https://nova.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:35106 Wed 21 Jun 2023 11:30:04 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:33560 Wed 04 Sep 2019 12:18:53 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:34065 Wed 04 Sep 2019 12:18:45 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:45917 Tue 08 Nov 2022 09:32:08 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:45520 3/cm³, against in-situ field data, and an average ubRMSEs of 0.07 cm³/cm³ when compared to the National Airborne Field Experiment 2005 (NAFE'05) soil moisture retrievals. Both models showed promising results over semi-arid regions in estimating soil moisture at a high spatial resolution, but with their own strengths and weaknesses. The findings here provide useful insights on the robustness of the soil thermal inertia relationship across scales and the effects of the model resolution to the downscaled soil moisture estimates. The approach demonstrated encouraging results over semi-arid regions in estimating soil moisture at a high spatial resolution.]]> Mon 31 Oct 2022 14:09:58 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:45518 μSM) have shown promising results over arid and semi-arid landscapes. However, the linearity of these algorithms is affected by factors such as vegetation, soil texture and meteorology in a complex manner. This study tested a (i) Regression Tree (RT), an Artificial Neural Network (ANN), and a Gaussian Process Regression (GPR) model based on the soil thermal inertia theory over a semi-arid agricultural landscape in Australia, given the ability of machine learning algorithms to capture complex, non-linear relationships between predictors and responses. Downscaled soil moisture from the RT, ANN and GPR models showed root mean square errors (RMSEs) of 0.03, 0.09 and 0.07 cm³/cm³ compared to airborne retrievals and unbiased RMSEs (ubRMSEs) of 0.07, 0.08 and 0.05 cm³/cm³ compared to in-situ observations, respectively. The study showed encouraging results to integrate machine learning techniques in estimating near-surface soil moisture at a high spatial resolution.]]> Mon 31 Oct 2022 14:02:59 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:35054 Fri 14 Jun 2019 14:37:38 AEST ]]>