Vegetation Processes and Carbon Cycle

Evapotranspiration: ETa Simplified Land Surface Temperature (SLST) model Using MODIS Terra Operational version of the NDTI 2-layer SEB model Monthly time step Requires ‘upscaling’ single time-of-day observations/estimates (e.g., Van Niel et al. (2011) & Van Niel et al. (2012)) Tumbarumba RMSD = ~ 0.38 mm d -1 or ~ 11W m -2 SLST Flux tower obs. Van Niel TG, McVicar TR, Roderick ML, van Dijk AIJM, Beringer J, Hutley LB, van Gorsel E (2012) Upscaling latent heat flux for thermal remote sensing studies: Comparison of alternative approaches and correction of bias. Journal of Hydrology , Van Niel TG, McVicar TR, Roderick ML, van Dijk AIJM, Renzullo LJ, Van Gorsel E (2011) Correcting for systematic error in satellite-derived latent heat flux due to assumptions in temporal scaling: Assessment from flux tower observations. Journal of Hydrology 409,

Light use efficiency (LUE) for C modelling Numerous canopy-scale C uptake measurement studies report that C uptake efficiency is greater in diffuse light than direct light (Donohue et al (2014) and the references therein) This is due to relative increase in penetration of diffuse light (cf. direct light) to deeper layers in the canopy (for diffuse light there is limited (to no) self shading within the canopy (cf. direct light) Monteith’s (1977) original LUE model can be modified to include the dependence on diffuse light by using a simple relation between the atmospheric transmission and the diffuse fraction A (or GPP) = C.R s.(LUE).fPAR (LUE) function of R d / R s (ratio of diffuse irradiance [R d ] to global solar irradiance [R s ]) – this can be observed by geostationary satellites Donohue, R.J., Hume, I.H., Roderick, M.L., McVicar, T.R., Beringer, J., Hutley, L.B., Gallant, J.C.,Austin, J.M., Van Gorsel, E., Cleverly, J.R., Meyer, W.S., Arndt, S.K. (2014), ‘Evaluation of the remote-sensing-based DIFFUSE model for estimating photosynthesis of vegetation’, Remote Sensing of Environment, 155, doi: /j.rse Himawari-8/-9 Applications Workshop | JMA 28 No 2014

Total GPP can be split into Tree (T) and Grass (G) components Grass can be split into C4 and C3 (AVHRR based results) Himawari-8/-9 Applications Workshop | JMA 28 No 2014

Research questions for Himawari-8 LUE / GPP modelling Need access to daily R d / R s ratio estimates Need understanding of errors in estimates of R d and R s in all major Australian atmospheric conditions (i.e., tropics vs. temperate latitudes, summer vs. winter and coastal vs. arid) Himawari-8/-9 Applications Workshop | JMA 28 No 2014 Through use of LST for ETa modelling and LUE for GPP modelling, allows the development of integrated water-energy- carbon modelling (downstream applications such as crop forecasting, C accounting etc) This can be performed for all Australia at a daily time-step using Himawari-8 data given our experience with using geostationary and polar orbiting satellite data we have a “running start”) GPP can be modelled using this simple (non- tuned) LUE model

What are the stakeholder needs? AOD, Angstrom Exponent (aerosol transport and air quality) Reflectance, individual or daily/weekly composites Transient environmental phenomena (dust storm, bushfires, algal blooms, floods) Fire, flood, SST, soil moisture and NDVI products Thermal hotspots, Fire perimeter, Water perimeter, Oil slick perimeter, Burn intensity

Corrections for Landsat / Sentinel / + Smoke Aerosol Dust Water Vapour

BRDF Corrections

MODIS Landsat TM/ETM+ Fire

Water and Floods

Vegetation state and trend

Vegetation Clumping and Scale

Production Modelling

How do we deliver?