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: m.lukowski@ipan.lublin.pl

*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/