Linking Hydro-Geophysics and Remote Sensing Technology for Sustainable Water and Agricultural Catchment Management
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CitationO'Leary, Dave, Fenton, Owen, Mellender, P, Touhy, P, Brown, Colin & Daly, Eve. (2019). Linking Hydro-Geophysics and Remote Sensing Technology for Sustainable Water and Agricultural Catchment Management. Ryan Institute Research Day 2019, NUI Galway.: https://dx.doi.org/10.13140/RG.2.2.27911.14245.
AbstractThe acquisition of sub-surface data for agricultural purposes is traditionally achieved by in situ point sampling in the top 2m over limited target areas (farm scale ~ km2) and time periods. This approach is inadequate for integrated regional (water catchment ~ 100 km2) scale management strategies which require an understanding of processes varying over decadal time scales in the transition zone (~ 10’s m) from surface to bedrock. With global food demand expected to increase by 100% by 2050, there are worldwide concerns that achievement of production targets will be at the expense of water quality. In order to overcome the limitations of the traditional approach, this research programme will combine airborne and ground geophysics with remote sensing technologies to access hydrogeological and soil structure information on Irish Soils at multiple spatial scales. It will address this problem in the context of providing tools for the sustainable management of agricultural intensification envisioned in Food Harvest 2020 and Food Wise 2025 and considering the EU Habitats and Water Framework Directives (WFD), Clean Air Policy and Soil Thematic Strategies. The work will use existing ground based geophysical and hydrogeological data from Teagasc Agricultural Catchment Programme (ACP) and Heavy Soil sites co-located ground and airborne electromagnetic data. Neural Networks training and Machine learning approaches will supplement traditional geophysical workflows. Work will then focus on upscaling results from ACP to WFD catchment scale. This upscaling will require modification of traditional satellite remote sensing conceptual frameworks to analyse heterogeneous, multi-temporal data streams.
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