Quantifying uncertainties in land surface temperature and emissivity retrievals from ASTER and MODIS thermal infrared data

Glynn C. Hulley; Christopher G. Hughes; Simon J. Hook

doi:10.1029/2012jd018506

journal article Dec 12, 2012

Quantifying uncertainties in land surface temperature and emissivity retrievals from ASTER and MODIS thermal infrared data

Glynn C. Hulley Christopher G. Hughes

Simon J. Hook

Journal of Geophysical Research: Oceans Vol. 117 No. D23 · American Geophysical Union (AGU)

View at Publisher Save 10.1029/2012jd018506

Abstract

Land surface temperature and emissivity (LST&E) data are essential for a wide variety of surface‐atmosphere studies, from calculating the evapotranspiration of the Earth's land surface to retrieving atmospheric water vapor. LST&E products are generated from thermal infrared data acquired from sensors such as ASTER and MODIS on NASA's EOS platforms. NASA has identified a major need to develop long‐term, consistent products valid across multiple missions, with well‐defined uncertainty statistics addressing specific Earth science questions. These products are termed Earth System Data Records (ESDRs) and LST&E have been identified as an important ESDR. Currently a lack of understanding in LST&E uncertainties limits their usefulness in land surface and climate models. To address this issue, a LST&E uncertainty simulator has been developed to quantify and model uncertainties for a variety of TIR sensors and LST algorithms. Using the simulator, uncertainties were estimated for the MODIS and ASTER TES algorithm, including water vapor scaling (WVS). These uncertainties were parameterized according to view angle and estimated total column water vapor for application to real data. The standard ASTER TES algorithm had a RMSE of 3.1 K (1.2 K with WVS), while the MODIS TES algorithm had a RMSE of 4.5 K (1.5 K with WVS). Accuracies in retrieved spectral emissivity for both sensors degraded with higher atmospheric water content, however, with WVS the emissivity uncertainties were reduced to <0.015. Accurately quantifying uncertainties in LST&E products not only improves their utility and understanding but will also enable the data to be fused into long‐term, well characterized ESDRs.

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Details

Published: Dec 12, 2012
Vol/Issue: 117(D23)
License: View

Authors

G

Glynn C. Hulley

C

Christopher G. Hughes

From the Critical Illness, Brain Dysfunction, and Survivorship Center (C.G.H., J.C.J., A.S.H., B.T.P., O.M.O., R.R., J.L.S., A.L.K, M.B.P., N.E.B., T.D.G., R.S.D., G.R.B., E.W.E., P.P.P.), the Center for Health Services Research (C.G.H., J.C.J., R.R., M.B.P., T.D.G., R.S.D., E.W.E., P.P.P.), the Division of Anesthesiology Critical Care Medicine, Department of Anesthesiology (C.G.H., P.P.P.), the Division of Allergy, Pulmonary, and Critical Care Medicine (J.C.J., B.T.P., G.R.B., E.W.E.), and the Division of General Internal Medicine and Public Health (R.S.D.), Department of Medicine, the Departments of Biostatistics (O.M.O., R.R.) and Pharmaceutical Services (J.L.S.), and Division of Trauma and Surgical Critical Care, Department of Surgery (M.B.P.), Vanderbilt University Medical Center, and the Anesthesia Service (C.G.H., P.P.P.), Research Service (J.C.J.), Surgical Service (M.B.P.), and Geriatric Research, Education and Clinical Center (R.S.D., E.W.E.), Department of Veterans Affairs Medical Center, Tennes...

S

Simon J. Hook

Jet Propulsion Laboratory California Institute of Technology Pasadena California USA

Cite This Article

Glynn C. Hulley, Christopher G. Hughes, Simon J. Hook (2012). Quantifying uncertainties in land surface temperature and emissivity retrievals from ASTER and MODIS thermal infrared data. Journal of Geophysical Research: Oceans, 117(D23). https://doi.org/10.1029/2012jd018506

Quantifying uncertainties in land surface temperature and emissivity retrievals from ASTER and MODIS thermal infrared data

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