The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
moisture time series with regional soil moisture in situ data (e.g. 
Scipal 2002, Wagner et al. 2003, Zhao et al. 2006). 
Compared to synthetic aperture radar (SAR) systems 
scatterometers offer multiple incidence angles for each overpass, 
which enables to better account for the effects of vegetation and 
surface roughness. Furthermore, contrary to SAR, lower 
resolution scatterometer sensors allow to map the Earth surface 
within less than three days. Here a coarse spatial resolution of 
50 km (ERS Scat) or 25 km (ASCAT) is accepted, since an 
excellent temporal resolution can be achieved. Soil moisture 
can be investigated at two different spatial scales. The first is 
the spatial scale below 100 meters, where spatial and timely soil 
moisture variability are mainly driven by vegetation, soil type 
and topography (Scipal et al. 2005, Vachaud et al. 1985). The 
second scale at several kilometers represents soil moisture 
variability induced by atmospheric forcing effects, thus mainly 
being influenced by climatic conditions and large scale 
precipitation events (Vinnikov et al. 1999, Ceballos et al. 2002). 
Scatterometer derived soil moisture data at the scale of 25 to 50 
km therefore contains information about large scale 
meteorological events. Furthermore, especially spatio-temporal 
changes in longer time series of data with expected seasonal 
soil moisture patterns can indicate the occurrence of slow onset 
natural hazards such as floods or droughts. 
1.2 The TU Wien Dataset: ERS-Scatterometer derived 
Surface Moisture 
The active ERS scatterometer with three sideways looking 
antennae collects backscatter measurements in the 5.3 GHz 
domain (C band) with vertical polarization over an incidence 
angle range from 18° to 57°. Global coverage is achieved every 
3-4 days (Scipal et al. 2005). Strictly speaking, the 
backscattered signal cP is mainly a function of dielectric 
properties of materials depending on frequency, /, polarization, 
pp, and incidence angle 6. The dielectric constant of a material 
mainly depends on its water content. The function S( f ,0) 
describes backscattering according to surface roughness and is 
also influenced by frequency and incidence angle. This basic 
principle of dielectric properties and geometric surface structure 
is used by the majority of electromagnetic backscattering 
models to derive soil moisture (Knabe 2004). 
a o(PPf > 9) =D(f, pp, 0) • S(f, 0) (1) 
Since one is only interested in the part of the signal, which 
represents the moisture content other influences need to be 
corrected for. Heavily vegetated areas like rainforests are 
masked out from the TU Wien data set. In dense forest areas 
volume scattering dominates and the backscattered signal from 
the ground covers a too small portion of the overall 
backscattered signal. Furthermore, snow covered areas are 
masked to exclude areas, where no statement about soil 
moisture is possible. Coastal zones and inland water bodies are 
also excluded. Incidence angle dependencies and effects of 
surface roughness, heterogeneous vegetation cover, and land 
cover are fully accounted for with the change detection 
approach implemented and presented by Wagner et al. (1999). 
Thus, after corrections the relationship between backscatter 
(normalized to an incidence angle of 40°, cP (40)) and soil 
moisture variability is linear (Scipal 2002). The change 
detection approach thus only requires a time series of data to be 
available. From this time series surface soil moisture 
information equivalent to the degree of saturation in relative 
units, ranging between 0-100 %, can be retrieved. In this 
change detection method the current backscattering coefficient 
is compared to the highest and lowest measurement record 
(referred to as cP wet and a° dry respectively) for this spatial 
location within the available time series. If cP^ and cP^ A 
represent a completely dry soil surface and a saturated soil 
surface then m s is equal to the degree of saturation, equaling the 
soil moisture content in percent of porosity. m s can be derived 
from every backscatter measurement for a point on earth and is 
thus available every 3-4 days. From the TU Wien Global Soil 
Moisture Archive surface moisture data sets can be extracted on 
a weekly, ten-day, or monthly basis for every defined area. 
2. METHODS FOR TIME SERIES ANALYSES 
2.1 Method: Anomaly Extraction and Analyses 
Figure 1. “La Nina” (post El Niño event) related drought in 
south-eastern China as observed in soil moisture anomaly data 
(top), and highly correlated also occurring in GPCC data 
(gridded precipitation data of the German Weather Service) 
As a first focus of analyses, we extracted major anomalies from 
the time series. The term anomaly refers to the deviation of 
surface soil moisture at a given spatial location with respect to 
the time series mean of all soil moisture values for this month. 
The extracted strong anomalies reflect severe drought and flood 
conditions over the course of over 15 years in many countries 
worldwide. The following figure 1 shows an example, 
representing a “La Niña” related drought situation in China in 
February 1999 after the very strong 1997/1998 El Niño. In the 
upper part of the figure green areas are masked out areas (snow 
cover), grey areas indicate soil moisture conditions within the 
normal range, while blue areas indicate wetter than normal 
conditions and yellowish to brown areas indicate drier than 
normal conditions. 
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