Download Hydrotope-based protocol to determine average soil

Hydrotope-based protocol to determine average soil moisture over land
areas for satellite calibration and validation – With results from
observation campaigns in the Volta Basin
Jan Friesen1*, Charles Rodgers2, Philip G. Oguntunde3, Jan M.H. Hendrickx4, and Nick van de Giesen1
1Water
Resources Section, Faculty of Civil Engineering & Geosciences, Delft University of Technology, The Netherlands; 2Center for Development Research, University of Bonn, Germany; 3Department of Agricultural Engineering, School of Engineering, Federal
University of Technology; 4Nigeria, Department of Earth & Environmental Science, New Mexico Tech, USA
Introduction
In West Africa, an extremely moisture limited region, soil water information plays a vital role in hydrologic and meteorologic
modeling for improved water resources planning and food security. Several satellite missions planned for the near future, such
as SMOS, ALOS, and MetOp, hold promise for regional observation of soil moisture. The resolution of all these satellites is
relatively coarse (>100 km²), which brings with it the need for large scale soil moisture information for calibration and validation
purposes. Soil moisture fields obtained via remote sensing have to be validated with ground truth campaigns to provide useful
moisture information.
Hydrotope analysis helps to ensure statistically sound validation and to improve sampling schemes for groundtruthing. In 2005
& 2006 soil sampling ground campaigns in the Volta Basin, West Africa based on hydrotope analysis were conducted.
Identification of hydrotope units Based on knowledge
Hydrotope mapping
812000.000000
814000.000000
692000.000000
694000.000000
686000.000000
elevation and vegetation
694000.000000
814000.000000
810000.000000
812000.000000
690000.000000
688000.000000
690000.000000
information.
812000.000000
692000.000000
Value
maps using globally available
808000.000000
690000.000000
688000.000000
806000.000000
814000.000000
688000.000000
E_DEM
694000.000000
808000.000000
812000.000000
686000.000000
810000.000000
686000.000000
Legend
692000.000000
810000.000000
690000.000000
692000.000000
694000.000000
686000.000000
688000.000000
732000.000000
728000.000000
730000.000000
732000.000000
1052000.000000
730000.000000
690000.000000
692000.000000
1050000.000000
1048000.000000
1048000.000000
806000.000000
0 (0-4 %)
728000.000000
806000.000000
Legend
1 (4 - max %)
694000.000000
1050000.000000
692000.000000
726000.000000
808000.000000
690000.000000
724000.000000
806000.000000
810000.000000
688000.000000
808000.000000
686000.000000
wetland areas.
1052000.000000
722000.000000
Low : 8.99962
814000.000000
810000.000000
694000.000000
High : 17.9148
812000.000000
692000.000000
Value
810000.000000
690000.000000
808000.000000
E_ro_ln(area)
812000.000000
688000.000000
Legend
814000.000000
810000.000000
686000.000000
such as upland areas,
slopes, and lowland or
808000.000000
Low : 156 m
806000.000000
High : 254 m
806000.000000
Source: Masiyandima, et al. (2003)
The identified hydrotope
units are then translated into
812000.000000
internally consistent
hydrologic behavior
688000.000000
814000.000000
810000.000000
These hydrotopes are
landscape units with
808000.000000
Dunn flow
686000.000000
808000.000000
Horton flow
694000.000000
814000.000000
processes, landscape
units are identified.
Baseflow
692000.000000
806000.000000
Wetlands
690000.000000
812000.000000
Slopes
806000.000000
Plateaus
688000.000000
814000.000000
686000.000000
of the hydrological
694000.000000
Legend
1042000.000000
slopes
1042000.000000
1044000.000000
Low : -0.26
1046000.000000
plateaus
High : 0.2125
1044000.000000
Value
1046000.000000
E_NDVI
722000.000000
724000.000000
726000.000000
Legend
1 Plains
2 Wetlands
3 Rivers
wetlands
4 Slopes
Analysis
Hydrotope analysis helps (i) minimizing sampling biases due to oversampling of hydrotope units, and (ii)
minimize the overall variance in sampling schemes. Other advantages are the possibility of ex post
analysis to minimize the overall variance, and the calculation of minimum sampling sizes based on the
required estimate precision.
nj
mw =
∑ A ⋅m
i =1
j
j
A
A good hydrotope analysis can be defined by the statistical differences between the measured classes.
s2
Var
m
<
Var
m
=
(
)
(
)
w
i
High differences between the means of the chosen classes compared to the variance within each class
n
will yield a reduction of the overall variance, thus taking advantage of the hydrotope analysis.
Results
Results show that hydrotope analysis
proves to be a statistically stable
method to derive pixel sized field
averages of soil moisture. For West
Africa, the results show a high soil
moisture organization during
intermediate conditions in both time
and space. Low soil moisture
organization is seen during both
extremely wet and dry conditions.
*corresponding author: Jan Friesen, [email protected]
HYDROTOPE
ANALYSIS RE SULTS
Tamale
Boudtenga
Campaign
Ejura
I
I
II
I
II
Avg_all
0.13
0.17
0.10
0.16
0.14
σ_all
0.039
0.051
0.033
0.038
0.034
Avg_HT
0.13
0.16
0.10
0.16
0.14
σ_HT
0.038
0.037
0.030
0.032
0.031
Samples
197
187
204
200
200
Average and standard deviation of study site soil moisture with (Avg_HT,
σ_HT) and without (Avg_all, σ_all) hydrotope unit separation for the two
sampling campaigns (I = wet conditions, II = dry conditions) as well as the
total number of samples.