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Progress with land DA P. Lewis UCL Geography & NERC NCEO

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Integration of RS products into process models Testing: evaluate model performance via diagnostic variables Forcing: using RS estimates as new updates of state variables Assimilation: adjusting model parameters or initial conditions so that diagnostic variables simulated by the model is close to RS estimates What are the requirements of the models/EO?

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Testing: compare ‘diagnostics’ (e.g. fAPAR, LAI) Brut et al Biogeosciences ISBA MODIS CYCLOPES LAI as diagnostic variable for comparison Pragmatic: rescale and smooth (‘bias’) Essentially use EO phenology

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Barriers to effective DA Why are products different? Different assumptions/treatments/(datasets) In any case inconsistent with model assumptions … Radiance DA DA/comparisons with low level EO Advantages: ‘control’ over data interpretation assumptions i.e. definition of observation operator: consistency ? (potentially) include multiple EO (in consistent manner) More easily treat uncertainties++

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Initial efforts: Quaife et al (+) T. Quaife, P. Lewis, M. DE Kauwe, M. Williams, B. Law, M. Disney, P. Bowyer (2008), Assimilating Canopy Reflectance data into an Ecosystem Model with an Ensemble Kalman Filter, Remote Sensing of Environment, 112(4), Shaded crown Illuminated crown Illuminated soil Shaded soil Flux tower site 1: Oregon (‘Young’)

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Issues NEP good, but actually over-estimate GPP … Partial structural consistency ‘lumped’ ecosystem model (only one canopy layer) ‘effective’ LAI Even though tried to account for canopy-scale clumping Fixed parameters (e.g. leaf chlorophyll): Because of radiometric trade-offs between LAI & e.g. chlorophyll Because assume (e.g.) SLA known (& fixed) Because of partial structural consistency

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Leaf Area Index Area based measure of leaf amount Related to mass-based through SLA Generally assumed constant Kattge et al., 2011 SLA

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Data Assimilation and EOLDAS Make all terms EO sensitive to dynamic Weak constraint 4DVAR Lewis et al RSE

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EOLDAS

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EOLDAS++ Follow-on project ( ) Integrate with model (with Reading) Deal with snow/soil water Build in JULES-like vegetation model and Observation operators Include passive microwave obs. op.

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Regularisation for albedo Quaife and Lewis 2010

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dailies BRDF f 0 (SW, NIR, VIS= r,g,b)

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Change detection: disturbance Relax constraint at discontinuities

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Disturbance

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Current: Edge-preserving DA in time Next Extend spatially Multiple constraints (FRE) Build in model interpretation Initially FCC Most of tools in place to track state within DA system To estimate biomass loss and recovery

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Guanter et al. (2012) RSE accepted: Fs GOSAT

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Fs Modelling

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First model Initial exploration with regulariser Compare to environmental constraints Multi-model DA MPI-GPP, PEM, Fs data (linear GPP model) Use to constrain extrapolation of MPI observations Data quite noisy How far to go with GOSAT?

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Summary weak constraint: regularisation (xval): Enable to treat ~all terms EO sensitive to E.g. chlorophyll etc. Build in disturbance/change Time/(space) Multiple model/data constraints Working out how to deal with new observations (Fs)

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Where next? EO-LDAS++ Exploit EOLDAS ideas in model-data integration More observation operators & underlying process model Structural consistencies / learn from TRY (Terrabites) Disturbance DA Build up: spatial; FRE; FCC; process model … testbed & high res tracking system for C emissions and interaction with vegetation (e.g REDD+ work with Edinburgh) Fs? More testing, examining at higher spatial & time res. (DA) Integrate with rest of DA work Interface to atmospheric models?

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Thank you

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Conclusion Integration of RS products into process models Testing; forcing; assimilation Main EO role so far constraining LC & timing (phenology, snow) Barriers to progress … Model/interpretation inconsistencies / fixed parameters Need to work on this in observations & models Important tools Weak constraint DA ‘low level’ DA

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Conclusions 1/2 LAI products still not optimally used Lack of uncertainty information

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Conclusions 2/2 Clumping major issue Potentially accessible from EO Or can model impacts even in simple models Minimum requirement: LAI and crown cover… BUT do we need to deal with it? Or is effective LAI (i.e. including clumping) sufficient? If so, significant implications for EO efforts And model testing (Keep in mind need for direct/diffuse on fAPAR/albedo)

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Thank you

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Canopy Scaling of leaf process Sellers 1992 canopy process scaling model: Assume leaf N, V max, V m profiles distributed according to fAPAR profile obtain scalar from (top) leaf to canopy scale process (assim., resp, transp.) v =0.2

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Canopy Photosynthesis If horizontal heterogeneity in LAI (Sellers, 1992) Spatial variation in fAPAR But fAPAR scales ~linearly If Ac,k etc constant Ac i ~ fAPAR i /k So Ac (etc) scale linearly (in the absence of variations in forcings and process rates) BUT does not nec. follow if other leaf process to canopy scalings assumed E.g. per layer A in JULES Different assumptions about leaf N vertical distribution

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scattering asymmetry: impact Often assume scattering isotropic For diffuse fluxes, e.g. -Eddington radiatively account for asymmetry in phase fn by mapping to equivalent LAI and e.g. NIR: =0.9, e.g. f=0.3, L’=0.73L, ’=0.86 f: fractional scattering in fwd peak

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Problems in the retrieval of variables : balance between accuracy and precision 32 Best reflectance match Median of cases within ±1σ Very little bias but large scattering Accurate, not precise Smaller scattering but larger biases Precise, not (always) accurate Selection of solutions within a Look Up Table (LUT). Measurement uncertainties Importance of:- the retrieval method - knowledge of uncertainties (model and measurements) - the prior distribution of input variables Importance of:- the retrieval method - knowledge of uncertainties (model and measurements) - the prior distribution of input variables

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LAI anomalies

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Scaling properties Weiss et al. 2000

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Differences depending on directionality

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Clumping impact Govind et al. 2010

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Also interest in non-photosynthetic vegetation e.g. for fire

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