Pardhasaradhi Teluguntla (with Dr Dongryeol Ryu and Dr Biju George): Remote Sensing of Evapotranspiration and Crop Water Productivity in the Krishna River Basin, India [AusAid]

Mahdi Alahmoradi (with Dr Dongryeol Ryu and Dr Ed Kim): Soil moisture retrieval from multi-sensor observations [.]

Ye Nan (with Dr Dongryeol Ryu and Prof Robert Gurney): Soil moisture retrieval from mixed pixels [MFRS]

Sandy Pieschl (with Dr Dongryeol Ryu and Dr Yann Kerr): Soil moisture retrieval from multi-angle observations [MRS/MFRS]

Vjeko Matic (with A/Prof Andrew Western and Dr Justin Costelloe): Quantifying near-surface diffuse discharge from the southwest Great Artesian Basin [ARC-LP]

Soo See Chai (with Dr Bert Veenendall and Prof Graceila Metternicht): A high performance artificial neural network model for surface soil moisture retrieval incorporating high spatial resolution passive microwave and multi-sensor remote sensing data [.]

Clara Draper (with Dr Peter Steinle): Soil moisture assimilation for enhanced weather prediction [MRS/CRC eWater]

Rocco Pancierra (with Dr Ed Kim): High resolution soil moisture mapping [APA]

Robert Pipunic (with Dr Andrew Western): Assimilation of evapotranspiration data [MRS]

Kevin Ellett (with Dr Andrew Western): Gravity changes, soil moisture and data assimilation [MRS/MFRS/Fullbright]
Adam Smith (with Dr Andrew Western): Gravity changes, soil moisture and data assimilation [ARC-DP]

Cressida Savige (with Dr Andrew Western and Dr Mohammad Abuzar): Remote sensing of evapotranspiration for mapping irrigation water use efficiency [APA-LP]

2007 Manju Hemakumara (with Prof Jetse Kalma and Prof Garry Willgoose): Aggregation and disaggregation of soil moisture measurements [ARC-DP]
2006 Christoph Ruediger (with Prof Jetse Kalma and Prof Garry Willgoose): Streamflow data assimilation for soil moisture prediction [ARC-DP] (pdf 24.1Mb)
2006 Mathew Turner (with Prof Rodger Grayson): Integration of satellite data into water quality models [MRS] (pdf 13.6Mb)
2006 Stephen Wealands (with Prof Rodger Grayson): Spatial understanding of hydrologic processes through remote sensing technologies [APA] (pdf 7.1Mb)
2005 Leo Lymburner (with Dr Peter Hairsine and Dr Alex Held): Mapping riparian vegetation functions using remote sensing and terrain analysis [CRCCH] (pdf 5.8Mb)

Active Passive Microwave Soil Moisture Remote Sensing: Towards Sustainable Land and Water Management From Space

Soil moisture is a highly critical resource for the Australian agricultural economy which is stressed by climate change. Daily monitoring of paddock scale soil moisture from space represents a powerful tool to inform land management, allowing accurate crop yield and pasture growth predictions. At the continental scale, soil moisture information will result in better weather, climate and extreme flood prediction skill and the ability to assess the effects of future climate change on Australia. It is therefore imperative that active passive soil moisture retrieval algorithms be developed specifically for the Australian environment in order to take full advantage of the SMAP remote sensing mission when it is launched in 2012.

A New Paradigm for Improved Water Resource Management Using Innovative Water Modelling Techniques

The threat of climate change and Australia's arid environment makes accurate water resource planning essential for sustainable water management. This is particularly relevant in rural Australian catchments with competing needs for scarce water resources, including irrigation to sustain farming communities, maintaining adequate flows for river health, and seasonal flooding for fragile eco systems. Accurately predicting key water balance components across catchments is crucial for improved water resource planning. Continuously constraining model predictions with time series of spatial data can identify weaknesses in model physics for correction and make model scenario testing more reliable so better water management decisions can be made.

Airborne Hyperspectral Scanning for Advanced Monitoring and Assessment of Vegetation and Water Properties

The proposed infrastructure will give Australian researchers the most advanced capabilities available world wide in airborne remote sensing of the environment. By combining hyper spectral scanning, with full wave form resolving Light Detection and Ranging (LIDAR), microwave scanning and sythetic aperture RADAR, flown simultaneously on the most cost efficient and technologically advanced research aircraft, it will be possible to assess and monitor a wide range of parameters not accessible to airborne methods before.

Towards Improved Soil Moisture Retrieval by LiDAR Measurement of Microwave Properties of Vegetation and Development of a Data Assimilation Framework

We aim to improve the accuracy of soil moisture retrieval from satellite-based passive microwave systems. First, data collected from the recent Australian National Airborne Field Experiments (NAFE) by the University of Melbourne will be used to evaluate and test the data assimilation framework developed at the University of Reading (for snow mass modelling) in the context of soil moisture retrieval. Second, techniques used at the University of Reading for deriving vegetation structural information from airborne scanning LiDAR will be developed for application to a wider range of vegetation types and larger spatial scales, and applied to the NAFE data sets to explore the effects of improved vegetation characteristics on the retrieval of soil moisture from passive microwave observations.

MoistureMap: A Soil Moisture Monitoring, Prediction and Reporting System for Sustainable Land and Water Management

Knowledge of the spatial and temporal variation of surface and root zone soil moisture content is critical to environmental sustainability and risk adverse farm management. A paddock scale soil moisture prediction tool will allow i) grain growers to make informed decisions of what to plant and when, based on likely germination rates and crop yield, ii) graziers to be proactive regarding management of stocking rates based on likely pasture growth, and iii) better weather and climate prediction skill. At regional scales moisture information can be used to support claims of drought exceptional circumstances.

High Resolution Airborne Radar for Environmental Research: Soil Moisture, Vegetation, Salinity and Terrain Mapping

There is a rapidly increasing demand for a range of environmental data. For example, information on soil moisture status is required for efficient and sustainable water use. Moreover, irrigation practices and large scale clearing have led to serious land degradation through increased salinity from rising water tables. Combined soil moisture and salinity measurement will provide important insight to this complex issue. Further, understanding the complex and rich biodiversity of Australian flora and its adaptation to droughts and fire is essential to ensuring Australian ecosystem longevity. Knowledge of flora changes through time as a function of soil moisture content and salinity is key to gaining this understanding.

Potential Applications for Airborne Remote Sensed Data This project investigates the potential for high-resolution airborne sensing technologies for monitoring human impacts on rivers at the basin-scale. The resolution and coverage provided by airborne data can potentially transform our understanding of landscape-scale impacts and underlying causal processes. Improved monitoring and understanding of these impacts will inform more effective environmental policy and management for rivers at regional, state and national levels.

The Murrumbidgee Monitoring Network

This project will make the Murrumbidgee Monitoring Network data available to the ACCESS modelling community.

Improving the Airborne Mapping of Soil Moisture

Deriving soil moisture from passive microwave observations requires knowledge of the near surface soil temperature. The aim of our project is to develop a model by which the near surface soil temperature can be estimated from airborne data. These estimates can then be used for the retrieval of soil moisture information.

Quantifying Near-surface Diffuse Discharge from the Southwest Great Artesian Basin

The Great Artesian Basin (GAB) is the largest groundwater resource in Australia and supports a number of mining and pastoral operations. Sustainable use of this resource requires an improved understanding of its water balance, particularly the large component of vertical leakage. This project proposes to use isotopic and geochemical techniques, in addition to physical monitoring of evaporation losses, to constrain the near-surface component of vertical leakage from the GAB in South Australia. Sampling will be optimally targeted and scaled up using detailed mapping of the near-surface leakage zones from remote sensing. This study will result in improved understanding and management of this critical resource.

Advanced Algorithms to Retrieve Remotely Sensed Soil Moisture and Enhance Hydrological Prediction

The spatial information on soil moisture provided by remotely sensed data has considerable potential to improve hydrological prediction. Despite this, it has had very little application in hydrological modelling. This is because remote sensed data is an indirect measure of soil moisture, confounded by several factors which introduce considerable uncertainties. These uncertainties have proved difficult to reliably characterise and this has limited application. This project will utilise unique datasets acquired in recent large scale field experiments to develop new algorithms to more accurately retrieve remotely sensed soil moisture and its associated uncertainty. These algorithms will be based on statistical Bayesian hierarchal spatio-temporal models which have yet to be applied to retrieving soil moisture from remotely sensed data. The potential of new algorithms to improve the predictive power of hydrological models will be assessed. This pilot project will develop prototype algorithms on a small scale to demonstrate proof-of-concept with the long-term goal of seeking external research funding to develop a large scale application.

Integrate NASA’s Global Soil Moisture Remote Sensing and Modeling Data into USDA’s Global Crop Production Decision Support System

This project aims at integrating NASA’s global soil moisture remote sensing and modeling data products into the U. S. Department of Agriculture’s Global Crop Production Decision Support System to improve their reliability and accuracy for forecasting crop yields. USDA’s crop yield forecasts affect decisions made by farmers, businesses, and governments by defining the fundamental conditions in commodity markets.

High Resolution Mapping of Surface and Root Zone Soil Moisture

Knowledge of the spatial and temporal variation of surface and root zone soil moisture content at high spatial resolution is critical to achieving more efficient water utilisation practices in agriculture. Australia 's main river basins are under mounting pressure to satisfy a wide range of competing economic, social and environmental needs for water, particularly in terms of environmental flows and efficient irrigation. A better understanding of the soil moisture distribution at sub-farm scales will allow farmers to better utilise both the moisture in their soil and their limited allocation for irrigation. This will help alleviate soil moisture related problems in some of the nation's key catchments, such as the Murray Darling Basin.

Airborne Laser Scanning for Advanced Environmental Monitoring

This proposal seeks to enhance the national capability for airborne remote sensing of key environmental variables through the acquisition of an airborne laser scanner and inertial navigation system. Many environmental science studies, such as hydrology, soil moisture scaling and salinity, can be significantly enhanced by airborne laser scanning, through the creation of high precision, high resolution digital terrain models. Airborne laser scanning can also measure three dimensional vegetation canopy structure, a useful indicator of biomass, carbon storage and vegetation health. This infrastructure will provide Australian researchers with a unique arsenal of remote sensing tools for advanced yet affordable environmental research Studies.

Assimilation of Latent and Sensible Heat Flux Data into the CSIRO Biosphere Model

This project will investigate the use of latent and sensible heat flux data to improve the predictive performance of a land surface model. The significance of land surface models is their ability to provide continuous prediction of latent and sensible heat flux feedback to atmospheric models for weather and climate forecasting. Historically, soil moisture and/or air temperature and humidity data have been used to correct land surface model predictions, despite their often weak and indirect relationship to latent and sensible heat flux. This research aims to use direct information on latent and sensible heat flux to achieve optimal flux predictions from land surface models.

Gravity Changes, Soil Moisture and Data Assimilation

This project will assess the utility of space and ground-based gravity measurements for monitoring changes in the hydrological cycle at regional scales. At present there are no methods available for monitoring changes in terrestrial water storage over the globe, despite its importance for assessing the effects of large-scale changes in land use and climate change. The launch of NASA's Gravity Recovery and Climate Experiment (GRACE) satellites earlier this year provides a 5-year window of opportunity to undertake ground-based research to test this innovative technique for monitoring terrestrial water storage from gravity measurements - something that has been shown to be possible theoretically, but has not been testable until now.

On the Use of Remote Sensing Data in Bio-geo-chemical Marine Models

This project will investigate the use of satellite and other sources of water quality data for evaluating coastal marine model structure and improving predictive performance. The capability to model highly complex bio-geo-chemical marine systems is becoming increasingly possible due to advances in computing power, remote sensing data availability and interpretation algorithms. However, the techniques for using this data to evaluate and/or correct model trajectories are immature. This project will enhance the current capacity for using satellite derived data sources in marine bio-geo-chemical models with the purpose of generating greater credibility and reliability of model predictions. It will combine the strengths of CSIRO marine modelling capability with the UofM’s expertise in data assimilation and spatial analysis.

A New Airborne Facility for Environmental, Hydrological, Atmospheric and Oceanic Research: High Resolution Measurement of Soil Moisture, Temperature and Salinity

This proposal seeks to establish a new national capability for airborne remote sensing of key environmental variables. It will enable high-resolution mapping of near-surface soil moisture, land surface salinity and temperature, and ocean surface salinity and temperature. It will be a new tool for hydrologic, atmospheric and oceanic researchers, providing unprecedented detail on characteristics critical to our understanding and management of the environment. The small instrument size and weight will enable use of a light aircraft as the observing platform, providing the national (and international) research community with an affordable tool, hitherto unavailable.

Scaling and Assimilation of Soil Moisture and Streamflow

Information on how soil moisture varies in space and time has been largely restricted to point-scale groundbased measurements. We will develop methods for predicting how soil moisture status evolves in time over a range of spatial scales, by assimilating groundbased measurements and satellite observations of soil moisture together with streamflow observations into simple rainfall-runoff models. Extensive soil moisture monitoring will allow development of scaling relationships and validation for new satellite-based microwave radiometers.

Use of Remote Sensing Actual Evapotranspiration Estimates and Water Delivery Data for Assessing Water Use Efficiency in an Irrigated Landscape

Improving management of water in irrigated agriculture will lead to increased sustainability, decreased environmental impacts and increased profitability. Quatifying water use efficiency for an irriguation district is a vital part of improving water management. This project aims to develop new methods for quatifying water use efficiency by combining actual evapotranspiration estimates with water delivery information at the farm scale for an entire irrigation district. Remote sensing techniques will be utilized to map actual evapotranspiration and water meter data will be combined with cadastral and channel network data to map water delivery.

Assimilation of Satellite Observations of the Land Surface in Support of NASA's Seasonal-to-Interannual Prediction Project

This project will address the importance of assimilation of satellite observations, with the focus on soil moisture retrievals, in the initialisation of the land surface to enhance seasonal-to-interannual climate forecasts. Key aspects of this research are i) the extension of the current one-dimensional approach to take into account three-dimensionsl structures in forecast error due to forcing error and large scale correlations in the model properties; and through the development of strategies to account for model biases, and ii) the implementation of an operational land assimilation system based on these combined activities.

A Catchment-Based Global River Routing Scheme for Climate Models and Assimilation of Streamflow and Altimetry Data

There are two broad goals of this research. The first is to develop and test a new, catchment-based, global river transport scheme for use in global climate models and coupled climate system models that overcomes the shortcommings of cell-based models. The second goal is to develop and test methodologies for assimilation of stream gauge and satellite altimetric measurements into the new scheme.

Global Validation of EOS-AQUA Land Surface Dynamics Using Data Assimilation

This project seeks to determine the nature and variability in selected global soil moisture and snow products measured by the AMSR-E sensor on a variety of time scales, and to analyise the effects of these uncertainties on the predictability of the global surface water and energy balance using land surface data assimilation techniques in near real-time.

Optimal Land Initialisation for Seasonal Climate Prediction

This project seeks to provide optimal initialisation of the NSIPP (NASA Seasonal to Interannual Prediction Project) land surface model, through assimilation of near-surface soil moisture observations into the land surface model.

Validation of the AMSR-E Satellite Soil Moisture Product

One of the products to be provided by the AMSR-E remote sensing instrument is 25km resolution near-surface (top 1cm) soil moisture content at a specified accuracy of better than 6%v/v for areas having vegetation cover of less than 1.5kg/m2. Due to a lack of historical data, the algorithms to derive this soil moisture product have not been widely tested. We propose to test the validity of these algorithms for soil moisture measurement under Australian conditions.

Optimal Land Initialisation for Seasonal Climate Prediction: Brightness Temperature Assimilation

Accurate initialisation of the land surface in global climate models is critical for seasonal-to-interannual climatological prediction, because of its regulation on fluxes from the land surface to the atmosphere. Previous work has demonstrated the ability to obtain soil moisture initial conditions throughout the top metre or so of the Earth’s surface by assimilation of satellite measurements of near-surface soil moisture. This project seeks to extend that work, by assimilating the satellite brightness temperature measurements (ie. measurements of the radiation received at the sensor), rather than a derived soil moisture product, allowing the assimilation to impact both the soil moisture and temperature states, and eliminating restrictive assumptions made in deriving the soil moisture independent of the assimilation.

Optimal Land Initialisation for Seasonal Climate Prediction: Soil Moisture Assimilation

Accurate initialisation of the land surface in global climate models is critical for seasonal-to-interannual climatological prediction, because of its regulation on fluxes from the land surface to the atmosphere. Due to its long term persistence, it is most important that land soil moisture content be optimaly initialised. This project seeks to provide optimal land surface initialisation for Australia, in collaboration with the NASA Seasonal-to-Interannual Prediction Project (NSIPP) and the efforts at NASA Goddard Space Flight Centre to provide such initial conditions globally, through the assimilation of satellite measurements of near-surface soil moisture.

Assimilation of Remotely Sensed Snow Observations into the NSIPP Land Surface Model

This project seeks to provide optimal initialisation of the NSIPP (NASA Seasonal to Interannual Prediction Project) land surface model, through assimilation of snow melt signature, snow water equivalent, snow depth and snow cover observations into the land surface model.

A Global Land Data Assimilation System (GLDAS)

This project seeks to develop a high resolution, near-real-time global land data assimilation system using relevant remotely sensed and in-situ observations within a land data assimilation framework. This project is an extension to the North American LDAS project.

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