Issues in the Integration of Research and Operational Satellite Systems for Climate Research Part II. Implementation (2000) / Chapter Skim
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Calibration and Validation
Calibration and Validation
Pages 11-19
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From page 11... ...
As illustrated in Figure 2.1, five steps are involved in the process of calibration and validation: instrument characterization, instrument calibration, calibration verification, data quality assessment, and data product validation.] All five steps are necessary to add value to the data, permitting an accurate, quantitative picture rather than a merely qualitative one.
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From page 12... ...
In principle, similar standards obtained from two different national measurement laboratories should be equivalent within the combined uncertainty accumulated in their respective chain of measurements.
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Data quality assessment ensures that the algorithm will perform as expected; it is a way of identifying the impacts of changes in instrument performance on algorithm performance. Instrument degradation or a partial malfunction will affect data quality.
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The characterization of MTF was first demonstrated for the Landsat-4 Thematic Mapper under the NASA Landsat Instrument Data Quality Assessment (LIDQA) program in 1984.
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From page 15... ...
There has been some recent discussion, however, advocating a change in the traceability of the radiation balance sensors, from temperature to electricity, using an electrical substitution radiometer operated in a cryogenic environment at NIST. The accumulated uncertainty of the measurement chain for the proposed alternative approach, at its present state of development, is much larger than that of the traceability method now being used.
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From page 16... ...
The stability of microwave radiation sensors, both active and passive, is tested against the on-board hot and cold brightness temperature sources. The greatest uncertainty in backscattered microwave measurements arises from the assumption of sensor linearity in scaling between the hot and cold sources and not from calibrating the temperature of the radiation sources.
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From page 17... ...
DATA QUALITY ASSESSMENT The objective of data quality assessment is to identify data that are suspect or of poor quality relative to expected instrument and algorithm performance. Automated data quality assessment is undertaken during data production, and post-run-time quality assessment is done shortly after production, usually on a sample of the data.
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From page 18... ...
Data products from MODIS such as changes in snow and ice cover, fire, and land cover/change will be validated at locations suited to specific product validation. Validation efforts will also include information collected by higher-resolution sensors (Justice et al., 1998~.
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From page 19... ...
· The calibration of thermal sensing instruments such as CERES and the thermal bands of MODIS should continue to be traceable to the SI unit of temperature via Planckian radiator, blackbody technology. The accumulated uncertainty of calibrations traceable to the fundamental unit of electricity via a cryogenic electrical substitution radiometer is at present much larger than that of calibrations traceable to temperature.
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