Identification of catchment functional units by time series of thermal remote sensing images

Abstract

Abstract. The identification of catchment functional behavior with regards to water and energy balance is an important step during the parameterization of land surface models.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).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.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 radiationdriven conditions.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.

Estimating spatially distributed soil texture using time series of thermal remote sensing – a case study in central Europe

“…The catchment has a size of 288 km 2 for the gauge in Bissen and stretches from 222 to 535 m above sea level. Collated in for- mer studies (Müller et al, 2014), a spatial data set containing land cover, geology, elevation data and qualitative agricultural soil information is available (see Fig. 2).…”

“…Based on the 28 TOA temperature time series data, PCs (PC1-PC28) are calculated as described in Müller et al (2014). The components of a PCA are orthogonal and represent linear-independent spatial patterns.…”

“…As described in Müller et al (2014), channel 13 (10.25-10.95 µm) is used exclusively, as thermal signals in this wavelength are least altered by absorption in the atmosphere. The remote sensing time series then is processed for reaching 15 m resolution geo-referenced top-of-atmosphere (TOA) temperatures (see Müller et al (2014), for details). The used ASTER data set consists of 28 snow and rather cloud free images from the period January 2001 to June 2012 (Fig.…”

“…In a previous investigation, Müller et al (2014) utilized principal component analysis (PCA) for a statistical extraction of dominant patterns within an ASTER (advanced spaceborne thermal emission and reflection radiometer) thermal infrared (TIR) time series. The produced principal components (PCs) are independent and will be used here to derive stable patterns that are related to soil texture classes.…”

Evaluation of Spatio-Temporal Patterns of Remotely Sensed Evapotranspiration to Infer Information about Hydrological Behaviour in a Data-Scarce Region

“…Since clouds rarely repeated in the same areas, distortion due to clouds was negligibly small. Therefore, the computation of cloud masks was omitted as heavy fragmentation of the time series would occur if the masks were applied for even small clouds in every affected image and cumulatively applied to the entire time series [25]. In addition, MOD16A2 uses daily global reanalysis weather data as part of its input which potentially mitigates the effect of missing data due to cloudiness [52].…”