ORCID Profile
0000-0002-9047-2970
Current Organisation
Monash University
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Water Resources Engineering | Photogrammetry and Remote Sensing | Surfacewater Hydrology | Geomatic Engineering |
Effects of Climate Change and Variability on Australia (excl. Social Impacts) | Weather | Farmland, Arable Cropland and Permanent Cropland Water Management
Publisher: American Meteorological Society
Date: 28-09-2017
Abstract: The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
Publisher: Elsevier BV
Date: 12-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
Publisher: IEEE
Date: 07-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2015
Publisher: Elsevier BV
Date: 03-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2014
Publisher: IEEE
Date: 07-2017
Publisher: IEEE
Date: 07-2016
Publisher: Elsevier BV
Date: 06-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: IEEE
Date: 07-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: MDPI AG
Date: 22-08-2020
DOI: 10.3390/MIN10090745
Abstract: The Australian landscape is affected by abandoned mines that pose environmental, public health and safety risks. To promote the beneficial reuse, rehabilitation and/or remediation of these sites and understand their spatial arrangement, we compiled, classified and analysed a country-wide geospatial database of all known inactive hard rock mine sites. Following extensive review and classification of disparate records of such sites that have been terminated, neglected or classified as heritage, plus those under care and maintenance in Australia, we assessed state-by-state reporting and cross-border rehabilitation requirements. This was enabled by the development of the Mining Incidence Documentation & Assessment Scheme (MIDAS) that can be used to catalogue and compare active or inactive mine data regardless of reporting conventions. At a national level, and with four case studies, we performed GIS-based spatial analyses and environmental risk assessments to demonstrate potential uses of our database. Analyses considered the proximity of sites to factors such as infrastructure and sensitive environmental receptors. As Australia struggles to manage the ongoing technical, socioeconomic and environmental challenges of effective mine rehabilitation, the insights enabled by this national-level spatial database may be key to developing coordinated responses that extend beyond state boundaries. Our classification and methodology are easily transferable, thereby encouraging more formalized, systematic and widespread documentation of abandoned mines worldwide.
Publisher: IEEE
Date: 07-2018
Publisher: MDPI AG
Date: 25-06-2023
DOI: 10.3390/AGRICULTURE13071297
Abstract: New sensing technologies are at the cusp of providing state-of-the-art infrastructure to precisely monitor crop water requirements spatially so as to optimize irrigation scheduling and agricultural productivity. This project aimed to develop a new smart irrigation system that uses an L-band radiometer in conjunction with an irrigation boom, allowing for a precision water delivery system using derived high-resolution soil moisture information. A potato farm was selected due to its sensitivity to water and an existing irrigation system where the radiometer could be mounted. A field experiment was conducted to capture the soil moisture variation across the farm using the radiometer. A greenhouse trial was also conducted to mimic the actual growth of potatoes by controlling the soil moisture and exploring the impact on their growth. It was found that 0.3 cm3/cm3 was the optimal moisture level in terms of productivity. Moreover, it was demonstrated that on-farm soil moisture maps could be generated with an RMSE of 0.044 cm3/cm3. It is anticipated that through such technology, a real-time watering map will be generated, which will then be passed to the irrigation software to adjust the rate of each nozzle to meet the requirements without under- or over-watering.
Start Date: 2018
End Date: 2019
Funder: Monash University
View Funded ActivityStart Date: 2014
End Date: 2016
Funder: Australian Research Council
View Funded ActivityStart Date: 2014
End Date: 06-2019
Amount: $547,515.00
Funder: Australian Research Council
View Funded Activity