CEOS Cal Val Test Site - Algeria 3 - Pseudo-Invariant Calibration Site (PICS)Entry ID: CEOS_CalVal_Test_Sites-Algeria3
Abstract: On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols ... and guidelines for these purposes:
Reference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.
Initiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.
Pseudo-Invariant Calibration Sites (PICS):
Algeria 3 is one of six CEOS reference Pseudo-Invariant Calibration Sites (PICS) that are CEOS Reference Test Sites. Besides the nominally good site characteristics (temporal stability, uniformity, homogeneity, etc.), these six PICS were selected by also taking into account their heritage and the large number of datasets from multiple instruments that already existed in the EO archives and the long history of characterization performed over these sites. The PICS have high reflectance and are usually made up of sand dunes with climatologically low aerosol loading and practically no vegetation. Consequently, these PICS can be used to evaluate the long-term stability of instrument and facilitate inter-comparison of multiple instruments.
Purpose: To facilitate and coordinate calibration and validation data over the Algeria 3 test site.
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Start Date: 1972-08-11
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Email: Tyler.B.Stevens at nasa.gov
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Almeida, A. and Journel, A.: 1994, Joint simulation of multiple variables with
a markovtype coregionalization model, Mathematical Geology 26(5), 565-588.
Chiles, J. and Delfiner, P.: 1999, Geostatistics: Modeling spatial
uncertainty, John Wiley & Sons, New York.
Deutsch, C. and Journel, A.: 1992, GSLIB: Geostatistical Software Library and
User's Guide, ... Oxford University Press, New York.
Goovaerts, P.: 1994, Comparative performance of indicator algorithms for
modeling conditional probability distribution functions, Mathematical Geology
Goovaerts, P.: 1997a, Geostatistics for natural resources evaluation, Oxford
University Press, New York.
Goovaerts, P.: 1997b, Geostatistics for natural resources evaluation, Oxford
University Press, New York.
Holden, L., Hauge, R., Skare, O. and Skorstad, A.: 1998, Modeling of fluvial
reservoirs with object models, Mathematical Geology 30(5), 473-496.
Journel, A.: 1999, Markov models for cross covariances, Mathematical Geology
Journel, A.: 2002, Combining knowledge from diverse sources: An alternative to
traditional data independence hypotheses, Mathematical Geology 34(5), 573-596.
Mao, S. and Journel, A.: 1999, Generation of a reference petrophysical/seismic
data set: the stanford v reservoir, Report 12, Stanford Center for Reservoir
Forecasting, Stanford, CA.
Strebelle, S.: 2000, Sequential simulation drawing structures from training
images, PhD thesis, Stanford University, Stanford, CA.
Tjelmeland, H.: 1996, Stochastic models in reservoir characterization and
Markov random fields for compact objects, PhD thesis, Norwegian University of
Science and Technology, Trondheim, Norway.
Tran, T.: 1994, Improving variogram reproduction on dense simulation grids,
Computers and Geosciences 20(7), 1161-1168.
Zhu, H. and Journel, A.: 1993, Formatting and interpreting soft data:
Stochastic imaging via the markov-bayes algorithm, 1, 1-12.
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