Correlating satellite remote sensing signals with
groundwater head time series
Edwin H. Sutanudjaja, Steven M. de Jong, Frans C. van Geer and Marc F.P. Bierkens Dept. of Physical Geography, Faculty of Geosciences, Utrecht University, The Netherlands
: e.sutanudjaja@geo.uu.nl
Can we monitor groundwater from space ?
Purpose:
To check whether remote sensing signals carry information on groundwater levels. Currently, we focus on the ERS Soil Water Index (SWI) fields (Wagner et al, 1999), time series of which are compared to more than 5000 groundwater head time series in the Rhine-Meuse drainage basin.
➢
Derived from European Remote Sensing (ERS) active scatterometer signals.
➢
Represent soil moisture contents (%) in the first 1 m of soil.
➢
25-50 km spatial resolution.
ERS Soil Water Index
SWI (%)
15 25 35 45 55 65
Rhine-Meuse border
lakes
NETHERLANDS
BELGIUM
GERMANY
GERMANY
AUSTRIA SWITZERLAND
FRANCE FRANCE
Lobith
Borgharen
Liege Namur
Koblenz
Mannheim
Frankfurt
Basel Rekingen Rhine Meuse
Moselle hi R ne
Lahn Sieg
Ruhr Lippe
Nec kar
Aare LUX
0 100km
Soil Water Index August 2005
Groundwater head
➢
Ground measurements from various institutions in the Rhine-Meuse basin.
➢
Only time series from the first upper aquifer were used (> 5000 points).
➢
Point scale.
0 100km
Number of stations in our groundwater head database
1 2-5 6-10 11-25 26-50 51-100 101-250 251-500
>500
# points
Study area:
Rhine-Meuse Basin
The combined basin has ample groundwater head time series.
Figure A and Figure B illustrate mean groundwater head and depth for the period 1974-2008 calculated by using the model of Sutanudjaja et al (2010).
head (+m)
≤ 0 200 400 600 800 1000
≥ 1200
NETHERLANDS
BELGIUM
GERMANY
GERMANY
AUSTRIA
SWITZERLAND FRANCE
FRANCE
Lobith
Borgharen
Liege Namur
Mannheim
Frankfurt
Basel Rekingen LUX
0 100km
Average groundwater head 1974-2008
Koblenz
a
b c
A
0 100km
Average groundwater depth 1974-2008
≤ 0.5 1 5 10 25 50
≥ 100
Results:
R
0 100km
R : Cross correlation at zero lag (k = 0)
C
≤ 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
0 100km
R best : Best cross correlation at lag k ≥ 0
D
≤ 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
0 100km
0 25 50 75 100 125 150 175 200 225 250
k best
(days)
Lag k corresponding to the best cross
correlation Histogram of R
(cross correlation at zero lag)
-1 -0.5 0 0.5 1
G 0 100 200 300 400 500
No. of stations
depth(m)
≤ 0 0 - 0.5 0.5 - 1 1 - 5 5 - 10 10 - 25
> 25
k best : Lag k corresponding to the best cross
correlation
Lag k corresponding to the best cross
correlation
-1 -0.5 0 0.5 1
H 0 100 200 300 400 500
No. of stations
depth(m)
≤ 0 0 - 0.5 0.5 - 1 1 - 5 5 - 10 10 - 25
> 25
1990 1995 2000 2005
1.0
0.0 -1.0
0.0 -7.5 7.5
1.0
0.0 -1.0
42
0
-42
0 -43 43
E R S S o il W a te r In d e x a n o m a ly (% )
42
0
-42
Examples of comparison between groundwater head time series (red) and ERS Soil Water Index (SWI) time series (black)
R(k=0)= 0.80
R
best(0
days)= 0.80 mean groundwater
depth = 1.19 m mean head = 7.09 m
mean SWI = 47.49 %
mean head = 232.19 m mean SWI = 47.17 %
mean head = 32.58 m mean SWI = 55.47 %
0 5 10 15 20
Rpearson
= 0.40
Rspearman= 0.41
0 5 10 15 20
Rpearson
= 0.26
Rspearman= 0.34
groundwater depth (m)0 5 10 15 20
groundwater depth (m)
0
-0.4 -0.6
0.6 0.4
120 100 80 60 40 20
k
best: la g k ( da ys ) fo r b e st c ro ss c o rr e la ti o n
0 0.2 -0.2E2 E1
5.0 0.0 -5.0
g ro u n d w a te r h e a d a n o m a ly ( m )
5.0 0.0 -5.0
5.0 0.0
-5.0
1970 1975 1980 1985 1990 1995 2000 2005 2010
a) Maaseik, Belgium
R = 0.87 average values: data (red) = 23.88 m model (black) = 25.10 m
b) Trier-Euren, Germany
R = 0.81 average values: data (red) = 131.00 m model (black) = 130.81 m
c) Kemmern, Germany
R = 0.84 average values: data (red) = 237.79 m model (black) = 237.68 m