters in Columbia, MD, USA, bringing together 26 active researchers from 16 institutions. In this document, we discuss and develop the workshop findings as they pertain to primary productivity measurements, including the essential issues, nuances, definitions, scales, uncertainties, and ultimately best practices for data collection across multiple methodologies.