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July 1, 1998IEEE Transactions on Geoscience and Remote Sensing1,468 citations

A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images

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AGAlan R. GillespieGold Coast HospitalSRShuichi RokugawaNational Research Institute for Earth Science and Disaster ResilienceTMTsuneo MatsunagaNational Institute for Environmental Studies

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Abstract

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scanner on NASA's Earth Observing System (EOS)-AM1 satellite (launch scheduled for 1998) will collect five bands of thermal infrared (TIR) data with a noise equivalent temperature difference (NE/spl Delta/T) of /spl les/0.3 K to estimate surface temperatures and emissivity spectra, especially over land, where emissivities are not known in advance. Temperature/emissivity separation (TES) is difficult because there are five measurements but six unknowns. Various approaches have been used to constrain the extra degree of freedom. ASTER's TES algorithm hybridizes three established algorithms, first estimating the normalized emissivities and then calculating emissivity band ratios. An empirical relationship predicts the minimum emissivity from the spectral contrast of the ratioed values, permitting recovery of the emissivity spectrum. TES uses an iterative approach to remove reflected sky irradiance. Based on numerical simulation, TES should be able to recover temperatures within about /spl plusmn/1.5 K and emissivities within about /spl plusmn/0.015. Validation using airborne simulator images taken over playas and ponds in central Nevada demonstrates that, with proper atmospheric compensation, it is possible to meet the theoretical expectations. The main sources of uncertainty in the output temperature and emissivity images are the empirical relationship between emissivity values and spectral contrast, compensation for reflected sky irradiance, and ASTER's precision, calibration, and atmospheric compensation.

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Gillespie et al. (1998) studied this question.

synapsesocial.com/papers/69df3f676324afb55d591c05https://doi.org/10.1109/36.700995
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