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  • 1
    Publication Date: 2019-07-19
    Description: We describe the development of a new suite of aerosol models for the retrieval of atmospheric and oceanic optical properties from the SeaWiFs and MODIS sensors, including aerosol optical thickness (tau), angstrom coefficient (alpha), and water-leaving radiance (L(sub w)). The new aerosol models are derived from Aerosol Robotic Network (AERONET) observations and have bimodal lognormal distributions that are narrower than previous models used by the Ocean Biology Processing Group. We analyzed AERONET data over open ocean and coastal regions and found that the seasonal variability in the modal radii, particularly in the coastal region, was related to the relative humidity, These findings were incorporated into the models by making the modal radii, as well as the refractive indices, explicitly dependent on relative humidity, From those findings, we constructed a new suite of aerosol models. We considered eight relative humidity values (30%, 50%, 70%, 75%, 80%, 85%, 90%. and 95%) and, for each relative humidity value, we constructed ten distributions by varying the fine-mode fraction from zero to 1. In all. 80 distributions (8Rh x 10 fine-mode fractions) were created to process the satellite data. We. also assumed that the coarse-mode particles were nonabsorbing (sea salt) and that all observed absorptions were entirely due to fine-mode particles. The composition of fine mode was varied to ensure that the new models exhibited the same spectral dependence of single scattering albedo as observed in the AERONET data,
    Keywords: Optics
    Type: Applied Optics; 49; 29; 5545-5560
    Format: text
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  • 2
    Publication Date: 2019-07-13
    Description: The efforts to improve the data quality for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data products have continued, following the third reprocessing of the global data set in May 2000. Analyses have been ongoing to address all aspects of the processing algorithms, particularly the calibration methodologies, atmospheric correction, and data flagging and masking. All proposed changes were subjected to rigorous testing, evaluation and validation. The results of these activities culminated in the fourth reprocessing, which was completed in July 2002. The algorithm changes, which were implemented for this reprocessing, are described in the chapters of this volume. Chapter 1 presents an overview of the activities leading up to the fourth reprocessing, and summarizes the effects of the changes. Chapter 2 describes the modifications to the on-orbit calibration, specifically the focal plane temperature correction and the temporal dependence. Chapter 3 describes the changes to the vicarious calibration, including the stray light correction to the Marine Optical Buoy (MOBY) data and improved data screening procedures. Chapter 4 describes improvements to the near-infrared (NIR) band correction algorithm. Chapter 5 describes changes to the atmospheric correction and the oceanic property retrieval algorithms, including out-of-band corrections, NIR noise reduction, and handling of unusual conditions. Chapter 6 describes various changes to the flags and masks, to increase the number of valid retrievals, improve the detection of the flag conditions, and add new flags. Chapter 7 describes modifications to the level-la and level-3 algorithms, to improve the navigation accuracy, correct certain types of spacecraft time anomalies, and correct a binning logic error. Chapter 8 describes the algorithm used to generate the SeaWiFS photosynthetically available radiation (PAR) product. Chapter 9 describes a coupled ocean-atmosphere model, which is used in one of the changes described in Chapter 4. Finally, Chapter 10 describes a comparison of results from the third and fourth reprocessings along the US. Northeast coast.
    Keywords: Optics
    Type: NASA/TM-2003-206892/VOL22 , Rept-2003-01915-0/VOL22 , NAS 1.15:206892/VOL22 , (ISSN 1522-8789)
    Format: application/pdf
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  • 3
    Publication Date: 2019-07-13
    Description: The Geostationary Ocean Colour Imager (GOCI) instrument, on Koreas Communications, Oceans, and Meteorological Satellite (COMS), can produce a spectral artefact arising from the motion of clouds the cloud is spatially shifted and the amount of shift varies by spectral band. The length of time it takes to acquire all eight GOCI bands for a given slot (portion of a scene) is sucient to require that cloud motion be taken into account to fully mask or correct the eects of clouds in all bands. Inter-band correlations can be used to measure the amount of cloud shift, which can then be used to adjust the cloud mask so that the union of all shifted masks can act as a mask for all bands. This approach reduces the amount of masking required versus a simple expansion of the mask in all directions away from clouds. Cloud motion can also aect regions with unidentied clouds thin or fractional clouds that evade the cloud identication process yielding degraded quality in retrieved ocean colour parameters. Areas with moving and unidentied clouds require more elaborate masking algo-rithms to remove these degraded retrievals. Correction for the eects of moving fractional clouds may also be possible. The cloud shift information can be used to determine cloud motion and thus wind at the cloud levels on sub-minute timescales. The benecial and negative eects of moving clouds should be con-sidered for any ocean colour instrument design and associated data processing plans.
    Keywords: Earth Resources and Remote Sensing; Meteorology and Climatology
    Type: GSFC-E-DAA-TN41630 , International Journal of Remote Sensing (ISSN 0143-1161) (e-ISSN 1366-5901); 37; 20; 4948-4963
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  • 4
    Publication Date: 2022-05-25
    Description: © The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Applications 28 (2018): 749-760, doi: 10.1002/eap.1682.
    Description: The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite‐based sensors can repeatedly record the visible and near‐infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100‐m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short‐wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14‐bit digitization, absolute radiometric calibration 〈2%, relative calibration of 0.2%, polarization sensitivity 〈1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3‐d repeat low‐Earth orbit could sample 30‐km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.
    Description: National Center for Ecological Analysis and Synthesis (NCEAS); National Aeronautics and Space Administration (NASA) Grant Numbers: NNX16AQ34G, NNX14AR62A; National Ocean Partnership Program; NOAA US Integrated Ocean Observing System/IOOS Program Office; Bureau of Ocean and Energy Management Ecosystem Studies program (BOEM) Grant Number: MC15AC00006
    Keywords: Aquatic ; Coastal zone ; Ecology ; Essentail biodiversity variables ; H4 imaging ; Hyperspectral ; Remote sensing ; Vegetation ; Wetland
    Repository Name: Woods Hole Open Access Server
    Type: Article
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