Suomi NPP VIIRS prelaunch and on‐orbit geometric calibration and characterization

Wolfe, Robert E.; Lin, Guoqing; Nishihama, Masahiro; Tewari, K.; Tilton, James C.; Isaacman, Alice R.

doi:10.1002/jgrd.50873

Cited by 164 publications

(158 citation statements)

References 15 publications

Supporting

Mentioning

134

Contrasting

Unclassified

Order By: Relevance

“…VIIRS applies bow-tie deletion to remove duplicated pixels in the off-nadir part of the scan [33,34]. The geolocation error for the I and M bands is around 1% of the pixel size in mean and less than 20% of the pixel size in root mean squared error [35].…”

Section: Instrument Inputmentioning

confidence: 99%

Sea and Freshwater Ice Concentration from VIIRS on Suomi NPP and the Future JPSS Satellites

2016

View full text Add to dashboard Cite

Information on ice is important for shipping, weather forecasting, and climate monitoring. Historically, ice cover has been detected and ice concentration has been measured using relatively low-resolution space-based passive microwave data. This study presents an algorithm to detect ice and estimate ice concentration in clear-sky areas over the ocean and inland lakes and rivers using high-resolution data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar Orbiting Partnership (S-NPP) and on future Joint Polar Satellite System (JPSS) satellites, providing spatial detail that cannot be obtained with passive microwave data. A threshold method is employed with visible and infrared observations to identify ice, then a tie-point algorithm is used to determine the representative reflectance/temperature of pure ice, estimate the ice concentration, and refine the ice cover mask. The VIIRS ice concentration is validated using observations from Landsat 8. Results show that VIIRS has an overall bias of´0.3% compared to Landsat 8 ice concentration, with a precision (uncertainty) of 9.5%. Biases and precision values for different ice concentration subranges from 0% to 100% can be larger.

show abstract

Section: Instrument Inputmentioning

confidence: 99%

Sea and Freshwater Ice Concentration from VIIRS on Suomi NPP and the Future JPSS Satellites

2016

View full text Add to dashboard Cite

show abstract

“…VIIRS has a similar acrosstrack scanning pattern to MODIS, although with 16 M-band detectors per scan. However, VIIRS incorporates several design features to reduce the bow-tie distortion (Wolfe et al, 2012(Wolfe et al, , 2013. Firstly, the VIIRS on-board native pixel size is actually smaller than the nominal M-band size in the acrosstrack direction.…”

Section: Perspectives On Product Regularity and Modis/viirs Continuitymentioning

confidence: 99%

Implications of MODIS bow-tie distortion on aerosol optical depth retrievals, and techniques for mitigation

2015

View full text Add to dashboard Cite

Abstract. The scan geometry of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, combined with the Earth's curvature, results in a pixel shape distortion known as the "bow-tie effect". Specifically, sensor pixels near the edge of the swath are elongated along-track and across-track compared to pixels near the centre of the swath, resulting in an increase of pixel area by up to a factor of ∼ 9 and, additionally, the overlap of pixels acquired from consecutive scans. The Deep Blue and Dark Target aerosol optical depth (AOD) retrieval algorithms aggregate sensor pixels and provide level 2 (L2) AOD at a nominal horizontal pixel size of 10 km, but the bow-tie distortion means that they also suffer from this size increase and overlap. This means that the spatial characteristics of the data vary as a function of satellite viewing zenith angle (VZA) and, for VZA > 30• , corresponding to approximately 50 % of the data, are areally enlarged by a factor of 50 % or more compared to this nominal pixel area and are not spatially independent of each other. This has implications for retrieval uncertainty and aggregated statistics, causing a narrowing of AOD distributions near the edge of the swath, as well as for data comparability from the application of similar algorithms to sensors without this level of bow-tie distortion. Additionally, the pixel overlap is not obvious to users of the L2 aerosol products because only pixel centres, not boundaries, are provided within the L2 products. A two-step procedure is proposed to mitigate the effects of this distortion on the MODIS aerosol products. The first (simple) step involves changing the order in which pixels are aggregated in L2 processing to reflect geographical location rather than scan order, which removes the bulk of the overlap between L2 pixels and slows the rate of growth of L2 pixel size vs. VZA. This can be achieved without significant changes to existing MODIS processing algorithms. The second step involves additionally changing the number of sensor pixels aggregated across-track as a function of VZA, which preserves L2 pixel size at around 10 km × 10 km across the whole swath but would require algorithmic quality assurance tests to be re-evaluated. Both of these steps also improve the extent to which the pixel locations a user would infer from the L2 data products represent the actual spatial extent of the L2 pixels.

show abstract

“…Therefore, alternative approaches using remote targets have been developed, with the general idea of calculating the BBR offset between any two bands by measuring the shift between their images of the same target. Various approaches using Earth view data [8][9][10] or the lunar images have been proposed [11][12][13]. Compared to the Earth view data, lunar data are available much less frequently and cover a limited scan-angle range, because the Moon is only viewed at a fixed scan angle corresponding to the space view (SV) port.…”

Section: Remote Sens 2016 8 27mentioning

confidence: 99%

Improved Band-to-Band Registration Characterization for VIIRS Reflective Solar Bands Based on Lunar Observations

Wang

Xiong

2015

Remote Sensing

View full text Add to dashboard Cite

Spectral bands of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite are spatially co-registered. The accuracy of the band-to-band registration (BBR) is one of the key spatial parameters that must be characterized. Unlike its predecessor, the Moderate Resolution Imaging Spectroradiometer (MODIS), VIIRS has no on-board calibrator specifically designed to perform on-orbit BBR characterization. To circumvent this problem, a BBR characterization method for VIIRS reflective solar bands (RSB) based on regularly-acquired lunar images has been developed. While its results can satisfactorily demonstrate that the long-term stability of the BBR is well within˘0.1 moderate resolution band pixels, undesired seasonal oscillations have been observed in the trending. The oscillations are most obvious between the visible/near-infrared bands and short-/middle wave infrared bands. This paper investigates the oscillations and identifies their cause as the band/spectral dependence of the centroid position and the seasonal rotation of the lunar images over calibration events. Accordingly, an improved algorithm is proposed to quantify the rotation and compensate for its impact. After the correction, the seasonal oscillation in the resulting BBR is reduced from up to 0.05 moderate resolution band pixels to around 0.01 moderate resolution band pixels. After removing this spurious seasonal oscillation, the BBR, as well as its long-term drift are well determined.

show abstract

Suomi NPP VIIRS prelaunch and on‐orbit geometric calibration and characterization

Cited by 164 publications

References 15 publications

Sea and Freshwater Ice Concentration from VIIRS on Suomi NPP and the Future JPSS Satellites

Sea and Freshwater Ice Concentration from VIIRS on Suomi NPP and the Future JPSS Satellites

Implications of MODIS bow-tie distortion on aerosol optical depth retrievals, and techniques for mitigation

Improved Band-to-Band Registration Characterization for VIIRS Reflective Solar Bands Based on Lunar Observations

Contact Info

Product

Resources

About