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Soil Moisture Remote Sensing Science and Applications
Xiwu Zhan, Chris Hain, Jicheng Liu, Li Fang, Jeff Yin, Zhengpeng Li, Min Huang, Mitch Schull
Collaborators:
L. Zhao, H. Ding, W. Zheng, M. Ek, M. Anderson, M. Temimi, T. Lakhanka, J. Wen, K. Yang, C. Shi, Y. Zheng, R. Wu, et al.
University of Maryland ESSIC CICS Science Conference 2016
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X. Zhan, NOAA-NESDIS. CICS Science Conference, December 7, 2016 2
Why Soil Moisture Soil Moisture Remote Sensing Science
MW, TIR Satellite soil moisture product systems
SMOPS, GET-D Soil Moisture Data Applications
NWP, Drought, Flood, Crop, etc.
Outline
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3 X. Zhan, NOAA-NESDIS. December 7, 2016
Why Soil Moisture for NOAA
Soil moisture controls land surface mass and energy partitioning through impacting evapotranspiration
Mass balance
Recharge/Runoff R
Evap E Precip P
Soil moisture S
Evaporation & soil moisture couple mass & energy balances at land surface
L is the latent heat of vaporization: 2.5x106 [J/kg]
−= PdtdS V E(T, S) - R(S) dS (T) - H(T) - LˑE(T, S) netRad
dtdTc =dT
=
Ground G
Energy balance Latent LˑE Radnet Sensible H
Soil temp T
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4 X. Zhan, NOAA-NESDIS. December 7, 2016
Why Soil Moisture for NOAA
Dry Soil
Moist Soil
5°C
Dry Surface
Moist Surface
Deep Mixing up to 1.5 Km Altitude
Shallow Mixing to 1.0 Km
May 10 Dry soil, clear, mild winds. (LE≈H)
May 18 90 mm Rain
May 20 Moist soil, clear, mild winds. (LE>H)
CASES’97 Field Experiment, BAMS, 81(4), 2000.
Over land actual evapotranspiration depends on both incident energy and soil moisture.
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5 X. Zhan, NOAA-NESDIS. December 7, 2016
24-Hours Ahead Atmospheric Model Forecasts
Observed Rainfall 0000Z to 0400Z 13/7/96 (Chen et al., NCAR)
Buffalo Creek Basin
"The strong motivation for this land data assimilation and land-monitoring space missions such as Hydros is that the land states of soil moisture, soil ice, snowpack, and vegetation exert a strong control on ...the heating and moistening of the lower atmosphere…forecast of tomorrow's heat index, precipitation, and severe thunderstorm likelihood."
Louis Uccellini, NCEP
“The experience of the last ten years at ECMWF has shown the importance of soil moisture...Soil moisture is a major player on the quality of weather parameters such as precipitation, screen-level temperature and humidity and low-level clouds."
Anthony Hollingworth, ECMWF
Soil Moisture Data Will Improve Numerical Weather Prediction (NWP) Over the Continents by Accurately Initializing Land Surface States
With Realistic Soil Moisture
Without Realistic Soil Moisture
Observed Rainfall from intense storm in Colorado: Model forecasts with and w/o soil moisture:
Actual storm event is forecasted accurately only if soil moisture information is available.
Soil Moisture Impact on Weather Forecasting
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6 X. Zhan, NOAA-NESDIS. December 7, 2016
Soil Moisture Impacts on Others
Drought & Ag Production
Military Mobility
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7 X. Zhan, NOAA-NESDIS. December 7, 2016
Two ways to retrieve soil
moisture from satellites:
• Microwave (MW): Observed MW brightness temperature depends on soil dielectric constant that is related to soil moisture:
– Strength: higher reliability based on direct physical relationships
– Weakness: antenna technology limits spatial resolution
• Thermal Infrared (TIR): Observed surface temperature changes result from surface energy balance that is dependent on soil moisture:
– Strength: TIR sensor could have higher spatial resolution
– Weakness: relies on land surface energy balance model that is prone to input data errors.
R soil
T c T ac
H s
T s
R a H = H c + H s
R x
H c
T a
5 km
R soil
T c T ac
H s
T s
R a H = H c + H s
R x
H c
T a T a
5 km Two-
Sou
rce
Mod
el (
ALE
XI)
TRAD fc
ABL
TMI
Microwave Sensitivity By Wavelength and Vegetation Density
0.0
1.0
2.0
3.0
4.0
0 5 10 15 20 25 Wavelength (cm)
Sens
itivi
ty (
Del
ta T
B /
Del
ta V
ol S
M)
BARE
VEGETATION WATER CONTENT (kg/m2)
1
2
4
0
SSM/I AMSR /WindSat SMOS / SMAP
Soil Moisture Remote Sensing Science
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8 X. Zhan, NOAA-NESDIS. December 7, 2016
MW Soil Moisture Remote Sensing Technology
1970s 1980s 1990s 2000
Ground and aircraft development and verification of theory (1 m)
Limited global mapping, demonstrate feasibility (50 km)
Large scale mapping and integrated hydrologic research
(1 km)
Exploration of spatial and temporal concepts (100 m)
Field Experiments
SMOS/Aquarius
AMSR/WindSat/ ESCAT/ASCAT
ESTAR
PBMR
Evolution of Soil Moisture Mapping Sensors
Soil
Moi
stur
e Se
nsin
g Te
chno
logy
SMAP Improved global mapping
(50 km)
Broad science, high spatial resolution, higher sensitivity (10 km)
2010
SMMR/ SSM/I
Limited global mapping (150 km)
Limited global mapping, operational capability (50 km)
AMSR2
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9 X. Zhan, NOAA-NESDIS. December 7, 2016
MW Soil Moisture Remote Sensing: Retrieval Algorithm Science
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10 X. Zhan, NOAA-NESDIS. December 7, 2016
TB,icmp = Ts {er,p exp (-τi/cosθ) +
(1 – ω) [1 – exp (-τi/cosθ)] [1 + Rr,i exp (-τi/cosθ)]} τi = b *VWC Rr,i = Rs exp(h cos2θ) Rs = f(ε) -- Fresnel Equation ε = g(SM) -- Mixing model TB,i
obs = TB06h , TB06v , TB10h , TB10v , TB18h , TB18v
}min{2
6
1
,,2 ∑=
−=
i i
cmpiB
obsiB TT
σχ
Multi-channel Inversion Algorithm (MCI):
Soil Moisture Retrieval Algorithms (Njoku & Li, 1999)
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11 X. Zhan, NOAA-NESDIS. December 7, 2016
TB,icmp = Ts {er,p exp (-τi/cosθ) +
(1 – ω) [1 – exp (-τi/cosθ)] [1 + Rr,i exp (-τi/cosθ)]} τ = f(MPDI) ,MPDI = (TBv-TBh)/(TBv+TBh) eh = f(es, h, Q) es = f(ε) -- Fresnel equation ε = f(SM) -- Mixing model (Dobson’85, others) Ts = f(TB37v) or Ts
LSM TBh
obs = TB06h , TB10h or TB18h
}min{2
6
1
,,2 ∑=
−=
i i
cmpiB
obsiB TT
σχ
Land Parameter Retrieval Model (LPRM):
Soil Moisture Retrieval Algorithms (Owe, de Jeu &
Holmes, 2008)
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12 X. Zhan, NOAA-NESDIS. December 7, 2016
TB10h = Ts [1 –Rr exp (-2τ /cosθ)] Rr = Rs exp(h cos2θ) Rs = f(ε) -- Fresnel Equation ε = g(SM) -- Mixing model Ts = reg1(TB37v) or Ts
LSM
τ = b * VWC VWC = reg2(NDVI)
Single Channel Retrieval (SCR) Algorithm:
SCR can be applied to different sensors for a consistent satellite soil moisture data product.
Soil Moisture Retrieval Algorithms
(Jackson, 1993)
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13 X. Zhan, NOAA-NESDIS. December 7, 2016
Impact of Tau = f(MPDI) on SM Retrievals:
LPRM with τ = f(MPDI)
MCI with Ts= f(TB37v)
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14 X. Zhan, NOAA-NESDIS. December 7, 2016
Impact of Ts error on LPRM/MCI Retrievals:
Ts + 2K
Ts – 2K
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15 X. Zhan, NOAA-NESDIS. December 7, 2016
No Ts errors
No Ts errors
Impact of Ts error on LPRM/MCI Retrievals:
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16 X. Zhan, NOAA-NESDIS. December 7, 2016
Impact of Tb error on LPRM/MCI Retrievals:
Tbh + 2K Tbv - 2K
Tbh - 2K Tbv + 2K
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17 X. Zhan, NOAA-NESDIS. December 7, 2016
No Tb errors
No Tb errors
Impact of Tb error on LPRM/MCI Retrievals:
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18 X. Zhan, NOAA-NESDIS. December 7, 2016
Impact of Tau error on SCA Retrievals:
τ + 0.01
No τ error
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19 X. Zhan, NOAA-NESDIS. December 7, 2016
Impact of Tau error on SCA Retrievals:
τ + 0.05
No τ error
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20 X. Zhan, NOAA-NESDIS. December 7, 2016
Impact of Tau error on SCA Retrievals:
τ + 0.1
No τ error
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21 X. Zhan, NOAA-NESDIS. December 7, 2016
SCR and MCI Compared with In Situ
Measurements
Soil Moisture Retrieval Validation
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22 X. Zhan, NOAA-NESDIS. December 7, 2016
Soil Moisture Operational Product System (SMOPS)
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23 X. Zhan, NOAA-NESDIS. December 7, 2016
Soil Moisture Operational Product System (SMOPS)
• SM ingesting
• SM retrieving
• SM merging
• Reprocessing for the archive product
1
2
3
4
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24
Soil Moisture Operational Product System SMOPS 2.0/3.0
SMOS ASCAT
Blended
NWP models NCEP
GFS/NAM NLDAS/GLDAS
AFWA, etc
NESDIS SMOPS 3.0 upgraded over the current SMOPS 2.0 will Ingest NASA SMAP NRT and GPM TB data to retrieve global soil moisture with NOAA ancillary Data
SMAP SM
NASA GPM TB
NRT SMOS AMSR2
NASA SMAP NRT TB
GPM SM SMAP SM
NOAA Ancillary Data
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SMOPS Product Suite
Products Description Format Projection Spatial Coverage
Spatial Resolution
Main Purpose
SMOPS 6-Hour Product
SMOPS 6-hour Gridded Soil Moisture
GRIB2 Lat/Long Global 0.25 degree (720x1440)
For operational
use
SMOPS Daily Product
SMOPS Daily Gridded Soil
Moisture GRIB2 Lat/Long Global 0.25 degree
(720x1440)
For operational/research use
SMOPS Archive Product
SMOPS Daily Gridded Soil
Moisture netCDF4 Lat/Long Global 0.25 degree
(720x1440) For research
use
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SMOPS Product Data Layers
Soil Moisture Product
SMOPS Version 1.3 SMOPS Version 2.0 SMOPS Version 3.0
SMOPS Blended √ (1) √ (1) √ (1)
NOAA AMSR-E √ (2) × ×
NOAA NRT SMOS × √ (2) √ (2)
ESA SMOS √ (3) √ (3) √ (3)
EUMETSAT ASCAT-A √ (4) √ (4) √ (4)
EUMETSAT ASCAT-B √ (5) √ (5) √ (5)
NOAA WindSat √ (6) × ×
NOAA AMSR2 × √ (6) √ (6)
NOAA GMI × × √ (7)
NOAA NRT SMAP × × √ (8)
NASA SMAP × × √ (9)
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NOAA Soil Moisture Operational Product System (SMOPS)
http://www.ospo.noaa.gov/Products/land/smops/index.html
• Developed by NOAA/NESDIS/STAR
• Operationally running at NOAA/NESDIS/OSPO
Operational data access contact: [email protected] Science and historical data contact: [email protected], [email protected],
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28 X. Zhan, NOAA-NESDIS. December 7, 2016
TIR Soil Moisture Remote Sensing: Retrieval Algorithm Science
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TSoil
Tsoil & Tveg
soil evaporation
Root uptake
infiltration
drainage
PRECIPITATION
transpiration & evaporation
Rootzone moisture
Sfc moisture
Soil hydraulic parms
Veg stress parms
Bare soil evap parms
Soil moisture holding capacity
Root distribution parms
WATER BALANCE APPROACH REMOTE SENSING APPROACH (“forward modeling”) (“inverse modeling”)
Given known radiative energy inputs, how much water loss is required to keep the soil and vegetation at the observed temperatures?
transpiration & evaporation
soil evaporation
SURFACE TEMPERATURE
Tveg
runoff
From Martha Anderson
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Two-Source Energy Balance (TSEB)
Local scale TRAD - Infrared thermometer Ta - Micromet tower
Surface temp: Air temp:
TRAD (φ), fcTRAD (φ), fc
Rsoil
TcTac
Hs
Ts
Ra H = Hc + Hs
Rx
Hc
Ta
Blending heighTRAD(θ) ~ fc(θ)Tc + [1-fc(θ)]TS
Norman, J. M., W. P. Kustas, and K. S. Humes (1995) Agric. For. Meteorol., 77, 263-293.
Canopy transpiration (LEC) from PT or LUE scheme
Soil evaporation as residual: LES = RN – H – G - LEC
Canopy and soil H from T network
From Martha Anderson
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Atmosphere-Land Exchange Inverse (ALEXI)
TRAD (φ), fcTRAD (φ), fc
Rsoil
TcTac
Hs
Ts
Ra H = Hc + Hs
Rx
Hc
Ta
ABL
Ta
ALEXI DisALEXI5 km
30 m
Two-
Sou
rce
Mod
el
TRAD,i(φi), fc,i
i
Ra,i
Blending height
Regional scale ∆TRAD - GOES Ta - MODIS
Landscape scale TRAD - TM, ASTER, MODIS Ta - ALEXI
Surface temp: Air temp:
From Martha Anderson
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ET PET 0.0 0.2 0.4 0.6 0.8 1.0
Landsat 7 – 60m MODIS – 1km
Sensitivity to irrigation From Martha Anderson
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Sensitivity to shallow water tables Simulated climatological water table* Temporal variability in ET/PET
shallow deep low high * Miquez-Macho et al, BAMS, 90, 663-672
From Martha Anderson
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34 X. Zhan, NOAA-NESDIS. December 7, 2016
GOES ET and Drought Product System (GET-D)
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35 X. Zhan, NOAA-NESDIS. December 7, 2016
Data processing unit
External inputs Software system
ALEXI model Daily ET product with QC
ESI product with QC
External outputs
Satellite-based observations
Meteorological data
Ancillary data
Daily fluxes with QC
GET-D General Architecture
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36 X. Zhan, NOAA-NESDIS. December 7, 2016
GET-D Input Data
Name Category Source Description
Brightness temperature Satellite observation GOES GOES East/West Imagery; 11micron/3.9 micron
brightness temperature
Insolation Satellite observation GSIP GSIP real time insolation
Vegetation Index Satellite observation VIIRS VIIRS EVI
Snow mask Satellite observation NOAA IMS IMS Daily Northern Hemisphere Snow and Ice Analysis
Air temperature Meteorological data CFS Surface and pressure level profiles
Specific humidity Meteorological data CFS Surface and pressure level profiles
Geopotential height Meteorological data CFS Surface and pressure level profiles
Wind speed Meteorological data CFS Surface
Downwelling longwave radiation
Meteorological data CFS Surface
Land Cover Ancillary data University of Maryland Land cover classes in 1km resolution (static)
Albedo Ancillary data MODIS Surface Albedo from MODIS (static)
Clear day insolation Ancillary data GSIP Clear day insolation (static)
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37 X. Zhan, NOAA-NESDIS. December 7, 2016
GET-D Output Products
Variables Description ET product with QC Daily ET map
ESI products with QC 2,4,8, 12-week composite drought map
Flux products with QC
Daily sensible heat, soil heat, downward short wave radiation, long wave down/up ward radiation and net
radiation
Coverage North America
Spatial Resolution 8km
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38 X. Zhan, NOAA-NESDIS. December 7, 2016
GET-D Websites NESDIS-OSPO:
http://www.ospo.noaa.gov/Products/land/getd/ NESDIS-STAR:
https://www.star.nesdis.noaa.gov/smcd/emb/droughtMon/products_droughtMon.php
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39 X. Zhan, NOAA-NESDIS. December 7, 2016
Product Accuracy Validation
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Comparison with NLDAS Noah LSM Estimates
ECV 0.51
SMOPS 0.33
ALEXI 0.51
WindSat 0.18
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ECV 0.47
SMOPS 0.39
ALEXI 0.44
WindSat 0.28
Comparison with in situ Measurements
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Soil Moisture Product Applications
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SMOPS combines soil moisture retrievals from multiple satellites sensors to provide a global climatologically consistent soil moisture map with more spatial and temporal coverage.
Positive impact has been shown on the model performance, particularly for precipitation forecasts by assimilating SMOPS blended product into GFS using a simplified EnKF
SMOPS blended product over CONUS in Apr 2012
A Simplified EnKF data assimilation utility implemented in NCEP GFS
Assimilation of SM in NCEP GFS
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Assimilation of SM in NCEP GFS
Assimilating SMAP SM from 8/1 – 8/10/2015 Reduces the warm biases of NCEP GFS four (4) day forecasts of 2 meter air temperature
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UMD / CICS NOAA NESDIS-STAR
T2m Forecasts compared with In-situ Observations (LMV Domain)
Sub-regions in NCEP WRF-NMM forecast verification system
LMV
Average T2m forecasts from WRF Free run and SMAP DA run compared with In-situ measurements
Validation domains: LMV domain (~200 sites)
Validation period: Day 2 forecast, Oct. 2nd – Oct. 9th, 2015
Corrected daytime warm bias
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UMD / CICS NOAA NESDIS-STAR
T2m Forecasts compared with In-situ Observations (LMV Domain; 2-Day validation)
Day 1
Day 2
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Enhance Agricultural Drought Monitoring Using SNPP/JPSS Land EDRs for NIDIS
Jifu. Yin, X. Zhan, C. Hain, J. Liu, L. Fang, M. Ek, J. Huang, M. Anderson, M. Svoboda
• Objectives – Improve current US and global
drought monitoring via using near real time SNPP/JPSS land data products
• Primary sensors involved – S-NPP/VIIRS – GCOM-W1/AMSR2
• Primary ground data – Palmer Drought Severity Index (PDSI) – Standardized Precip ET Index (SPEI) – In situ observations
• Targeted end users – NIDIS of USDA, NOAA and USGS – NWS-NCEP
Risk Reduction/Proving Ground – Hydrology Initiative
Correlations of BDIs with PDSI
Correlations of BDIs with SPEI
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48 X. Zhan, NOAA-NESDIS. December 7, 2016
Soil Moisture Science and Applications
Bayesian or other Merging Method
ALEXI, regression, etc.
Low Rez Soil Moisture
Data
High Rez Soil Moisture
Proxy
TMI/AMSR2/WindSat/ SMOS/Aquarius/GPM/FY-3
ASCAT/SMAP
TM/AVHRR/ MODIS/VIIRS/Radar/
GOES/GOES-R
Soil Moisture in situ Obs
Meteorological Forcing & Land
Surface Obs
Data Assimilation
Agriculture DSS
Drought Monitoring/
Forecast
Flood Monitoring/
Forecast
Military Applications
Water Resources
Management
Numerical Weather
Predictions
High Rez/Quality
Soil Moisture Data
Products
Retrieval Alg.
1-10km, Low accuracy 25-150km, Higher accuracy Ancillary Data
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Thanks from NESDIS soil moisture gang!