Sensor Stability for SST (3S): Toward Improved Long-Term Characterization of AVHRR Thermal Bands
NOAA STAR, College Park, MD 20740, USA
Global Science and Technology, Inc., Greenbelt, MD 20770, USA
Stinger Ghaffarian Technologies, Inc., Greenbelt, MD 20770, USA
Author to whom correspondence should be addressed.
Academic Editors: Xuepeng Zhao, Xiaofeng Li and Prasad S. Thenkabail
Received: 16 February 2016 / Revised: 31 March 2016 / Accepted: 11 April 2016 / Published: 20 April 2016
Recently, the National Oceanic and Atmospheric Administration (NOAA) performed sea surface temperature (SST) reanalysis (RAN1) from seven AVHRR/3s onboard NOAA-15 to -19 and Metop-A and -B, from 2002–present. Operational L1b data were used as input. The time series of clear-sky ocean brightness temperatures (BTs) and derived SSTs were found to be unstable. The SSTs were empirically stabilized against in situ
SSTs using a 90-day moving filter, while the measured BTs were left intact. However, some users are interested in direct radiance assimilation and need stable BTs. Additionally, stabilized BTs will greatly benefit SST (by minimizing the need for their empirical stabilization), and other Level 2 products derived from AVHRR. To better understand the AVHRR calibration and stabilize its BTs, the Sensor Stability for SST (3S; www.star.nesdis.noaa.gov/sod/sst/3s/
) system was established at NOAA, which monitors orbital statistics of the sensor measured blackbody temperatures (BBTs), blackbody counts (BCs), and the space counts (SCs), along with the derived calibration gains and offsets. Analyses are performed separately for the satellite night (when the satellite is in the Earth’s shadow) and day (on the sunlit part of its orbit). Factors affecting the BBT, BC and SC are also monitored, including the Sun and Moon position relative to the sensor, local equator crossing time, and duration of the satellite night. All AVHRRs show long-term and band-specific smooth changes in the calibration gains and offsets, which are occasionally perturbed by spurious non-monotonic anomalies. The most prominent irregularities occur shortly after the satellite crosses from the night into day, or when it is in a (near) full Sun orbit for extended periods of time. We argue that the operational quality control (QC) and calibration procedures are suboptimal and should be improved. Analyses in 3S suggest that a more stringent QC is needed, and scan lines where the calibration coefficients cannot be derived, due to poor quality SC, BC or BBT data, should be filled in by interpolation from the best parts of orbit or more broadly satellite lifetime. Work is underway to redesign the AVHRR QC and calibration algorithms and create a more stable long-term record of AVHRR calibration and BTs, and use them in the subsequent SST RANs.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style
He, K.; Ignatov, A.; Kihai, Y.; Cao, C.; Stroup, J. Sensor Stability for SST (3S): Toward Improved Long-Term Characterization of AVHRR Thermal Bands. Remote Sens. 2016, 8, 346.
He K, Ignatov A, Kihai Y, Cao C, Stroup J. Sensor Stability for SST (3S): Toward Improved Long-Term Characterization of AVHRR Thermal Bands. Remote Sensing. 2016; 8(4):346.
He, Kai; Ignatov, Alexander; Kihai, Yury; Cao, Changyong; Stroup, John. 2016. "Sensor Stability for SST (3S): Toward Improved Long-Term Characterization of AVHRR Thermal Bands." Remote Sens. 8, no. 4: 346.
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