Earth Observation Satellite
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The woody vegetation extent for Queensland is attributed with an estimated age in years since the last significant disturbance. The method uses a sequential Conditional Random Fields classifier applied to Landsat time series starting 1988 to predict woody cover over the time period. A set of heuristic rules is used to detect and track regrowing woody vegetation in the time series of woody probabilities and record the approximate start and end dates of the most recent regrowth event. Regrowth detection is combined with the Statewide Land and Trees Study (SLATS) Landsat historic clearing data to provide a preliminary estimate of age since disturbance for each woody pixel in the woody extent. The 'last disturbance' may be due to a clearing event or other disturbance such as fire, flood, droughtrelated death etc. Note that not all recorded disturbances may result in complete loss of woody vegetation, so the estimated age since disturbance does not always represent the age of the ecosystem. The age since disturbance product is derived from multiple satellite image sources and derived products which represent different scales and resolutions: Landsat (30 m), Sentinel2 (10 m) and Earthi (1 m).

Evaporation, Transpiration, and Evapotranspiration Products for Australia based on the Maximum Entropy Production model (MEP). This record is an introduction of a method into the MEP algorithm of estimating the required model parameters over the entire continent of Australia through the use of pedotransfer function, soil properties and remotely sensed soil moisture data. The algorithm calculates the evaporation and transpiration over Australia on daily timescales at the 0.05 degree (5 km) resolution for 2003 – 2013. The MEP evapotranspiration (ET) estimates were validated using observed ET data from 20 Eddy Covariance (EC) flux towers across 8 land cover types in Australia and compared the MEPET at the EC flux towers with two other ET products over Australia; MOD16 and AWRAL products. The MEP model outperformed the MOD16 and AWRAL across the 20 EC flux sites, with average root mean square errors (RMSE), 8.21, 9.87 and 9.22 mm/8 days respectively. The average mean absolute error (MAE) for the MEP, MOD16 and AWRAL were 6.21, 7.29 and 6.52 mm/8 days, the average correlations were 0.64, 0.57 and 0.61, respectively. The percentage bias of the MEP ET was within 20% of the observed ET at 12 of the 20 EC flux sites while the MOD16 and AWRAL ET were within 20% of the observed ET at 4 and 10 sites respectively. The analysis showed that evaporation and transpiration contribute 38% and 62%, respectively, to the total ET across the study period which includes a significant part of the “millennium drought” period (2003 – 2009) in Australia. File naming conventions: E – Evaporation T – Transpiration ET – Evapotranspiration For the 8 day ET, Daily T and ET, the suffix nnn indicates day of year, for example: 001 for January 1, 145 for May 25 (leap year) or 26, etc. While for the daily E, the suffix is in the format mmdd (month,day) for example 0101 for January 1, 0525 for May 25.

This dataset comprises spatially and temporally dynamic estimates of the monthly latent heat flux (λE) and sensible heat flux (H) for all of Australia. The available energy (A, being net radiation [Rn] less the gound heat flux [G]) can be obtained by adding the λE and H datasets provided. Energy variables have been provided as hydrological equivalent units of depth, normalised to daily rates (mm/d). TERN OzFlux Surface Energy Balance (SEB) data were used to scale MODISbased covariates of surface temperature less air temperature (Ts – Ta) and Rn using a Spatial and Temporal General Linear Model (STGLM) to third order. The STGLM SEB model was implemented across all of Australia on a 0.005° spatial grid (~ 500 m) on a monthly timestep from March 2000 through June 2023. Coefficients of the model were determined from the OzFlux network of eddy covariance flux tower data. Three flux tower sites were used to independently validate the accuracy of the model, being Calperum, SA, Howard Springs, NT, and Tumbarumba, NSW. The mean absolute difference (MAD) for λE, H and A was estimated as: 0.37, 0.39 and 0.34 mm/d, respectively. The relative errors determined by the MAD percentage (MADP) for λE, H, and A were estimated to be: 16%, 26%, and 9%, respectively. This dataset represents a new pathway for operational regional to globalscale estimation of dynamic SEB variables.