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Integration of remote sensing and land surface models for continental scale analysis carbon and water fluxes.


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Project overview


Terrestrial ecosystems annually sequester about one quarter of anthropogenic emissions of CO2 and as such provide an ecosystem service worth millions of dollars [1]. In sequestering carbon and producing food and fibre, they also use water. Indeed, water use by vegetation (through evapotranspiration) is the biggest loss term in the terrestrial water balance and, through land management, is the only term in the water balance that we can manage. Terrestrial landscapes also affect the local and regional climate through changing the surface properties of reflectance and roughness [2]. Quantification of the exchanges of carbon, water and energy in space and time therefore provides critical information required to underpin the sound management of Australia’s landscapes to maintain key ecosystem services. Despite its great importance to understand and manage the impact of land use on carbon sequestration and water availability, such knowledge has not been readily available for many of Australia’s unique ecosystems.

The project objective is to achieve a step change improvement in land surface models (LSM) by using the spatial distribution of critical biophysical and canopy structural parameters derived from remote sensing. LSM parameters are commonly a mixture of data-constrained parameters (e.g. eddy covariance flux data, streamflow and soil moisture data) and fixed parameters (e.g. literature values, soil atlas, plant functional types). We will replace key fixed parameters with parameters derived from remotely-sensed data, which will reflect the spatial variability and biophysical attributes of the vegetation more realistically. We will verify the modelling results with flux data, which cover the full range of temporal processes under consideration.

Project output is a time series of energy fluxes (sensible and latent heat (evapotranspiration) and carbon fluxes) for key Australian ecosystems. We will analyse the data to investigate inter and intra-annual variability of these fluxes1 with focus on the role of the major drivers (energy and water) and their temporal variation due to El Nino and La Nina cycles.


1 including the OzFlux PIs running sites at the ecosystems in this analysis to make use of their 'local' knowledge.


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[1] Canadell, J. G., & Raupach, M. R. (2008). Managing forests for climate change mitigation. Science (New York, N.Y.), 320(5882), 1456-7. doi:10.1126/science.1155458
[2] Anderson, R. G., Canadell, J. G., Randerson, J. T., Jackson, R. B., Hungate, B. a, Baldocchi, D. D., Ban-Weiss, G. a, et al. (2011). Biophysical considerations in forestry for climate protection. Frontiers in Ecology and the Environment, 9(3), 174-182. doi:10.1890/090179


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Products and outcomes


Final report


Integration of remote sensing and land surface models.

FINAL REPORT available for download [1.5MB]

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Workshop Report (13–17 February 2012)


The working group met to explore the improvement in Land Surface Models (LSMs) that can be achieved through integration with remote sensing and flux tower observations by i) using remote sensing data to quantify model parameters in space and time ii) using footprint weighted remote sensing data and model output to benchmark the model with flux measurements and to explore the interannual variability in the carbon dynamics in continental Australia by analyzing 30 years of model output using the CSIRO Atmosphere Biosphere Land Exchange model (CABLE) and a much simpler model, based on light use efficiency, to analyse trends, covariance with climate drivers and indices (e.g. ENSO, IOD, Monsoon) and spatially attribute these anomalies to the underlying processes.


It was agreed to run CABLE/BIOS2 with local meteorology for fPAR values derived from various vegetation indices (VIs), sensors and hence pixel sizes to i) assess which VI leads to the best agreement between weighted model output and flux tower observation and to ii) assess the uncertainty associated with using the corresponding AVHRR VI (which will be available for 30+years). For model validation we agreed to use data from two towers with >10 years time series (Tumbarumba and Howard Springs) and data of 14 towers with 1-4 years which provides good spatial and temporal coverage. The degradation of the modelling results using gridded meteorological drivers rather than local meteorological data in conjunction with the best performing AVHRR VI will also be assessed.


We will carry out a covariance analyses between climate indices, climate drivers and the time series of remote sensing and measured fluxes and assess the temporal scale at which we observe highest linearization between the major drivers and the fluxes by determining where the change in the covariance matrix reaches a minimum. This is the temporal scale at which simpler models are expected to perform best. A simple model, based on light use efficiency, will be developed and tested with AVHRR data.


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Workshop 13-17 February 2012

back row: Jason Beringer (Monash University), Eva van Gorsel (CSIRO), Youngryel Ryu (Seoul National University), Natasha Kljun (Swansea Unviersity), Dong Gill Kim (Hawassa University, Ethiopia), Matt Paget (CSIRO).

front row(s): Chris Pickett-Heaps (CSIRO), Alfredo Huete (University of Technology, Sydney), Alex Held (CSIRO), Damian Barrett (CSIRO and University of Queensland), Pep Canadell (CSIRO), Brad Evans (Macquarie University), Ying-Ping Wang (CSIRO).

absent: Tomasz Bednarz (CSIRO), Vanessa Haverd (CSIRO) and Lindsay Hutley (Charles Darwin University).


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Integration of remote sensing and land surface models.

FINAL REPORT available for download

Last Updated on Sunday, 08 February 2015 16:05