Improved VIIRS and MODIS SST Imagery

Gladkova, Irina; Ignatov, Alexander; Shahriar, Fazlul; Kihai, Yury; Hillger, Donald W.; Petrenko, B.

doi:10.3390/rs8010079

Cited by 29 publications

(13 citation statements)

References 12 publications

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“…The possible impact of bow tie effect and aggregation schemes (Gladkova et al, ) was also examined. For VIIRS scan angles less than 32°, three detector footprints are aggregated in the cross‐track direction to form one VIIRS pixel.…”

Section: Discussionmentioning

confidence: 99%

Intercomparison Between VIIRS and CrIS by Taking Into Account the CrIS Subpixel Cloudiness and Viewing Geometry

Gong

et al. 2018

JGR Atmospheres

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The Cross‐track Infrared Sounder (CrIS) onboard the Suomi National Polar‐orbiting Partnership (SNPP) has high radiometric accuracy, which can be used to intercompare and understand the radiometric performance of the thermal emissive bands of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the same platform. Previous studies usually selected uniform scenes to avoid potential uncertainties due to geolocation errors. This study focuses on all scenes (including nonuniform scenes), as well as CrIS subpixel cloudiness, local zenith angle and surface type, and their impacts on VIIRS/CrIS intercomparisons. A geolocation error correction scheme is applied to mitigate the geolocation mismatch between CrIS and VIIRS. Intercomparison results of three days from 19 to 21 September 2016, a quarterly warm up/cool down calibration period, show overall good agreement in terms of radiance biases (less than 0.2 K in terms of brightness temperature) between VIIRS and CrIS, for scene temperatures between 220 and 300 K; however, larger biases are evident outside of this range. The three factors—CrIS subpixel cloudiness, local zenith angle, and surface type—show weak impacts on VIIRS radiance biases. Both clear‐sky and overcast conditions have slightly different radiance biases from other partially cloudy conditions because of more contributions from colder or warmer scenes. The time series show the impact of warm up/cool down and the effectiveness of the correction algorithm for reprocessing VIIRS M15 and M16 data sets. The methods and tools can be applied to monitor and validate other infrared imager/sounder duos onboard the same platform.

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Section: Discussionmentioning

confidence: 99%

Intercomparison Between VIIRS and CrIS by Taking Into Account the CrIS Subpixel Cloudiness and Viewing Geometry

Gong

et al. 2018

JGR Atmospheres

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Section: The Visible Infrared Imaging Radiometer Suite (Viirs) Instrumentioning

confidence: 99%

“…This enabled the VIIRS Day/Night band to detect low cloud structures that revealed the true center of low-level circulation associated with Hurricane Flossie. The DNB has been proved to be useful for many applications including tropical cyclone analysis and forecasting, night fire products, nighttime aerosol retrievals, mapping of city lights/power outage detection, boat detections, and detection of airglow gravity waves, among other events [31][32][33][34][35][36]. In addition to the EDR imagery products, the VIIRS product suite contains a wide variety of geophysical products (i.e., EDRs), include those for atmosphere (e.g., aerosol optical thickness, AOT; aerosol detection products, ADP), land (e.g., variety of vegetation indices, VI; green vegetation fraction, GVF; land surface temperature, LST), ocean (e.g., sea surface temperature, SST; ocean color, OC), and cryosphere (e.g., ice surface temperature, IST; sea ice thickness, concentration; snow fraction, polar winds).…”

Section: The Visible Infrared Imaging Radiometer Suite (Viirs) Instrumentioning

confidence: 99%

“…Figures 5 and 6 show two important ocean EDR products, the SST and the ocean color chlorophyll-a (OCC). The SST product is derived using the Advanced Clear-Sky Processor for Ocean (ACSPO) SST algorithm [35,50,51]. The algorithm, a regression algorithm trained with in situ measurements, uses separate sets of VIIRS channels for day and night and retrieves SST at every cloud-free VIIRS pixel.…”

Section: Productmentioning

confidence: 99%

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An Overview of the Science Performances and Calibration/Validation of Joint Polar Satellite System Operational Products

et al. 2019

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The Suomi National Polar-orbiting Partnership (S-NPP) satellite, launched in October 2011, initiated a series of the next-generation weather satellites for the National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) program. The JPSS program at the Center for Satellite Applications and Research (JSTAR) leads the development of the algorithms, the calibration and validation of the products to meet the specified requirements, and long-term science performance monitoring and maintenance. All of the S-NPP products have been validated and are in successful operation. The recently launched JPSS-1 (renamed as NOAA-20) satellite is producing high-quality data products that have been available from S-NPP, along with additional products, as a direct result of the instrument upgrades and science improvements. This paper presents an overview of the JPSS product suite, the performance metrics achieved for the S-NPP, and the utilization of the products by NOAA stakeholders and user agencies worldwide. The status of NOAA-20 science data products and ongoing calibration/validation (Cal/Val) efforts are discussed for user awareness. In addition, operational implementation statuses of JPSS enterprise (multisensor and multiplatform) science algorithms for product generation and science product reprocessing efforts for the S-NPP mission are discussed.

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“…However, IR-based satellite measurements are required to undergo a cloud removal procedure prior to SST estimation because of their inability to observe the surface through clouds [18][19][20][21][22]. SST has been derived from satellite-based IR observations since 1981, with a typical spatial resolution of 1-4 km [1,[23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Variational Based Estimation of Sea Surface Temperature from Split-Window Observations of INSAT-3D/3DR Imager

Thapliyal

2020

Remote Sensing

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Infrared (IR) radiometers from geostationary (GEO) satellites have an advantage over low-earth orbiting (LEO) satellites as they provide continuous observations to monitor the diurnal variations in the sea surface temperature (SST), typically better than 30-minute interval. However, GEO satellite observations suffer from significant diurnal and seasonal biases arising due to varying sun-earth-satellite geometry, leading to biases in SST estimates from conventional non-linear regression-based algorithms (NLSST). The midnight calibration issue occurring in GEO sensors poses a different challenge altogether. To mitigate these issues, we propose SST estimation from split-window IR observations of INSAT-3D and 3DR Imagers using One-Dimensional Variational (1DVAR) scheme. Prior to SST estimation, the bias correction in Imager observations is carried out using cumulative density function (CDF) matching. Then NLSST and 1DVAR algorithms were applied on six months of INSAT-3D/3DR observations to retrieve the SST. For the assessment of the developed algorithms, the retrieved SST was validated against in-situ SST measurements available from in-situ SST Quality Monitor (iQuam) for the study period. The quantitative assessment confirms the superiority of the 1DVAR technique over the NLSST algorithm. However, both the schemes under-estimate the SST as compared to in-situ SST, which may be primarily due to the differences in the retrieved skin SST versus bulk in-situ SST. The 1DVAR scheme gives similar accuracy of SST for both INSAT-3D and 3DR with a bias of −0.36 K and standard deviation (Std) of 0.63 K. However, the NLSST algorithm provides slightly less accurate SST with bias (Std) of −0.18 K (0.87 K) for INSAT-3DR and −0.27 K (0.95 K) for INSAT-3D. Both the NLSST and 1DVAR algorithms are capable of producing the accurate thermal gradients from the retrieved SST as compared to the gradients calculated from daily Multiscale Ultrahigh Resolution (MUR) level-4 analysis SST acquired from Group for High-Resolution Sea Surface Temperature (GHRSST). Based on these spatial gradients, thermal fronts can be generated that are very useful for predicting potential fishery zones (PFZ), which is available from GEO satellites, INSAT-3D/3DR, in near real-time at 15-minute intervals. Results from the proposed 1DVAR and NLSST algorithms suggest a marked improvement in the SST estimates with reduced diurnal/seasonal biases as compared to the operational NLSST algorithm.

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Improved VIIRS and MODIS SST Imagery

Cited by 29 publications

References 12 publications

Intercomparison Between VIIRS and CrIS by Taking Into Account the CrIS Subpixel Cloudiness and Viewing Geometry

Intercomparison Between VIIRS and CrIS by Taking Into Account the CrIS Subpixel Cloudiness and Viewing Geometry

An Overview of the Science Performances and Calibration/Validation of Joint Polar Satellite System Operational Products

Variational Based Estimation of Sea Surface Temperature from Split-Window Observations of INSAT-3D/3DR Imager

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