Using Space Based Sensors to Characterise the Earth System

Remote Sensing and Earth Observation


The HALO Group has considerable expertise and experience in using a wide variety of remote sensing data for Earth systems characterisation. While the focus of our work is often on terrestrial hydrological systems, we have undertaken a range of activities that explore applications in oceanic, atmospheric and even arctic applications. Following is a brief overview of this work, with links to ongoing projects that team members are engaged in. Further details can be found within the page links and also under the Research tab. 
  • Remote sensing of evapotranspiration: Prof McCabe has more than 10 years of experience in the estimation of land surface heat fluxes from satellite systems. Together with a number of his team and with international collaborators, he has explored multi-scale retrieval of evapotranspiration from multiple satellite sensors, intercomparison projects to evaluate satellite predictions, as well as current activities with international space agencies (ESA) and programs (GDAP) to develop regional and global scale flux estimates. 

    Further information on this work can be found here. 
​Multi-scale/multi-temporal analysis of land surface heat fluxes over an agricultural site in Australia. See Ershadi et al. (2013)​ for details. 
  • SoilMoistureOZ.pngMicrowave based remote sensing of soil moisture​​Prof McCabe has been actively engaged in the remote sensing of soil moisture since 2003, publishing a number of papers examining the retrieval accuracy of the (then) recently launched AMSR-E sensor (see A, B, C). Recently, we have been working with Prof Richard de Jeu from the Vrije University in Amsterdam to develop a long-time series of harmonized soil moisture retrievals using a range of passive (and active) microwave sensors. These data provide great insights into hydrological behaviour and dynamics and ​offer improved understanding into a range of linked processes (land-atmosphere interactions, droughts, heat-waves etc). 

    Further information on this work can be found here

  • Remote sensing of vegetation optical depth: In addition to using microwave based systems to interpret soil moisture dynamics at the near surface (see above), one of the most interesting variables that can be derived from such sensors is information on the aboveground living biomass, which is retrieved through a property that is termed the vegetation optical depth (VOD). The VOD acts as an indicator of the water content of both woody and leaf components and is distinct from optical vegetation remote sensing data such as the normalized difference vegetation index in that it is: (a) less prone to saturation in dense canopies; (b) sensitive to both photosynthetic and non-photosynthetic biomass; and (c) less affected by atmospheric conditions. As such, it provides a great deal of information on vegetation dynamics, phenology and change, particularly with the capacity to develop long-term data sets.  
    Annual average vegetation optical depth (VOD) for 1988-2008. Regionas likely to be affected by open water are masked grey.
  • Remote sensing of vegetation (reflectance/NIR)The reflected satellite signal in the visible to shortwave infrared region has great utility for detecting vegetation dynamics and physiological condition at a range of spatial and temporal scales, highly advantageous for agricultural monitoring and management activities. However advances in the utilization and interpretation of available satellite sensor data is still needed to realize the full potential. The HALO group is involved in activities to advance the use of multispectral, superspectral and hyperspectral remote sensing data for retrieving vegetation characteristics. We are also engaged in studies on their relationship to plant functional traits, validation against in-situ measurements and integration into land surface models. 

    Further information on this work can be found here
Apart from these particular research themes, the HALO Group is active in a wide variety of development and application of remote sensing data, from characterising land cover and land use changes, mapping and monitoring land surface features and structures (DEMs) to exploring features in the linked oceanic and atmospheric domains. 

Selected Publications

  1. Ershadi A, McCabe MF, Evans JP, Walker JP (2013) “Effects of spatial aggregation on the multi-scale estimation of evapotranspiration” Remote Sensing of Environment 131, 51-62
  2. Kalma JD, McVicar TR and McCabe MF (2008), "Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data", Surveys in Geophysics, vol. 29, no. 4-5, pp. 421 - 469
  3. Jimenez and others (2011), "Global intercomparison of 12 land surface heat flux estimates" Journal of Geophysical Research, 116(2), No. D02012
  4. Mueller and others (2011), "Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations", Geophysical Research Letters, vol. 38, no. 6, pp. L06402, 
  5. McCabe MF and Wood EF (2006), "Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors​", Remote Sensing of Environment, vol. 105, no. 4, pp. 271 - 285​
Soil Moisture
  1. Liu Y, Dorigo W, Parinussa R, De Jeu R, Wagner W, McCabe MF, Evans JP and Van dijk AIJM (2012) "Trend-preserving blending of passive and active microwave soil moisture retrievals​", Remote Sensing of Environment, vol. 123, pp. 280 - 297
  2. Liu Y, Parinussa R, Dorigo W, de Jeu RAM, Wagner W, Van dijk AIJM, McCabe MF and Evans JP (2011), "Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals​', Hydrology and Earth System Sciences, vol. 15, no. 2, pp. 425 - 436
  3. Liu Y, Evans JP, McCabe MF, de Jeu RAM, Van dijk AIJM and Su H (2010), "Influence of cracking clays on satellite estimated and model simulated soil moisture", Hydrology and Earth System Sciences, vol. 14, no. 6, pp. 979 - 990
  4. McCabe MF, Wood EF and Gao H (2005) "Initial soil moisture retrievals from AMSR-E: Multiscale comparison using in situ data and rainfall patterns overs Iowa', Geophysical Research Letters, vol. 32, no. 6, pp. 1 - 4
  5. McCabe MF, Gao H and Wood EF (2005), "Evaluation of AMSR-E-derived soil moisture retrievals using ground-based and PSR airborne data during SMEX02", Journal of Hydrometeorology, vol. 6, no. 6, pp. 864 - 877​
  1. Liu Y, Van dijk AIJM, McCabe MF, Evans JP and De Jeu R (2013) "Global vegetation biomass change (1988?2008) and attribution to environmental and human drivers", Global Ecology and Biogeography​
  2. Liu YY, Evans JP, McCabe MP, de Jeu RAM, van Dijk AIJM, Dolman AJ and Saizen I (2013) “Changing climate and overgrazing are decimating Mongolian Steppes”, PloS One 8(2), e57599 
  3. Liu YY, de Jeu RAM, McCabe MF, Evans JP and van Dijk AIJM (2011) "Global long-term pas­sive microwave satellite-based retrievals of veg­e­ta­tion opti­cal depth", Geo­phys­i­cal Research Let­ters, 38, L18402
Terrestrial Systems
  1. ​Bormann KJ, McCabe MF and Evans JP (2012) "Satellite based observations for seasonal snow cover detection and characterisation in Australia", Remote Sensing of Environment 123, 57-71
  2. McCabe MF, Chylek P and Dubey MK (2011) "Detecting ice-sheet melt area over western Greenland using MODIS and AMSR-E data for the summer periods of 2002–2006Remote Sensing Letters 2 (2), 117-126
  3. McCabe MF, Balick LK, Theiler J, Gillespie AR and Mushkin A (2008) "Linear mixing in thermal infrared temperature retrieval", International Journal of Remote Sensing 29 (17-18), 5047-5061
  4. Chylek P, McCabe MF, Dubey MK and Dozier J (2007) "Remote sensing of Greenland ice sheet using multispectral near-infrared and visible radiances", J. Geophys. Res 112, D24S20