Calibration approach and plan for the sea and land surface temperature radiometer

Smith, David L.; Nightingale, T.; Mortimer, Hugh; Middleton, Kevin; Edeson, Ruben; Cox, Caroline V.; Mutlow, C. T.; Maddison, B. J.; Coppo, P.

doi:10.1117/1.jrs.8.084980

Cited by 12 publications

(8 citation statements)

References 16 publications

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“…SLSTR, the focus of this paper, is a multichannel scanning radiometer that is a follow-on to the two decades of successful operation of along-track scanning radiometer (ATSR) series. 11 It was designed by incorporating the basic functionality of ATSR, with the addition of some new, more advanced, features. 10 SLSTR has 11 spectral bands with wavelengths from 0.55 to 12 µm.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of MODIS and Sentinel-3 SLSTR thermal emissive bands calibration consistency using Dome C

Shrestha

Angal

Xiong

2018

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV

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Over the years, data from different satellites has provided invaluable information about Earth's atmosphere, land and oceans. The thermal emissive bands (TEB) on the Moderate Resolution Imaging Spectroradiometer (MODIS) are comprised of 16 spectral bands with wavelengths ranging from 3.7 to 14.4 µm. MODIS TEB are calibrated on orbit on a scan-by-scan basis using an on-board blackbody (BB). Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR), launched on 16 February 2016, has 11 spectral bands with wavelengths from 0.55 to 12 µm. In this study, we compare the observed brightness temperature from MODIS bands 31 and 32 and SLSTR bands S8 and S9 over Dome C using a 20 × 20 km region of interest (ROI) centered at (75.102 • S ,123.395 • E). A total of 2989 scenes for Terra, 2963 for Aqua and 1961 for SLSTR from November, 2016 to January 2018 are analyzed. The relative bias between MODIS and SLSTR is evaluated using the near-surface temperature measurements from an Automatic Weather Station (AWS).

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Section: Introductionmentioning

confidence: 99%

Evaluation of MODIS and Sentinel-3 SLSTR thermal emissive bands calibration consistency using Dome C

Shrestha

Angal

Xiong

2018

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV

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“…This series supported accurate, stable SST timeseries [20]. The SLSTR series started with the launch of the SLSTR-A unit on Sentinel 3A [21,22], with data available since November 2016.…”

Section: Gap In Dual-view Reference Sensors For Sstmentioning

confidence: 72%

Harmonization of Space-Borne Infra-Red Sensors Measuring Sea Surface Temperature

et al. 2020

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Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing.

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“…The origins of this work began during the development phase of the SLSTR instruments where preliminary uncertainty estimates were derived to assess the predicted performance with the view to demonstrate compliance to the mission requirements [6][7][8][9][10][11]. For current and future missions, it is important to start the process early, documenting the full calibration model as described in this paper and providing access to test reports in accessible forms to demonstrate traceability.…”

Section: Discussionmentioning

confidence: 99%

“…In principle, the radiometric calibration of a TIR instrument may be traced to SI via blackbody calibration sources whose temperatures are measured by accurate thermometers, calibrated against standard platinum resistance thermometers (S-PRTs), which in turn are calibrated against reference artefacts of the ITS-90, such as triple point cells as illustrated in Figure 1. Prior to launch, the SLSTR was calibrated following a full end-to-end calibration plan [11], as described in Smith et al, 2020 [12]. The pre-launch calibration activities included:…”

Section: Introductionmentioning

confidence: 99%

“…The aim of this paper is to collate and condense this information to provide a description of the end-to-end traceability of the instrument calibration and to provide per-pixel uncertainty estimates of the Level-1 infrared radiometric data products. Prior to launch, the SLSTR was calibrated following a full end-to-end calibration plan [11], as described in Smith et al, 2020 [12]. The pre-launch calibration activities included:…”

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confidence: 99%

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Traceability of the Sentinel-3 SLSTR Level-1 Infrared Radiometric Processing

et al. 2021

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Providing uncertainties in satellite datasets used for Earth observation can be a daunting prospect because of the many processing stages and input data required to convert raw detector counts to calibrated radiances. The Sea and Land Surface Temperature Radiometer (SLSTR) was designed to provide measurements of the Earth’s surface for operational and climate applications. In this paper the authors describe the traceability chain and derivation of uncertainty estimates for the thermal infrared channel radiometry. Starting from the instrument model, the contributing input quantities are identified to build up an uncertainty effects tree. The characterisation of each input effect is described, and uncertainty estimates provided which are used to derive the combined uncertainties as a function of scene temperature. The SLSTR Level-1 data products provide uncertainty estimates for fully random effects (noise) and systematic effects that can be mapped for each image pixel, examples of which are shown.

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Calibration approach and plan for the sea and land surface temperature radiometer

Cited by 12 publications

References 16 publications

Evaluation of MODIS and Sentinel-3 SLSTR thermal emissive bands calibration consistency using Dome C

Evaluation of MODIS and Sentinel-3 SLSTR thermal emissive bands calibration consistency using Dome C

Harmonization of Space-Borne Infra-Red Sensors Measuring Sea Surface Temperature

Traceability of the Sentinel-3 SLSTR Level-1 Infrared Radiometric Processing

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