Identification of catchment functional units by time series of thermal remote sensing images
The identification of catchment functional behavior with regards to water and energy balance is an important step during the parameterization of land surface models. <br><br> An approach based on time series of thermal infrared (TIR) data from remote sensing is developed and investigated...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2014-12-01
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Series: | Hydrology and Earth System Sciences |
Online Access: | http://www.hydrol-earth-syst-sci.net/18/5345/2014/hess-18-5345-2014.pdf |
Summary: | The identification of catchment functional behavior with regards to water and
energy balance is an important step during the parameterization of land
surface models.
<br><br>
An approach based on time series of thermal infrared (TIR) data from remote
sensing is developed and investigated to identify land surface functioning
as is represented in the temporal dynamics of land surface temperature
(LST).
<br><br>
For the mesoscale Attert catchment in midwestern Luxembourg, a time series
of 28 TIR images from ASTER (Advanced Spaceborne Thermal Emission and
Reflection Radiometer) was extracted and analyzed, applying a novel process
chain.
<br><br>
First, the application of mathematical–statistical pattern analysis
techniques demonstrated a strong degree of pattern persistency in the data.
Dominant LST patterns over a period of 12 years were then extracted by a
principal component analysis. Component values of the two most dominant
components could be related for each land surface pixel to land
use data and geology, respectively. The application of a data condensation
technique ("binary words") extracting distinct differences in the LST
dynamics allowed the separation into landscape units that show similar
behavior under radiation-driven conditions.
<br><br>
It is further outlined that both information component values from principal component analysis (PCA), as
well as the functional units from the binary words classification, will
highly improve the conceptualization and parameterization of land surface
models and the planning of observational networks within a catchment. |
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ISSN: | 1027-5606 1607-7938 |