The document discusses a study on evaluating soil moisture remote sensing products in Mediterranean landscapes, focusing on agricultural and water management applications. Various active and passive microwave sensors, such as ASCAT, AMSR-E, and SMOS, were analyzed for their effectiveness in measuring soil moisture. The findings indicate that ASCAT correlates best with local soil moisture, particularly during specific seasonal times, highlighting the need for enhanced resolution in these assessments.

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Comparison of Remote
Sensing and Simulated Soil
Moisture Datasets across a
range of spatial resolution in
Mediterranean Landscapes
!
Maria Jose Escorihuela (1) Pere Quintana-
Seguí(2) ) and Olivier Merlin(2)
(1) isardSAT, (2) Observatori de l’Ebre, (3) CESBIO
H2020-MSCA-RISE-2014, 2015- 2019
Grant agreement no: 645642

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CONTEXT
Soil moisture is a critical variable in many physical processes related to
agriculture, hydrology, meteorology or climatology
The Mediterranean region is one of the most sensitive areas to climate
change
Agriculture uses up to 80% of water available in Mediterranean
countries
IPCC predictions, the change in precipitation patterns descend the
productivity of rainfed agriculture down to the 50% of its present value
towards 2020
Irrigation performance assessments at the field scale show a mismatch
between irrigation requirements and the amount of irrigation water that
is actually applied. Optimize on-farm irrigation management.

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Context
MicroWave
Thermal
MicroWave
Fluorescence
NDVI

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How to measure SM globally?
MW
• active
• passive
high sensitivity to dielectric constant in MW domain
1.4
GHz

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How to measure SM globally?
SMOS mission provides for the first time global mapping of
soil moisture from L- band (1.4 GHz) MW radiometry
• soil moisture accuracy
0.04 m3m-3
• biomass ≤ 5 kg m-2
• spatial resolution better
than 50 km
• revisit frequency of 2 to
3 days
• crossing time over the
equator 6 am - 6 pm

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Passive MW L-band
November 2
2009
2017
January 31
2015
2020
before 2020 2025
SMOS
SMAP
WCOM

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OBJECTIVE
Evaluate the state of art of soil moisture remote sensing products in
a Mediterranean area
• ASCAT L2 Soil Moisture Index TU-Wien (5.2 GHz)
• AMSR-E LPRM (6.9 Ghz)
• SMOS L2 OP (1.4 GHz)
Focus on regional and local applications in the context of
agriculture and water management

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REC OBJECTIVE
Evaluate the state of art of soil moisture remote sensing products in
a Mediterranean area
• ASCAT L2 Soil Moisture Index TU-Wien (5.2 GHz)
• AMSR-E LPRM (6.9 Ghz)
• SMOS L2 OP (1.4 GHz)
Focus on regional and local applications in the context of
agriculture and water management
REC aims at develop an operational remote sensing algorithm
dedicated to root zone soil moisture monitoring at the parcel scale.
These estimates will be integrated in an irrigation management
system that will be used to trigger irrigation.

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Disaggregation Methodology
20
cm
0
-­‐
5
cm
L-band Passive MW (40 km, 2/3 d)
+ Medium Resolution OT (1 km, 1 d)
+ High Resolution OT (100 m, 16 d)
SM (1 km, 2/3 d)
SM (100 m, 16 d)
D
I
S
P
A
T
C
H

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Urgell Field Campaign
Soil texture, stone percentage, wilting point
and field capacity
Once a month 2011 gravimetric SSM (0 – 5
cm) measurements:

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Urgell Field Campaign
DoY 146: 26 May 2011 DoY 244: 1 September 2011

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Validation 1km

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DisPATCh 100m
DoY 229: 17 August 2011

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DisPATCh 100m
DoY 277: 3 October 2011

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Validation 100m
O. Merlin, M. J. Escorihuela, M. Aran-Mayoral, O. Hagolle, A. Al Bitar, and Y. Kerr ''Self-calibrated evaporation-based
disaggregation of SMOS soil moisture: an evaluation study at 3 km and 100 m resolution in Catalunya, Spain'' Remote Sensing
of Environment, 130, pp 25–38, doi:10.1016/j.rse.2012.11.008, 2013

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Catalonia
NE Iberian Peninsula
Mediterranean climate
altitudes over 3,000 m
wide range of bioclimatic
habitats.

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Land-surface simulations
Model: SURFEX
• Developed at Météo-France.
• SURFEX is modular, we used the ISBA
3L scheme.
Spatial resolution: 5 km.
Time resolution: we decided to work with
daily series
Variable of interest:
• we focused on the surface layer soil
wetness.
• Satellite soil moisture products are
sensitive to the first few centimeters of
soil.
Simulation period:
1/9/2009 – 31/12/2011 (1 year spin-up)
Period of comparison with satellite data:
• 1 year: 2011
• Therefore, initial conditions are not an
issue.
Masson et al., “The SURFEXv7.2 land and ocean surface
platform for coupled or offline simulation of earth surface
variables and fluxes,” Geosci. Model Dev., vol. 6, no. 4, pp. 929–
960, 2013.

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Land-surface simulations
The SAFRAN meteorological analysis
system was used to force the LSM.
Optimal interpolation.
First guess: HIRLAM HNR 5km.
Operational NWP model at AEMET.
Observed data: All available AEMET
meteorological stations.
SAFRAN analyses temperature, relative
humidity, precipitation, wind and cloudiness
and simulates downward short and long
wave radiation.
Temporal resolution: 6h (interpolated to
hourly series).
Spatial resolution: 5 km.
Quintana-Seguí et al. “Analysis of Near-Surface Atmospheric Variables:
Validation of the SAFRAN Analysis over France,” J. Appl. Meteorol. Climatol.,
vol. 47, pp. 92–107, 2008.

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Methodology
SM products in different units (m3m3, %) and each refer to different depths ->
data normalised
A moving average was applied to ZV or ZV35 to enhance soil moisture memory
and detect seasonal effects, two windows of 10 and 30 days were used
Correlations calculated for each grid point and agregated per cover class
Temporal series analysed over selected points representative of Mediterranean
landscapes: rainfed crops, irrigated crops, grass-shrubs and forest

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Cover Class

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ZV SM Correlation with LSM

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Correlation per Cover Class
With no moving average ASCAT correlates better with LSM
With the 10 or 30-day average results depend on the method

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URGELL Dryland Crops
rain
veg
rough

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Irrigated Crops
Urgell
Delta
Fields inundated May - Nov

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Forest
Solsonès
Cerdanya
counter-phased

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Forest
Solsonès
Cerdanya
counter-phased
Escorihuela
and
Quintana-­‐Seguí
2016
Comparison
of
remote
sensing
and
simulated
soil
moisture
datasets
in
Mediterranean
landscapes,
RSE

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Summary and Conclusions
• Soil Moisture products evaluated across different Mediterranean
landscapes
• ASCAT correlates best with LSM, AMSR-E when seasonal behavior
enhanced
• Over crops, ASCAT soil moisture increases from July to Novembre
probably due to increase of roughness (ploughing or increase of
penetration depth)
• AMSR-E shows very high values of correlation but seems to be
correlated with vegetation cycle
• SMOS is the only to see irrigation
• Enhanced resolution (DisPATCh) is needed in Mediterranean
landscapes

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THANK YOU !
QUESTIONS?