Soil Moisture retrieval from multi-temporal SAR data

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Università degli Studi di
Napoli Federico II
Dipartimento di Ingegneria
Agraria ed Agronomia del
Territorio
“On the use of multi-temporal series of COSMO-SkyMed
data for LANDcover classification and surface parameter
retrieval over agricultural sites (COSMOLAND)”
Anna Balenzano (1), Giuseppe Satalino (1), Antonella Belmonte (1), Guido
D’Urso (2), Fulvio Capodici (2), Vito Iacobellis (3), Andrea Gioia (3),
Michele Rinaldi (4), Sergio Ruggieri (4) and Francesco Mattia (1)
(1) Consiglio Nazionale delle Ricerche (CNR) – Istituto di Studi sui Sistemi
Intelligenti per l’Automazione (ISSIA), Bari, Italy
(2) Dipartimento di Ingegneria Agraria e Agronomia del Territorio Università Federico II (UniNa)
(3) Politecnico di Bari (PoliBa) – Dipartimento di Ingegneria delle Acque e di
Chimica
(4) Consiglio per la Ricerca e la Sperimentazione in Agricoltura, Unità di
Ricerca sui Sistemi Colturali per Ambienti Caldo-Aridi (CRA-SCA)
Acknowledgement: the research is supported by the Italian Space Agency under
contract I/051/09/0. COSMO-SkyMed data were provided by ©ASI in the framework
of ©CSK AO 2161. SPOT data were obtained from CNES (2010) Distribution Spot
Image ISIS-368
2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, Canada, 24-29 July 2011
Università degli Studi di
Napoli Federico II
Dipartimento di Ingegneria
Agraria ed Agronomia del
Territorio
Objective
 to present the activities carried out during the first year of COSMOLAND
project on land cover classification and the retrieval of surface parameters
(i.e. Leaf Area Index and soil moisture content) using temporal series of
COSMO-SkyMed data
Outline
 Application context
 COSMO SkyMed & ground data collected over an agricultural site,
Foggia (Italy), during the COSMOLAND campaign in 2010
 Sensitivity of multi-temporal X band SAR data to crops:
 classification algorithm
 Sensitivity of multi-temporal X band SAR data to vegetation and soil
moisture content
 Leaf Area Index retrieval algorithm
 Soil moisture content retrieval algorithm (SMOSAR algorithm)
 Summary and future work
2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, Canada, 24-29 July 2011
Università degli Studi di
Napoli Federico II
Dipartimento di Ingegneria
Agraria ed Agronomia del
Territorio
Application context
The project intends to contribute to the COSMO-SkyMed objectives in
agriculture and hydrology application domains, combining SAR-derived
information and hydrological/crop growth models to improve land and
water resource managment.
 A limitation of land process models is the need for numerous
input parameters, which are seldom available at the appropriate
temporal scale. This lack of information often leads to erroneous
model predictions.
 Crucial information for the land process model performances:
 Land use maps (at medium/low resolution, e.g. monthly)
 Leaf area index maps(at high temporal resolution, e.g. weekly)
 Soil moisture content maps (at high/very high temporal
resolution during alert periods, e.g. daily or hourly)
 COSMO-SkyMed constellation characterized by a short revisiting
time gives a chance to monitor the surface parameters at high temporal
resolution and to demonstrate the use of EO data for improving land
process model forecasts
2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, Canada, 24-29 July 2011
Università degli Studi di
Napoli Federico II
Dipartimento di Ingegneria
Agraria ed Agronomia del
Territorio
2010 COSMOLAND campaign over Foggia (Italy)
Puglia
Product derived from SPOT images (2010)
Foggia site
(Apulia region)
Classified image
from multitemporal SPOT
 Series of 1C-Geocoded Ellipsoid
data (Maximum
Corrected COSMO Sky-Med StripMap
likelihood algorithm PingPong products
on SPOT4
DT
ID
Date
Mode swath
Mean
Polarization
incidence
04/07/2010,
angle [°]
25/07/2010).
D1
03/04/10
StripMap PP02
26
HH/HV
24
D2
27/04/10
StripMap PP02
26
HH/HV
Overall accuracy
24
in test: 95% for
D3
21/05/10
StripMap PP02
26
HH/HV
the test fields
8
D4
29/05/10
StripMap PP02
26
HH/HV
Main crops of(%
theofarea
cultivated
of approx.
area):
700km2
Wheat
(42%)
(48%)
Tomatao ( 8%)
Tomato
7%)
Olives
( 4%)
( 5%)
 In situ measurements from March to
September:
 soil moisture with gravimetric method
 fresh & dry biomass, plant density
and height, Leaf Area Index, yield
Sugar beet (4%)
(3%)
Vineyard
(8%)
(4%)
D5
08/07/10
40
StripMap PP02
26
HH/HV
D6
24/07/10
16
StripMap PP02
26
HH/HV
D7
01/08/10
8
StripMap PP02
26
HH/HV
D8
09/08/10
8
StripMap PP02
26
HH/HV
Limitation: lack of planned dense
temporal series of COSMO SkyMed
data (average revisiting time DT=18 days)
2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, Canada, 24-29 July 2011
Università degli Studi di
Napoli Federico II
Dipartimento di Ingegneria
Agraria ed Agronomia del
Territorio
Sensitivity of multi-temporal X band data to crops
RGB compositions of the COSMO HH images (left) and of HV images (right)
over Foggia. R: 20100403, G:20100427, B:20100521
HV pol.
HH pol.
Product derived from original COSMO-SkyMed products
©ASI- Agenzia Spaziale Italiana (2010)
Temporal X-band backscatter signature of crops is significantly
different and provides useful information for classification.
2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, Canada, 24-29 July 2011
Università degli Studi di
Napoli Federico II
Dipartimento di Ingegneria
Agraria ed Agronomia del
Territorio
Accuracy of the land use classification using multitemporal COSMO data over Foggia in 2010
Based on the Maximum Likelihood algorithm
Overall classification
accuracy of multitemporal, single/multipolarization COSMO
images vs the number
of the SAR images
Overall classification
accuracy of selected
(close to the time of full
development of crops)
multi-temporal,
single/multi-polarization
COSMO images vs the
number of the SAR
images
Product derived from original COSMO-SkyMed products
©ASI- Agenzia Spaziale Italiana (2010)
Land use image from COSMO pingpong Strip Map HH+HV (acquisition
dates 03/04/10, 21/05/10, 08/07/10,
09/08/10 )
 Temporal information during the growing season is important to
improve the crop classification accuracy.
2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, Canada, 24-29 July 2011
Università degli Studi di
Napoli Federico II
Dipartimento di Ingegneria
Agraria ed Agronomia del
Territorio
Sensitivity of X band backscatter to soil moisture (mv)
and vegetation of wheat fields
 data set collected during the ESA
AgriSAR 2006 campaign, DEMMIN site
(Germany):
Temporal behaviour of E-SAR s0
at X-band (HH polarization and
33° incidence angle) and mv and
1/fb of wheat field (N=11)
 dense temporal series of X
band E-SAR data from April to
August
 well-documented ground data
X band
soil moisture
fresh biomass
Correlation coefficients
0.55 (< 7.8%) -0.77 (<0.6%)
 The radar signal is strongly affected by the canopy, which attenuates
the backscatter and reduces the sensitivity to the soil moisture.
 X band SAR data are suited to retrieve vegetation parameters (fb&LAI)
of wheat fields and soil moisture content over low vegetated or bare soils.
2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, Canada, 24-29 July 2011
Università degli Studi di
Napoli Federico II
Dipartimento di Ingegneria
Agraria ed Agronomia del
Territorio
Empirical approach for Leaf Area Index retrieval
Experimental relationship between
LAI and backscatter of wheat field
(derived by the ESA AgriSAR’06
campaign data set)
Example of LAI map obtained
applying the experimental
relashionship to 2010 COSMO
data over Foggia
LAI [m2/m2]
5
4
3
2
y = -0.28x + 0.32
R2 = 0.69
1
29/05/2010
0
-20
-15
-10
Backscatter [dB]
-5
2
6
LAI [m2/m2 ]
 The rmse is 1.1 m2/m2 for LAI derived by COSMO SkyMed data.
The rmse is 0.8 or 1.0 m2/m2 using LAI derived by SPOT or MERIS
data acquired over Foggia from 2006 to 2008
2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, Canada, 24-29 July 2011
Università degli Studi di
Napoli Federico II
Dipartimento di Ingegneria
Agraria ed Agronomia del
Territorio
Soil moisture retrieval based on s0 temporal changes
using dense time series of SAR data
The rational of this approach is that temporal changes of soil roughness
and vegetation take place at longer temporal scales than soil moisture
changes
 dominant scattering mechanism:
 SPM & SSA & PO
s 0  s 0s
soil scattering
soil attenuated by the canopy
s 0s   pp  ,  r   F  , s, l , ACF 
2
s 0   2s 0s
 Soil roughness and vegetation from DoY → DoY+1 constant
(e.g. 1 week revisiting time)
s 
s 
0 doy 1
0 doy

 pp  , 
 pp

 ,  
2
r
doy 1
r
2
 HH  ,  
r
cos 
( r  1)
  sin 
r
2

2
doy
The alpha approximation is appealing as it simplifies the backscatter ratio
between two subsequent and closed SAR observations into a quantity that
depends only on soil moisture changes ( constant)
2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, Canada, 24-29 July 2011
Università degli Studi di
Napoli Federico II
Dipartimento di Ingegneria
Agraria ed Agronomia del
Territorio
Quantitative retrieval of mv
N SAR acquisitions  N-1 linear equations in N unknown pp coefficients
s 
s 
0 doy ( j )
 S ji
1  i  N 1
j  i 1
0 doy ( i )
  ,  
r
pp
doy ( j )

 SS ji  pp  ,  r

doy ( i )
 0
1  k  N 1
k
Under determined linear system  infinite number of solutions
Solution found through Least Square minimization subject to the linear
constraints:

min
pp

  pp  , 
r
 
max
pp
Details in Balenzano et al., IEEE JSTARS 2011
2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, Canada, 24-29 July 2011
Università degli Studi di
Napoli Federico II
Dipartimento di Ingegneria
Agraria ed Agronomia del
Territorio
Soil moisture maps over bare fields of Foggia site
using COSMO data at HH pol. in 2010
Doy 205
Doy 213
Rain event on DoY 212
 A quantitative assessment of the soil moisture retrieval
accuracy comparing retrieved and hydrologically modeled mv maps
is in progress
2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, Canada, 24-29 July 2011
Università degli Studi di
Napoli Federico II
Dipartimento di Ingegneria
Agraria ed Agronomia del
Territorio
European Space Agency project "GMES Sentinel-1 Soil
Moisture Algorithm Development“
poster section in area K on Wed.
Texture
maps
SMOSAR (Soil
Moisture retrieval
from multi-temporal
SAR data) algorithm
based on the alpha
approximation
Land
cover
maps
NDVI
maps
CrossPol SAR
images
Co-Pol
SAR
images
Inc.
angles
images
Time series of N-SAR images
Ancillary data
On-line processor
N-masked Co-Pol images
Masking block
Retrieving block
Time series of N-mv maps
Fast
Delivered
mv maps
N-FD mv maps
of the same
DoY
Off-line processor
Averaging procedure
1-P mv map &
1-error map
of the same
DoY
Precision
mv - error
maps
SMOSAR transforms dense time series of N SAR images into N-mv
maps over agricultural areas with vegetation cover not dominated
by volume scattering
2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, Canada, 24-29 July 2011
Università degli Studi di
Napoli Federico II
Dipartimento di Ingegneria
Agraria ed Agronomia del
Territorio
Summary
 classification, LAI and soil moisture retrieval algorithms, based on
maximum likelihood, empirical approach and temporal backscatter
changes, respectively, have been illustrated and applied to temporal series
of X band SAR data
 the experimental data set consists of 8 COSMO SkyMed images, at
HH/HV polarization, acquired in 2010 over an agricultural site in Italy.
 Results show that:
 classification accuracy improves as a function of the number of the
SAR images. Accuracy of 87% when 4 COSMO images on dates
close to the full development of crops are used.
 potential to retrieve LAI of wheat fields and soil moisture content of
bare or low vegetated soils using multi-temporal SAR data at X band
Future work
II year of the COSMOLAND project: validation of the retrieval/classification
algorithms and assessment of the improvement due to the coupling of the
land process models with SAR-derived information
2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, Canada, 24-29 July 2011
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