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| Titel |
Uncertainties in estimating Normalized Difference Temperature Index from TOA radiances |
| VerfasserIn |
Jian Peng, Yuanbo Liu, Alexander Loew |
| Konferenz |
EGU General Assembly 2013
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 15 (2013) |
| Datensatznummer |
250078541
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| Zusammenfassung |
| The widely used surface temperature/vegetation index (Ts/NDVI) triangle method provides
an effective way to estimate surface turbulent energy fluxes and soil moisture. This type of
method mainly relies on the Normalized Difference Temperature Index (NDTI), which is
usually calculated from land surface temperature (LST). However, retrieval of LST from
remote sensing data requires atmospheric correction procedures, which are often difficult and
troublesome. Our study investigates the feasibility of determining NDTI using top of the
atmosphere (TOA) radiances, instead of satellite-derived LST. A thorough assessment of the
uncertainties in NDTI estimates for different atmospheric and surface conditions is
performed. It is shown that NDTI can be estimated from TOA radiances with an accuracy of
90% if the spatial variabilities of atmospheric parameters (water vapor, effective atmospheric
temperature) and surface emissivity are below 10%, 4 K and 0.05, respectively. A test study is
performed using Moderate Resolution Imaging Spectroradiometer (MODIS) data over a
heterogeneous area of the Poyang Lake basin of China for six consecutive image
acquisitions. When the spatial variations of the surface emissivity, effective atmospheric
temperature and water vapor are respectively less than 0.01, 1 K and 0.2 g cm-2, the TOA
radiance-calculated NDTI value and LST-determined NDTI value are quite close with root
mean square deviation (RMSD) values and biases varying from 0.033 to 0.051 and from
-0.004 to 0.014. The high coefficient of determination (R2) values, ranging from
0.904 to 0.939, indicated that the use of TOA radiances appears to be adequate for
calculating NDTI in these studies. Overall, the proposed algorithm requires less a prior
information on the atmospheric state while providing NDTI estimates at a similar level
of accuracy than obtained using atmospherically corrected LST data products. It
therefore provides a useful alternative for determining NDTI from satellite data. |
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