There is a strong need for a reliable operational system for soil moisture, i.e., how much water stored in or at the land surface and available for evaporation in Australia or another country. Such a system is expected to provide, for any area, big or small, at Australia, a long period of daily average soil moisture plus uncertainty information. The data collected by remote sensors, such as satellites, can complement the limited representativeness of in situ soil moisture measurement data in Australia. They provide hydrological soil properties which are not properly described by soil maps. They also provide uncertainty information about soil moisture, which is important for robust decision making and risk controlling. Soil moisture also drives evaporation and influences substantially to crop yield, weather prediction, both at short and medium range, climate change studies and so on. A reliable soil moisture system will enable hydrologists, ecologists, agriculturalists, and climatologist to model and predict these hydrological and environmental systems more accurately and robust, especially at large scale. There are several quality soil moisture observations from satellites, but none of them satisfies all user requirements. None of them provides uncertainty information of soil moisture. These remote sensing techniques have inherent advantages and disadvantage. For example, the passive microwave sensor AMSR-E is expected to have higher accuracy over regions with low vegetation density. ASCAT performs better over moderately vegetated regions. AMSR-E can provide absolute soil moisture estimate while ASCAT only relative values. The Advanced Synthetic Aperture Radar (ASAR) from RADARSAT-1 has pretty high spatial resolution (5km) but covers the whole Australia only every 3 or more days.
The project aims to assimilate data from various remote sensors to build up a robust and continuous-time soil moisture product. It will focus on one or two challenges along the direction, such as
- handling different spatial resolutions from different sensors/satellites;
- processing data with different temporal resolution;
- better using spatial correlation;
- taking advantage of serially correlated data;
- providing uncertainty estimation;
- computation efficiency for large data sets;
- computation efficiency for complicated models;
- Applicants are expected to have a major in computer science, information technology, computer engineering with excellent programming skills, especially C/C++. A good understanding of algorithms, parallel computation, data mining and/or machine learning is a plus.
- Or strong background in applied mathematics/statistics, preferably with strong background in algorithms and statistics. Good programming knowledge like MatLab and/or R is a plus.
- Scipal, K., T. Holmes, R. de Jeu, V. Naeimi, and W. Wagner (2008), A possible solution for the problem of estimating the error structure of global soil moisture data sets, Geophysical Research Letters, 35(24), -, Artn L24403 Doi 10.1029/2008gl035599
- Cressie, N., T. Shi, and E. L. Kang (2010), Fixed Rank Filtering for Spatio-Temporal Data, Journal of Computational and Graphical Statistics, 19(3), 724-745, DOI 10.1198/jcgs.2010.09051
- Liu, Y. Y., R. M. Parinussa, W. A. Dorigo, R. A. M. de Jeu, W. Wagner, A. I. J. M. van Dijk, M. F. McCabe, and J. P. Evans (2010), Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals, Hydrology and Earth System Sciences Discussions, 7(5), 6699-6724, doi:10.5194/hessd-7-6699-2010
A student working in this project can expect
- to learn state-of-art of data assimiliation/mining techniques;
- to access state-of-art of remote sensing technologies;
- to be involved in developing cutting-edge soil moisture products, especially an operational system for Australia;
- to gain experiences on solving real-world challenges while working with a research group delivering great science and innovative solutions for Australian society and economy.