SNPP VIIRS RSB Earth View Reflectance Uncertainty

The Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) satellite uses its 14 reflective solar bands to passively collect solar radiant energy reflected off the Earth. The Level 1 product is the geolocated and radiometrically calibrated top-of- the-atmosphere solar reflectance. The absolute radiometric uncertainty associated with this product includes contributions from the noise associated with measured detector digital counts and the radiometric calibration bias. Here, we provide a detailed algorithm for calculating the estimated standard deviation of the retrieved top-of-the-atmosphere spectral solar radiation reflectance

Pre-Launch Radiometric Characterization of JPSS-1 VIIRS Thermal Emissive Bands

Pre-launch characterization and calibration of the thermal emissive spectral bands on the Joint Polar Satellite System (JPSS-1) Visible Infrared Imaging Radiometer Suite (VIIRS) is critical to ensure high quality data products for environmental and climate data records post-launch. A comprehensive test program was conducted at the Raytheon El Segundo facility in 2013-2014, including extensive environmental testing. This work is focused on the thermal band radiometric performance and stability, including evaluation of a number of sensor performance metrics and estimation of uncertainties. Analysis has shown that JPSS-1 VIIRS thermal bands perform very well in relation to their design specifications, and comparisons to the Suomi National Polar-orbiting Partnership (SNPP) VIIRS instrument have shown their performance to be comparable

Cross-Calibration of S-NPP VIIRS Moderate Resolution Reflective Solar Bands Against MODIS Aqua over Dark Water Scenes

The Visible Infrared Imaging Radiometer Suite (VIIRS) is being used to continue the record of Earth Science observations and data products produced routinely from National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. However, the absolute calibration of VIIRS's reflected solar bands is thought to be biased, leading to offsets in derived data products such as aerosol optical depth (AOD) as compared to when similar algorithms are applied to different sensors. This study presents a cross-calibration of these VIIRS bands against MODIS Aqua over dark water scenes, finding corrections to the NASA VIIRS Level 1 (version 2) reflectances between approximately +1 and 7 % (dependent on band) are needed to bring the two into alignment (after accounting for expected differences resulting from different band spectral response functions), and indications of relative trending of up to 0.35 % per year in some bands. The derived calibration gain corrections are also applied to the VIIRS reflectance and then used in an AOD retrieval, and they are shown to decrease the bias and total error in AOD across the mid-visible spectral region compared to the standard VIIRS NASA reflectance calibration. The resulting AOD bias characteristics are similar to those of NASA MODIS AOD data products, which is encouraging in terms of multi-sensor data continuity

CIRA annual report FY 2016/2017

Reporting period April 1, 2016-March 31, 2017

CIRA annual report FY 2015/2016

Reporting period April 1, 2015-March 31, 2016

CIRA annual report FY 2017/2018

Reporting period April 1, 2017-March 31, 2018

PACE Technical Report Series, Volume 7: Ocean Color Instrument (OCI) Concept Design Studies

Extending OCI hyperspectral radiance measurements in the ultraviolet to 320 nm on the blue spectrograph enables quantitation of atmospheric total column ozone (O3) for use in ocean color atmospheric correction algorithms. The strong absorption by atmospheric ozone below 340 nm enables the quantification of total column ozone. Other applications are possible but were not investigated due to their exploratory nature and lower priority.The first step in the atmospheric correction processing, which converts top-of-the-atmosphere radiances to water-leaving radiances, is removal of the absorbance by atmospheric trace gases such as water vapor, oxygen, ozone and nitrogen dioxide. Details of the atmospheric correction process currently used by the Ocean Biology Processing Group (OBPG) and will be employed for PACE with appropriate modifications, are described by Mobley et al. [2016]. Atmospheric ozone absorbs within the visible to near-infrared spectrum between ~450 nm and 800nm and most appreciably between 530 nm and 650 nm, a spectral region critical for maintaining NASA's chlorophyll-a climate data record and for PACE algorithms planned to characterize phytoplankton community composition and other ocean color products.While satellite-based observations will likely be available during PACE's mission lifetime, the difference in acquisition time with PACE, the coarseness in their spatial resolution, and differences in viewing geometries will introduce significant levels of uncertainties in PACE ocean color data products

Uncertainties in Retrieval of Remote Sensing Reflectance from Ocean Color Satellite Observations

Ocean Color radiometry uses remote sensing to interpret ocean dynamics by retrieving remote sensing reflectance () from satellite imagery at different scales and over different time periods. spectrum characterizes the ocean color that we observe, and from which we can discern concentrations of chlorophyll, organic and inorganic particles, and carbon fluxes in the ocean and atmosphere. is derived from the total radiance at the top of the atmosphere (TOA). However, it only represents up to ten percent of the total signal. Hence, the retrieval of from the total radiance at TOA involves the application of atmospheric correction (AC) algorithms, which include accurate modeling of Rayleigh and aerosol scattering, glint, and water variability. Each of these components yields uncertainties in the retrieved value of , especially in the blue bands. It is important to understand the main sources of uncertainties in , as uncertainties propagate into the retrieval of water parameters, which in turn inform climate models. In this study, a model was developed that quantifies the uncertainties of the main components in the current AC algorithm and used to analyze holistically the influence of these components on the uncertainties spatially and temporally in different water types taking advantage of the spectral differences between the components. The uncertainties were determined by comparing satellite and in situ data, with the in situ data obtained from the AErosol RObotic NETwork - Ocean Color (AERONET-OC) around the Northern Hemisphere and the Marine Optical BuoY (MOBY), Lanai, Hawaii. The satellite sensor data are from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the S-NPP platform, the Ocean and Land Colour Instruments (OLCI) on Sentinel 3A and 3B, and the Operational Land Imager (OLI) on Landsat 8. Results showed that the Rayleigh component (molecular scattering and surface effects) is the main source of uncertainties for all water types, followed by water variability, which is more influential in coastal areas. The contributions of other components, including aerosol scattering, are usually smaller. In addition, wind speed ranges can influence results, especially in coastal regions. Across spatial scales, water variability played a dominant role in uncertainty and increased proportionally to the ground sampling distance

Exploring Himawari-8 geostationary observations for the advanced coastal monitoring of the Great Barrier Reef

Larissa developed an algorithm to enable water-quality assessment within the Great Barrier Reef (GBR) using weather satellite observations collected every 10 minutes. This unprecedented temporal resolution records the dynamic nature of water quality fluctuations for the entire GBR, with applications for improved monitoring and management