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Land Surface Temperature and Emissivity (LST&E) products and their uncertainties
Glynn
C.
Hulley, JPL, glynn.c.hulley@jpl.nasa.gov
Simon
J.
Hook, JPL, simon.j.hook@jpl.nasa.gov
(Presenter)
Land surface temperature and emissivity (LST&E) data are critical variables for studying the energy, carbon, and water balances of the Earth surface, and have been identified as an important Earth System Data Record (ESDR) by NASA and many other international organizations, e.g. GCOS. An ESDR is defined as long-term, consistent and well calibrated data record for Earth Science research. One of NASA's recent goals is to enhance and improve the science value of ESDRs through rigorous estimation of uncertainty between different sensors and algorithms. Identifying and quantifying these uncertainties is essential if they are to be used as effectively as possible by users in merged datasets, modeling studies and as long-term climate records. In this study we use numerical simulations and validation data to generate uncertainties for LST&E products from MODIS, VIIRS, ASTER, and Landsat. Results from the study show that the MOD11 LST product, which is generated from a split-window algorithm, has high accuracy over graybody surfaces (water, vegetation) but lower accuracy over bare surfaces, while the reverse is true for the MOD21 LST product which is generated by the TES algorithm. Using these results we can produce merged LST products using the uncertainties as weighting functions in order to capitalize on the strength of each algorithm. In a similar manner, the emissivity product and associated uncertainty generated from the ASTER-TES algorithm at 90 m resolution can be used to calculate the LST from any Landsat sensor, since Landsat has only band in the thermal infrared region.
Presentation Type: Poster
Session: Poster Session 1-A
(Tue 11:00 AM)
Associated Project(s):
Poster Location ID: 44
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