Progress with land DA P. Lewis UCL Geography & NERC NCEO
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?
Testing: compare ‘diagnostics’ (e.g. fAPAR, LAI) Brut et al. 2009 Biogeosciences ISBA MODIS CYCLOPES LAI as diagnostic variable for comparison Pragmatic: rescale and smooth (‘bias’) Essentially use EO phenology
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++
Initial efforts: Quaife et al. 2008 (+) Flux tower site 1: Oregon (‘Young’) Shaded crown Illuminated crown Illuminated soil Shaded soil 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),1347-1364.
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
Leaf Area Index • Area based measure of leaf amount • Related to mass-based through SLA • Generally assumed constant SLA Kattge et al., 2011
Data Assimilation and EOLDAS • Make all terms EO sensitive to dynamic • Weak constraint 4DVAR Lewis et al. 2012 RSE
EOLDAS++ • Follow-on project (2012-2014) • Integrate with model (with Reading) • Deal with snow/soil water • Build in JULES-like vegetation model and Observation operators • Include passive microwave obs. op.
Regularisation for albedo Quaife and Lewis 2010
Change detection: disturbance Relax constraint at discontinuities
Disturbance • 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
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?
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)
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?
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
Conclusions 1/2 • LAI products still not optimally used • Lack of uncertainty information
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)
Canopy Scaling of leaf process Sellers 1992 canopy process scaling model: • Assume leaf N, Vmax, Vm profiles distributed according to fAPAR profile • obtain scalar P from (top) leaf to canopy scale process (assim., resp, transp.) wv=0.2
Canopy Photosynthesis If horizontal heterogeneity in LAI (Sellers, 1992) • Spatial variation in fAPAR • But fAPAR scales ~linearly • If Ac,ketc constant • Aci ~ fAPARi/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
scattering asymmetry: impact • Often assume scattering isotropic • For diffuse fluxes, e.g. d-Eddington • radiatively account for • asymmetry in phase fn • by mapping to equivalent LAI and w • e.g. NIR: w=0.9, e.g. f=0.3, L’=0.73L, w’=0.86 f: fractional scattering in fwd peak
Problems in the retrieval of variables : balance betweenaccuracy and precision Selection of solutions within a Look Up Table (LUT). Measurement uncertainties Median of cases within ±1σ Best reflectance match Very little bias but large scattering Accurate, not precise Smaller scattering but larger biases Precise, not (always) accurate Importance of: - the retrievalmethod - knowledge of uncertainties (model and measurements) - the prior distribution of input variables
Shoot-scale clumping reduces apparent LAI pshoot=0.47 (scots pine) p2<pcanopy Shoot-scale clumping reduces apparent LAI Smolander & Stenberg RSE 2005
Scaling properties Weiss et al. 2000
Clumping impact Govind et al. 2010