Articles | Volume 20, issue 8
Hydrol. Earth Syst. Sci., 20, 3263–3275, 2016
https://doi.org/10.5194/hess-20-3263-2016
Hydrol. Earth Syst. Sci., 20, 3263–3275, 2016
https://doi.org/10.5194/hess-20-3263-2016
Research article
11 Aug 2016
Research article | 11 Aug 2016

Cloud tolerance of remote-sensing technologies to measure land surface temperature

Thomas R. H. Holmes et al.

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Revised manuscript accepted for HESS
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Cited articles

Aires, F., Prigent, C., Rossow, W. B., and Rothstein, M.: A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations, J. Geophys. Res.-Atmos., 106, 14887–14907, https://doi.org/10.1029/2001JD900085, 2001.
Anderson, M. C., Kustas, W. P., Norman, J. M., Hain, C. R., Mecikalski, J. R., Schultz, L., González-Dugo, M. P., Cammalleri, C., d'Urso, G., Pimstein, A., and Gao, F.: Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery, Hydrol. Earth Syst. Sci., 15, 223–239, https://doi.org/10.5194/hess-15-223-2011, 2011.
André, C., Ottlé, C., Royer, A., and Maignan, F.: Land surface temperature retrieval over circumpolar Arctic using SSM/I–SSMIS and MODIS data, Remote Sens. Environ., 162, 1–10, https://doi.org/10.1016/j.rse.2015.01.028, 2015.
Catherinot, J., Prigent, C., Maurer, R., Papa, F., Jiménez, C., Aires, F., and Rossow, W. B.: Evaluation of “all weather” microwave-derived land surface temperatures with in situ CEOP measurements, J. Geophys. Res.-Atmos., 116, D23105, https://doi.org/10.1029/2011JD016439, 2011.
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Short summary
We test the cloud tolerance of two technologies to estimate land surface temperature (LST) from space: microwave (MW) and thermal infrared (TIR). Although TIR has slightly lower errors than MW with ground data under clear-sky conditions, it suffers increasing negative bias as cloud cover increases. In contrast, we find no direct impact of clouds on the accuracy and bias of MW-LST. MW-LST can therefore be used to improve TIR cloud screening and increase sampling in clouded regions.