Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
SMOS MissionLaunched in 2009 data flow started in January 2010First 6 months to be used with careTests dual pol full polOut gassing and calibration issuesMaximum RFI environment
Several re-processingsNew measurements & new instrument -> wax and
strings, trial and error approach to overcome the unexpected !Many improvements from V3 to V6, V7 underway!
Availability of L2 and L3 -L4, new products in the making
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
• Training on SMOS Level 2 v620 SM • Similar performances (slightly better indeed) • Much faster ! Less than 3.5 hours after sensingRodriguez-Fernandez et al. (2017, HESS)
Implemented by :
With support by :
Delivered to :
Disseminated by:
Near Real Time SM
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerrAveraged SM values over Jan/Apr/Jul/Oct from Jul-2010 to Apr-2017 (28 months)
V650 is ready ! All archive (2010 –july 2017) reprocessedSoon to start in the operational processor
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
First step: CalibrationComparison at TB level are conducted over various
surfaces of known temperaturesGalactic backgroundIce sheet (Antarctica)Ocean bodies
Or other similar sensorsBased on near simultaneous, iso geometryLandOcean
Ice core around Dome ConcordiaCompared TBs are ToA, without reflexion foreign source
corrections (gal, sun, moon)
SMOS’ view of the Galaxy
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
SMOS and DomeX
Long term stabilityOverall good agreementIncidence induced bias
between SMAP and Aquarius
Sensor Version Inc TBH TBV DTBH DTBV
Aquarius v4 28 192.90 206.19 0.59 1.16
Aquarius v4 38 189.23 210.61 0.33 -0.62
Aquarius v4 45 185.03 213.40 1.01 -0.98
SMAP R12170 40 187.67 212.46 -0.88 0.41
SMAP R13080 40 186.17 210.08 -2.38 -1.97
SMOS v620 38 188.90 211.23
SMOS v620 40 188.55 212.05
SMOS v620 28 192.31 205.03
SMOS v620 45 184.02 214.38
DOMEX-2 42 186.27 206.57 -0.015 -6.645
DOMEX-3 42 187.34 207.54 1.055 -5.675
F. Cabot
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
SMOS -SMAP, Successful RetrievalsMonthly Animation: 2015.04-2016.05
SMAP SMOS
SMOS-SMAPMahmoodi A.
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
L3 SM, SMOS Successful Rets & SMAP Recommended Rets
BIAS RMSE
Corr unB RMSEMahmoodi A.
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
Step 2 Validation Done over different targets
Sparse networks (Scan, SNOTel, …)
Dense networks USDA Watersheds, OziNet, Hobe, …
Specific, complicated targets and core sites Rugged terrain (Alps) Boreal areas (Sodankyla) Tropical forest (Chaco)
Satellite data different sensors (SMAP, Aquarius, AMSR, … ) Different retrieval approaches (RT model, Neural networks, simplified approaches,
….)Model outputs
Note that the retrieval approaches are all global not point to point Note that when using a large number of sites the total distribution of
quality metrics should be studied instead of just quality metrics averages
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
Step 2 Validation Issues to be tackledSurface area of ground measurements (representativeness)
HOBE like set upsCosmos probes
Spatio-temporal dimensionsBeatriz Molero Rodenas study
Organic soilsSpecific calibrations
Quality of measurementsGround measurements have own uncertainties and are never “truth”
Models might be “wrong” over specific ecosystems
Algorithmspoint to point or regional algorithms
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
Models and “proxy” sensors give erroneous estimatesA. Mialon
Very important region: • Hotspot (land feedback to atmosphere, Koster et al., Seneviratne et al.)• Very little in situ data to constrain weather models -> Remote sensing
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
SMOS SM used as reference for other instruments
AMSR-E retrievals using a neural network trained on SMOS L3 SM (2003-2010 -> 2010-2017)
Rodriguez-Fernandez et al. (2016. Remote Sensing)
Long time series
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
SMOS support to the CCI ESA SMOS/AMSR-E fusion project Neural networks are a promising approach SMOS can be used as reference for re-scaling other
instruments SMOS should be inserted into the CCI framework using LPRM
CESBIO : extraction and pre-processing of SMOS and ECMWF auxiliary data for their use by the CCI team
SMOS now taken into account in CCIv3 (Dorigo et al. 2017)
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
Soil Moisture 1 km Morocco
Land Surface Temperature
Optic/Thermal
Soil MoistureSMOS
1 km / 1 day
40 km / 3 days
DisPATCh-SM actual
Soil MoistureSMOS
Land Surface TemperatureMODIS (Aqua/Terra)
J. Malbeteau
A. Mahmoodi
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
Example of SMOS High Resolution data for irrigation monitoring
Molero et al
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
USING SMOS DATA IN NWP
In situOpen Loop SMOS NN SM σ x1 + T2m + RH2mSMOS NN SM σ x3 + T2m + RH2m
Assimilating SMOS data moderately improves the soil moisture analysis: On average, for more than 400 in situ sites,the performances of the analysed soil moisture fields are close (within 2-3 %) to those of the open loop experiment
Analysed surface fields are used to compute atmospheric forecasts: SMOS soil moisture (NRT, NN based product)improves the forecast in the Northern Hemisphere
Blue: positive impact
Red: negative impact
From:Rodriguez-Fernandez, de Rosnay, Albergel, et al. 2017, ECMWF ESA reportRodriguez-Fernandez et al. (in prep.)
Further work assimilating L-Band into NWP, e.g.• J. Kolassa: Merging active and passive microwave observations
in soil moisture data assimilation, RSE ,2017• G. De Lannoy: Assimilation of SMOS brightness temperatures or
soil moisture retrievals into a land surface model, Hydrology andEarth System Sciences, 2016
RMSE of 36h FC 850 hPa temperature forecastsSLV+SMOS DA (sigmao*3) minus OL SLV+SMOS DA (sigmao*9) minus OL
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
Summary L Band radiometry (SMOS- SMAP and Aquarius) has proven its ability to deliver reliable
accurate and absolute (non model scaled) soil moisture fields over the globe even in areas of dense vegetation thanks to its long wavelength
Independent retrieval approaches (RT, NN, SCA, SMOS-IC , DCA, ..) or completely different instruments provide very similar if not totally similar results (without fiddling our massaging such as trend correction, anomaly etc..) showing the robustness of the measurements
High temporal revisit (<14 h on average over the globe) using both SMOS and SMAP Why passive L band?
Because of its characteristics and inherent qualities The most appropriate tool as shown by all the products stemming from it Temporal stability and robustness
L band radiometry --> proof of concept demonstrated Uniqueness of the measurements hence
Many science outstanding results A very large number of operational or pre operational demonstration products
BUT … No follow on mission currently --> Data gap Need to act now
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 20322008
L-Band radiometry missions
PrecipitationPrecipitationGravity
SMOS (ESA CNES) (40 km / 3days / L-band / global )
SMAP (NASA) (10-60 km / 3days / L-band / global)
Thermal
Color codesAltimeters
L-Band PassiveOptical
Radar
Thermal
Color codesAltimeters
L-Band PassiveOptical
Radar
Aquarius (NASA) (100km / 8days / L-Band/ global)
Kerr and Al Bitar
TODAY!
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
SMOS DATA PRODUCTSOver land
Data product Resolution/format Latency Available from
NRT light: Level 1 brightness temperature
30-50km (N256 Gaussian grid), swathbased; BUFR.
NRT/ 3 hours from sensing
ESAEUMETCASTWMO GTS
Level 2 soil moisture in NRT (based on Neural Network)
15 km (ISEA 4H9 grid), swath based; NETCDF.
NRT/~4 hours from sensing
Operational/NRT products / Latency < 3 hours
Data product Resolution/format Latency Available from
Level 1 brightnesstemperature
15 km (ISEA), swath EEF /NetCDF.25 km, global, EASE- NetCDF
6-8 hours after sensing1 d after sensing
ESACATDS(+ stereopolar)
Level 2 Soil moisture 15 km (ISEA), swath EEF /NetCDF.25 km, global, EASE- NetCDF
8-12 hours1 d
ESACATDS
Level 3 Brightness Temperature and Soil Moisture
15 km (ISEA 4H9) grid/ 25 km (EASE) grid depending on product. NETCDF
Daily, 3, 9 days, weekly, monthly
CATDSBEC
Level 4 fine-scale soil moisture
1 km, for Iberian Peninsula; NETCDF1 km for MODIS Tiles
2 daily maps (one asc/ one desc) in NRT
BECCATDS (2017)
Level 4 CATDS Root Zone Soil Moisture
~25 km (EASE grid version 2); NetCDF Daily, 10 days, monthly CATDS
Level 4 Drought Index 25 km EASE 2 grid netcdf Daily, 10 day, Monthly CATDS
Freeze and thaw ~25 km (EASE grid version 2); NetCDF,Northern Hemisphere
Daily Demo data set available from FMI
Surface roughness 25 km NETCDF, global Yearly CATDS
10-day global composite of L3 soil moisture. Credits CATDS/CESBIO
Root zone soil moisture in m3/m3. Credits CATDS/CESBIO
Science and composite products/ Latency > 3 hours
DATA ACCESSESA: http://smos-diss.eo.esa.int/CATDS www.catds.fr/Products/Available-products-from-CPDCBEC: http://cp34-bec.cmima.csic.es/land-datasets
SMOS swath-based L2 soil moisture product. Credits ESA
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
Acknowledgments
32nd URSI General Assembly and Scientific Symposium | Montreal, Canada | Aug 19-26, 2017 | Steven Chan et al.
This work is funded by the 2016 NASA ROSES call for the
Science Utilization of the Soil Moisture Active Passive Mission (SUSMAP)
sponsored by the NASA Terrestrial Hydrology Program
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
Flow Diagram
SMAPSMOS
L1 gain/offset adjustment
SMOS L1 TB
SMAP L1 TBSMOS/SMAP L1 TBfine grid resampling on
9 km EASE Grid 2.0
SMAP AlgorithmSMAPAncillary Data
SMOS/SMAP soil moisture at 9 km(2009 – present)
32nd URSI General Assembly and Scientific Symposium | Montreal, Canada | Aug 19-26, 2017 | Steven Chan et al.
Part 1
Part 2
Chan et al (2017)
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
Part 2: Soil Moisture Retrieval
32nd URSI General Assembly and Scientific Symposium | Montreal, Canada | Aug 19-26, 2017 | Steven Chan et al.
9 km soil moisture using 6 am SMOS TBs with SMAP algorithm and ancillary data
9 km soil moisture using 6 am SMAP TBs with SMAP algorithm and ancillary data
Jun 2017
Jun 2017
Good agreement
between SMOS and
SMAP
Slight code
differences from
current SMAP
operational setup
(further work
needed)
Consistent TBs,
algorithm, and
ancillary data lead
to consistent
SMOS/SMAP soil
moisture
Chan et al (2017)
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
Part 2: Soil Moisture Retrieval
32nd URSI General Assembly and Scientific Symposium | Montreal, Canada | Aug 19-26, 2017 | Steven Chan et al.
9 km soil moisture retrieval using 6 am SMOS TBs with SMAP algorithm and ancillary data
Jun 2017
Jun 2017
9 km soil moisture retrieval using 6 am SMAP TBs with SMAP algorithm and ancillary data
Chan et al (2017)
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
The SMOS soil moisture validation and intercomparison approach and
results
Yann Kerr, Jean Pierre Wigneron, Beatriz Molero-Rodenas, Rajat Bindlish, Steven Chan, Roberto Fernandez-
Moran, Arnaud Mialon, Amen Al-Yaari, Ali Mahmoodi, Simone Bircher, Nemesio
Rodriguez-Fernandez, Philippe Richaume, Ahmad Al Bitarand François Cabot
CESBIO, INRA ISPA,JPL, GSFC
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
Part 1: SMAP TB vs. SMOS TB
32nd URSI General Assembly and Scientific Symposium | Montreal, Canada | Aug 19-26, 2017 | Steven Chan et al.
Overall SMOS and SMAP in good agreement; SMAP > SMOS (ocean) but SMAP < SMOS (land)
Match SMOS to SMAP to use SMAP’s inversion setup (algorithm and ancillary data)
SMOS TBH (K)
SMAP
TB H
(K)
SMAP
TB H
(K)
SMOS TBH (K)
SMAP
TB H
(K)
SMOS TBH (K)
6:00 am + 6:00 pmMay 2015 – Jun 2017
Before Before
Before
Chan et al (2017)
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
Part 1: SMAP TB vs. SMOS TB
32nd URSI General Assembly and Scientific Symposium | Montreal, Canada | Aug 19-26, 2017 | Steven Chan et al.
SMOS TBH (K)
SMAP
TB H
(K)
SMAP
TB H
(K)
SMOS TBH (K)
SMAP
TB H
(K)
SMOS TBH (K)
After adjustments, SMOS and SMAP exhibit minimal bias over the entire TB range
Separate adjustments needed for 6:00 am TBH, 6:00 am TBV, 6:00 pm TBH, and 6:00 pm TBV
6:00 am + 6:00 pmMay 2015 – Jun 2017
After After
After
Chan et al (2017)
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
Part 1: SMAP TB vs. SMOS TB
32nd URSI General Assembly and Scientific Symposium | Montreal, Canada | Aug 19-26, 2017 | Steven Chan et al.
After adjustments, swath discontinuity between SMOS and SMAP is reduced, though not completely
eliminated due to:
▸ Different observation times away from SMOS/SMAP swath intersection region
▸ Different azimuth angles (SMOS: fore only; SMAP: fore and aft)
6:00 am SMOS TBH overlaid with 6:00 am SMAP TBH(Jun 14 – 16, 2017)
moderate swath discontinuity before adjustments minimal swath discontinuity after adjustments
SMAP6 am
SMOS6 am
SMAP6 am
SMOS6 am
Chan et al (2017)
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
Part 1: SMAP TB vs. SMOS TB
32nd URSI General Assembly and Scientific Symposium | Montreal, Canada | Aug 19-26, 2017 | Steven Chan et al.
After adjustments, SMOS and SMAP exhibit minimal bias over the entire TB range
Customized adjustments necessary for 6:00 am TBH, 6:00 am TBV, 6:00 pm TBH, and 6:00 pm TBV
Bias (SMAP minus SMOS) (K) RMSE (K)
Before Adjustments After Adjustments Before Adjustments After Adjustments
6:00 am ocean TBH 1.18 0.04 2.36 2.02
6:00 am land TBH -1.66 -0.07 3.59 3.14
6:00 am TBH 0.59 0.03 2.66 2.30
6:00 pm ocean TBH 0.57 0.01 2.20 2.10
6:00 pm land TBH -2.10 -0.02 3.91 3.27
6:00 pm TBH -0.04 0.00 2.69 2.42
6:00 am ocean TBV 0.72 -0.02 2.04 1.88
6:00 am land TBV -2.51 -0.05 3.81 2.81
6:00 am TBV 0.06 -0.03 2.50 2.11
6:00 pm ocean TBV 0.57 -0.03 2.12 2.01
6:00 pm land TBV -2.67 0.00 4.00 2.92
6:00 pm TBV -0.16 -0.03 2.66 2.25
Chan et al (2017)