Th e International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
2.2 Method: Time Series Long-Term Trend Analyses 
The second analysis focused on the extraction of trends from 
the 15 year time series. For a global context, we depict, which 
areas have become wetter or drier between 1992 and today. A 
period of 10-15 years is not enough to conclude on large scale 
climatic change - however, no other soil moisture time series of 
this length exists. It is furthermore the first time that the TU 
Wien time series underwent a trend analyses. Some areas show 
no change at all, while in several regions worldwide a clear 
tendency towards drier or wetter soil conditions could be 
observed. We furthermore investigated critically, which areas 
of the global dataset are suitable for trend analyses. “Suitable” 
means that, firstly, landcover conditions should favor unbiased 
surface moisture extraction, and secondly, enough 
measurements need to be available. Thus, we firstly masked the 
data set to work with areas, which have favorable conditions for 
highly accurate surface moisture extractions (see figure 2) 
-90 0 90 
50 60 70 80 90 100 
Figure 2. The considered study area. Since soil moisture 
derivation works best in unforested areas, a mask for the present 
analyses has been generated from SPOT-based GLC-product 
derived land-cover information. Only areas with at least 50% 
grassland or Savannah were considered. 
Examples of variation in the number of measurements per 
month over the course of the time series are presented in figure 
3. Due to sensor problems and data downlink capacities not 
every spot on the earth is evenly covered with measurements. 
Figure 3. Variation of data availability over time - from around 
25 measurements per month to no data acquisition, a) 1994 
December; b) 2000 July; c) 2001 February; d) 2007 January. 
Overall the time-series between 1992 and January 2001 shows 
optimal global coverage. From 2001 to mid-2003 sensor 
problems lead to a gap in data acquisition. Since August 2003 
data is only available within the visibility range of a limited 
number of ground receiving stations. 
The time series (only for the unmasked areas from figure 2 was 
then analyzed in the following way: 
1) Two arrays were created (for wet anomalies and dry 
anomalies) with the dimension 15 x 12 (15 years time 
series, 12 months per year). 
2) For these arrays, the number of wet (respectively dry) 
anomalies as a percentage of the total number of 
measurements for that month has been calculated. A 
wet anomaly is defined as a surface soil moisture 
measurement, which is above the long-term mean for 
that day of the year plus 10 times the noise level 
associated to that long-term mean. A dry anomaly is 
defined as a surface soil moisture measurement , 
which is below the long-term mean for that day of the 
year minus 10 times the noise level associated to that 
long-term mean 
3) The yearly means of the percentage values in the 
arrays were calculated by taking the arithmetical 
mean of the 12 monthly values, for each year 
resulting in 2 arrays of 15 years each. 
4) Then a linear "a+bx" line was fit to each of the arrays 
from Step 3. The 2 plots of figure 7 in the results 
section for time series analyses show the "b" value of 
this fit, hence its unit is "percentage per year". 
3. RESULTS 
In the following two subchapters we present 1) results of 
anomaly analyses in the context of flood and drought 
monitoring and 2) results for trend analyses of the complete 
global time series. 
3.1 Results of Anomaly Analyses 
Among several example cases investigated the representation of 
major floods and droughts, e.g. the 1999 drought in Southeast 
China (figure 1) has been confirmed. 
Furthermore, the potential of the 15 year time series for flood 
forecasting has been analyzed. Figure 4 depicts the powerful 
potential of the TU Wien time series for possible early warning 
scenarios. A strong flood hit the UK in November 2000. 13 
people died, 6000 inhabitants had to be dislocated, and the 
estimated damage exceeded 3 Bio. USD. 22 rivers and an 
overall area of 96950 km 2 were affected. The Dartmouth Flood 
Observatory assigned Severity Class 3 
The upper sequence of images in figure 4 depicts surface soil 
moisture from January to December for the year 2000. Dark 
blue areas indicate wettest conditions, light blue areas slightly 
wet conditions and yellowish to dark brown areas drier to 
completely dry conditions. The lower sequence depicts 
anomalies. Grey areas indicate no outstanding / deviating 
behavior while blue areas indicate “wetter than normal” 
behavior and yellow-brownish areas “drier than normal” 
conditions with respect to the 15 year monthly mean. It is 
obvious that during the months January to March soil moisture 
values in the year 2000 are high. However, the anomaly 
sequence indicates that this is rather normal, except for some 
slightly wetter occurrences in Ireland and Scotland in February. 
During the summer, soil moisture is low and the anomaly