spatial and temporal variability of soil moisture: 6 years ... · vertisol 7 2.60 0.92 5.0 428...
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Spatial and temporal variability of soil moisture: 6 years survey of SMOS data and in-situ soil moisture measurements in Poland
Mateusz Łukowski, Wojciech Marczewski, Bogusław Usowicz, Jerzy Usowicz, Jan Słomiński, Edyta Rojek, Radosław Szlązak, Łukasz Gluba, Joanna Sagan
🔲🔲 FELIN 🔲🔲 BUBNOW
🔲🔲 TRZEBIESZOW
🔲🔲 BIEBRZA
🔲🔲 BIALOWIEZA
🔲🔲 WIGRY
🔲🔲 MAJDANEK
🔲🔲 JANOW
🔲🔲 CICIBOR
The aim of our research:to find long-term spatial dependencies of surface soil moisture (with focus on Poland)
SM from 9 agrometeorological stations installed in Eastern Poland were compared with SM SMOS
Rain
Solar radiation balance
Soil moisture
Air humidity, temperature
Wind speed
Soil temp.
SMOS vs. in situ SM: time-series comparison
🔲🔲 BUBNOW 🔲🔲Coordinates: 23.27°E, 51.36°NSand: 83%, silt: 15%, clay: 2%
Sensor depth: 10 cm
0
0.1
0.2
0.3
0.4
0.5
0.6
Wat
er c
onte
nt [m
3 /m
3 ]
SMOSBubnow
2010 2011 2012 2013 2014 2015 2016Date
020406080
100120
Rai
nfal
l [m
m]
0
0.1
0.2
0.3
0.4
0.5
0.6
Wat
er c
onte
nt [m
3 /m
3 ]
SMOSTrzebieszow
2010 2011 2012 2013 2014 2015 2016Date
020406080
100120
Rai
nfal
l [m
m]
🔲🔲 TRZEBIESZOW 🔲🔲Coordinates: 22.57°E, 51.99°NSand: 72%, silt: 26%, clay: 2%
Sensor depth: 10 cm
SMOS vs. in situ soil moisture: time-series comparison(classical, linear regression)
SMOS vs. in situ: time-series comparison (summary)
Determination coefficients analyses revealed big discrepancies in year 2010,2012 and 2014. SMOS pixel is large while the population of ground data small(1 station per pixel).We got moderate, but considerable results of validation.
Bubnow
SMOS vs. in situ soil moisture: Bland-Altman method of comparison
🔲🔲 BUBNOW 🔲🔲Coordinates: 23.27°E, 51.36°NSand: 83%, silt: 15%, clay: 2%
Sensor depth: 10 cm
🔲🔲 TRZEBIESZOW 🔲🔲Coordinates: 22.57°E, 51.99°NSand: 72%, silt: 26%, clay: 2%
Sensor depth: 10 cm
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45SM average [m3/m3]
-0.6-0.55
-0.5-0.45
-0.4-0.35
-0.3-0.25
-0.2-0.15
-0.1-0.05
00.05
0.1
SM d
iffer
ence
[m3 /
m3 ]
+1.96 SD
-1.96 SD
Mean
Bland-Altman plot for Bubnow
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35SM average [m3/m3]
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
SM d
iffer
ence
[m3 /
m3 ]
+1.96 SD
-1.96 SD
Mean
Bland-Altman plot for Trzebieszow
References: Bland JM, Altman DG (1986) Statistical method for assessing agreement between two methods of clinical measurement. The Lancet i:307-310.Bland JM, Altman DG (1999) Measuring agreement in method comparison studies. Statistical Methods in Medical Research 8:135-160.
SMOS vs. in situ: Bland-Altman method of comparison(summary)
Bialowieza Biebrza Bubnow Cicibor Felin Janow Majdanek Trzebieszow Wigry-0.6
-0.4
-0.2
0
0.2
SM
diff
eren
ce [m
3 /m3 ]
Summary for Bland-Altman analysis
The biases varied in a wide range depending on the locality of monitoringstation and type of land use. The lowest bias occurred in the Wigry andhighest on Bubnow regions. The Bland-Altman method confirmed a moderately good agreement of soilmoisture from SMOS and most agrometeorological stations.
SMOS vs. in situ: Passing-Bablok method of comparison
🔲🔲 BUBNOW 🔲🔲Coordinates: 23.27°E, 51.36°NSand: 83%, silt: 15%, clay: 2%
Sensor depth: 10 cm
🔲🔲 TRZEBIESZOW 🔲🔲Coordinates: 22.57°E, 51.99°NSand: 72%, silt: 26%, clay: 2%
Sensor depth: 10 cm
References: NCSS Statistical Software, Chapter 313 ”Passing-Bablok Regression for Method Comparison”
Why Passing-Bablok regression?
• Non-parametric method • No assumptions about distributions of samples • No assumptions about distributions of errors• Not sensitive to outliers
Y = a + bXSlope b is a median of all slopes that can be formed from all possible pairs of data points. Intercept a is median of {Yi – bXi} The Passing-Bablok regression confirmed a moderately good agreement of soil moisture from SMOS and agrometeorological stations (work in progress).
Spatial analyses – geostatistical methods
Spatial analyses – geostatistical methods
( ) ( ) ( ) ( )[ ]( )
∑=
+−=hN
iii hxzxz
hNh
1
2
21γ
Spatial analyses – geostatistical methods
The empirical semivariograms γ(h) for distance h were calculated from:
where N(h) is the number of pairs of points z(xi) separated by thedistance h.
Spatial analyses – geostatistical methods
Spatial analyses – geostatistical methodsDate
Variogram model type
Nugget Sill Range (°) Model fit (R2) Comments
2011-02-05 gaussian 0.0035 0.0236 2.97 0.883 Thaw
2011-03-08 exponential 0.0014 0.0060 0.93 0.315 Ground frost2011-03-14 exponential 0.0017 0.0113 2.64 0.538 Thaw
2011-05-11 exponential 0.0014 0.0089 2.04 0.721Drying after thaw and
precipitation
2011-07-14 exponential 0.0041 0.0082 2.22 0.883 After rain
2011-07-22 exponential 0.0022 0.0097 3.19 0.908 RFI
2011-08-15 exponential 0.0006 0.0044 1.23 0.965Drying after
rain
2011-09-16 exponential 0.0027 0.0082 1.95 0.944Drying after
rain
2011-11-01 exponential 0.0028 0.0060 4.32 0.846 Drought
2011-12-17 exponential 0.0010 0.0030 2.06 0.925After
significant rainfall
The considered medium (“ground”) isa mixture of plants, air, water and solid
Spoon of soil = = area of football pitch!
Specific Surface Area (SSA) of soil and ”bound water”
0.3754285.00.922.607Vertisol0.3202706.00.552.655Attapulgite0.2901475.01.302.657Illite0.260 616.01.142.887Ferralsol-A0.120414.31.362.457Wichmond
0.077254.31.492.657Groesbeek
m3 m-3m2 g-1Mg m-3Mg m -3
„Boundwater”*
Surface area*
Dielectricconstant
Bulkdensity*
Particledensity*
uPorous medium, soil
0.3754285.00.922.607Vertisol0.3202706.00.552.655Attapulgite0.2901475.01.302.657Illite0.260 616.01.142.887Ferralsol-A0.120414.31.362.457Wichmond
0.077254.31.492.657Groesbeek
m3 m-3m2 g-1Mg m-3Mg m -3
„Boundwater”*
Surface area*
Dielectricconstant
Bulkdensity*
Particledensity*
uPorous medium, soil
*Roth C.H., Malicki M.A., and Plagge R, (1992), Dirksen C. and Dasberg S. (1993) and Malicki (1993).
0102030405060708090
0 0.2 0.4 0.6 0.8 1Water content, m3 m-3
Die
lect
ric
cons
tant
u=1u=2u=4u=6u=8u=10u=12u=14u=16u=18u=20υ=∞
Specific Surface Area (SSA) of soil:Adsorption limiting of water molecules movement lower dielectric constant lower apparent soil moisture
”degree of freedom” u
Specific Surface Area (SSA) of soil: Map of Poland
>1000 samples!!!
Specific Surface Area (SSA) of soil: Map of Poland
Map of soil Specific Surface Area (SSA)Water adsorption on soil specific surface damping of molecules movement
SMOS Diel_Const_MD_IM MapImaginary part of dielectric constant EM wave damping
1. Several methods of comparison confirmed a moderately good agreement of soil moisture from SMOS and in situ agrometeorological stations
2. Autocorrelation ranges of soil moisture spatial distributions from SMOS were surprisingly high (comparable to other, ”non-satellite” studies*)
3. Specific Surface Area distribution vs. SMOS dielectric constant issue will be examined
Contact: [email protected]
*Vinnikov K., Robock A., Speranskaya N., Schlosser C., 1996. Scales of temporal and spatial variability of mitlatitude soil moisture. J. Geophys. Res. vol. 101, No. D3, 7163-7174.
Summary/Conclusions
THANK YOU FOR YOUR ATTENTION!
The work was partially funded under ESA project “ELBARA_PD (Penetration Depth)” No. 4000107897/13/NL/KML, financed by the Government of Poland through ESA-PECS contract
ELBARA Poland – Bubnow Wetland
ELBARA Poland – Bubnow Wetland
θ
H =
6.7
5 m
φ
Nor
th (N
) φ=
0°ELBARA III –azimuthaland vertical (+improved electronics)
SMOS vs. ELBARA comparison
Apr-2016 Jun-2016 Aug-2016 Oct-2016 Dec-2016 Feb-2017 Apr-2017Date
200
220
240
260
280
300
Brig
htne
ss te
mpe
ratu
re [K
]
SMOS TB-H ASL pin 39SMOS TB-V ASL pin 39ELBARA TB-H e40 a00ELBARA TB-V e40 a00
http://elbara.pl/