the two source energy balance model using satellite ... two source energy balance model using...
TRANSCRIPT
The Two Source Energy Balance model using
satellite, airborne and proximal remote sensing
7 years in a relationship
Héctor Nieto
Resistance Energy Balance Models (REBM)
Physics based on an analogy to the Ohm’s Law (Electricity)
– Heat transport is driven by a temperature gradient
– Some resistances oppose to the transport (Stomata, soil & air)
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GHRE ne
𝐻 ∝∆𝑇
𝑟
Python code available at https://github.com/hectornieto/pyTSEB
TSEB inputs:
– Surface temperature
– Leaf Area Index
– Meteo: Sdn, Ta, u and ea
– Albedo/spectral properties
– Fraction of LAI that is
green
– Canopy height & width
Retrieval of canopy (Tc) and soil temperatures (Ts)
Components Tc and
Ts obtained from:
– Dual angle LST
– Very High spatial
res. LST
...or Priestley-Taylor
approach
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𝜆𝐸𝑐 = 𝑓𝑔𝛼𝑃𝑇Δ
Δ + 𝛾𝑅𝑛,𝑐
𝑇𝑟𝑎𝑑 𝜃 ≅ 𝑓𝑐 𝜃 𝑇𝑐4 + 1 − 𝑓𝑐 𝜃 𝑇𝑠
4 Τ1 4
𝑓𝑐 𝜃 = 1 − 𝑒𝑥𝑝−0.5𝐿𝐴𝐼
cos 𝜃
Menenti et al. (2008) Advances in Land Remote Sensing
Scale effects in TSEB
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DTD
(MODIS, 1km)
TSEB
(Landsat, 30m)
DTD+DisAlexi
(MODIS+Landsat)
Bias (W m-2) 22 -10 1
RMSE (W m-2) 47 58 18
correlation 0.45 0.77 0.96
Guzinski et al. (2014) Biogeosciences
TSEB and UAV data on barley
Very high resolution of optical
and TIR data (few cm)
Good performance under
overcast conditions
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TSEB-PT during senescence
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𝜆𝐸𝑐 = 𝑓𝑔𝛼𝑃𝑇Δ
Δ + 𝛾𝑅𝑛,𝑐𝜆𝐸𝑐 = 𝑓𝑔𝛼𝑃𝑇
Δ
Δ + 𝛾𝑅𝑛,𝑐
Leaf Angle Distribution on TSEB
RMSE (W m-2) correlation
2ART 2A 1A 2ART 2A 1A
Barley field 86 106 119 0.61 0.49 0.45
Conifer plantation 147 165 94 0.52 0.44 0.71
Grazed meadow 73 102 68 0.85 0.82 0.87
Implementation of 4SAIL radiative transfer model: TSEB-2ART with AATSR data
– 4SAIL accounts for LAD, emissivity & reflected longwave radiation
– 2 angle (with and without 4SAIL) vs. single angle
𝑓𝑐 𝜃 = 1 − 𝑒𝑥𝑝−0.5𝐿𝐴𝐼
cos 𝜃
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Leaf Angle Distribution on TSEB
Implementation of 4SAIL radiative transfer model: TSEB-2ART with AATSR data
– 4SAIL accounts for LAD, emissivity & reflected longwave radiation
– 2 angle (with and without 4SAIL) vs. single angle
Modification of the extinction coefficient for canopy gap fraction
– Based on the Campbell ellipsoidal LIDF (𝜒)
RMSE (W m-2) correlation
2ART 2A 1A 2ART 2A 1A
Barley field 86 87 97 0.61 0.57 0.72
Conifer plantation 147 160 93 0.52 0.47 0.71
Grazed meadow 73 82 60 0.85 0.84 0.90
Python code will be available at https://github.com/hectornieto/pyTSEBv2
𝑓𝑐 𝜃 = 1 − exp −𝜅𝑏𝑒 𝜒, 𝜃 𝐿𝐴𝐼
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Pushing TSEB beyond its limits
TSEB assumes homogeneuscanopies, or at leastrandomly placed clumpledcanopies
– Affects transmission of radiation through the canopy(e.g. fIPAR/FAPAR)
– Affects wind speedattenuation below the canopy
… uses an empirical factor forsmooth surfaces in soilresistance formulation (Rs)
… assumes negligible heatadvection and heat storageat the canopy
… “only” includes 2 layers
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van Gogh (1889)
Radiation Transmission at Clumped Canopies
Simplified RTM for estimation of canopy and soil net radiation
– Uses effective values of LAI
– Clumping index developed for randomly placed stands
– Only dependent on zenith solar angle Ω(θ)
What about row crops?
Python code will be available at https://github.com/hectornieto/pyTSEBv220/01/2017
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Wind profile in row crops
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Python code will be available at https://github.com/hectornieto/pyTSEBv2
Turbulent heat transport at the soil layer
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Variable LAI
Variable fg
Smooth soil
Constant LAI
Variable fg
Smooth soil
Constant LAI
Variable fg
Rough soil
Python code will be available at https://github.com/hectornieto/pyTSEBv2
𝑟𝑠 ≡ 𝑓 𝑢𝑠, 𝑧0,𝑠, 𝐿
Kustas et al. (2016) Remote Sens. Env.
Exploting high res. Trad for Tc and Ts
Contextual algorithm Thermal sharpening
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Python code will be available at https://github.com/hectornieto/pyTSEBv2
The Energy Balance
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0 GHER en
t
WAFGHER hppen
Latent Heat Flux
Evapotranspiration
Net Radiation
Sensible Heat FluxFixation of CO2 for
photosynthesis
Heat flux leaving the
layer: Soil heat flux
Energy advection
Rate of energy storage
No advection With advection
Future steps
Validation and assessment of TSEB transpiration
– Can transpiration estimates provide added value to irrigationmanagement compared to ET and/or other methods?
– Application to orchards (UAB MSc project)
– Evaluation of TSEB gs for yield forecast
TSEB is one of many ET models
– Evaluation of other models/approaches (w/ USDA, CESBIO, UCLM?)
– Ensemble modeling for uncertainty assessment (w/ USDA)
What about three sources?
– vine+grass+soil (w/ USDA/Raimat) or a “dehesa” (w/ CSIC, IFAPA)
Data assimilation of remote sensing into
– crop models/DSS for yield forecast and irrigation management• From instantaneous ET to daily estimates
– Weather forecast models, (w/ meteoSIM/meteoCat?)
– Hydrological models, (w/ DHI?)
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Future steps
Retrieval of LAI, fPAR/fIPAR, fg/Ca+b using radiative transfer models, w/ CCHS-CSIC
– Use of cloud points/LiDAR in the retrieval (w/ UdL/Mariano Garcia)
– Parallel/efficient processing of RTM inversion (w/ Computer ScienceDept./hired staff)
Operational satellite daily estimates of ET and crop stress
– Fusion of Sentinel 2 (VNIR 10m), Landsat-8 (30m) and Sentinel 3 (TIR 1km). (w/ ESA)
– Temporal gapfilling , STARFM (w/ USDA and IFAPA)
– Potential of microwaves? (w/ CESBIO/IsardSat/RyC?)
Automatic processing of imagery
– Download and preprocessing of Copernicus (Sentinel+3rd parties) satellitedata (MODTRAN/libRadTran)
– Mosaicking, collocation and correction of airborne data (Photoscan/hired staff)
– Explore termal sharpening methods (UAB MSc project)
Others…
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Gràcies!
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Resistance Energy Balance Models (REBM)
One-source vs. Two-source
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Kustas & Anderson (2009) Agric. For. Meteo., 149
𝐻 ∝∆𝑇
𝑟
Can we apply TSEB model with a single directional
observation?
Iterative process using Priestley and Tailor parameterization
– Assumes green vegetation transpires at a potential rate (well watered, αPT=1.26)
– First estimate of Hc and hence Tc Ts & Hs
– Iteration until realistic fluxes (H & LE > 0)
– Need to estimate fraction of green vegetation (fg)
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CnPTgC RfE ,
ccnc ERH , a
p
xcc T
C
rHT
𝑇𝑠4 ≅
𝑇𝑟𝑎𝑑 𝜃 4 − 𝑓𝑐 𝜃 𝑇𝑐4
1 − 𝑓𝑐 𝜃
Leaf Area Index and optical remote sensing
Differential absorption/refraction in the optical spectrum. Usually between red and near infrared
Canopy structural variable: advantage of using multiangularinformation
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Single observation Multiangular observations
Appendix Two Source Energy Balance model