首页|New Study Findings from Newcastle University Illuminate Research in Machine Lear ning (Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machin e Learning Techniques: A Review)
New Study Findings from Newcastle University Illuminate Research in Machine Lear ning (Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machin e Learning Techniques: A Review)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on artificial intelligence have been published. According to news reporting out of Callaghan, Australia, by New sRx editors, research stated, "Soil moisture (SM) is a key variable driving hydr ologic, climatic, and ecological processes." Financial supporters for this research include Cooperative Research Centre For H igh Performance Soils. The news editors obtained a quote from the research from Newcastle University: " Although it is highly variable, both spatially and temporally, there is limited data availability to inform about SM conditions at adequate spatial and temporal scales over large regions. Satellite SM retrievals, especially L-band microwave remote sensing, has emerged as a feasible solution to offer spatially continuou s global-scale SM information. However, the coarse spatial resolution of these L -band microwave SM retrievals poses uncertainties in many regional- and local-sc ale SM applications which require a high amount of spatial details. Numerous stu dies have been conducted to develop downscaling algorithms to enhance the spatia l resolution of coarse-resolution satellite-derived SM datasets. Machine Learnin g (ML)-based downscaling models have gained prominence recently due to their abi lity to capture non-linear, complex relationships between SM and its driving fac tors, such as vegetation, surface temperature, topography, and climatic conditio ns. This review paper presents a comprehensive review of the ML-based approaches used in SM downscaling."
Newcastle UniversityCallaghanAustral iaAustralia and New ZealandAlgorithmsCyborgsEmerging TechnologiesMachi ne Learning
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