43338759-9133-5212-9850-281f81c8f85f urn:uuid en UTF-8 nonGeographicDataset nonGeographicDataset 2025-09-20T16:45:25 creation ISO 19115 2016 Daily 100 m near-surface soil moisture prediction from in-situ data upscaled to Landsat footprint in the Yanco agricultural region during 2016-2021 43338759-9133-5212-9850-281f81c8f85f This collection is structured to support reproducible research for "Spatial soil moisture prediction from in-situ data upscaled to Landsat footprint: Assessing area of applicability of machine learning models" (Yu et al., 2025). It provides all necessary input data, trained models, and soil moisture (SM) data extrapolated from 28 OzNet in-situ sites across a primary study area (100 km × 100 km) and an extended area (300 km × 300 km) in southeastern Australia (i.e., the Yanco agricultural region) during 2016-2021. The study period spans a cross-validation period (2016-2019) and an independent test period (2020-2021). The spatial resolution of SM prediction is 100 m and the temporal frequency is daily. A key focus is the characterisation of Area of Applicability (AOA) for Random Forests (RF) and eXtreme Gradient Boosting (XGB) models, delineating where predictions are statistically reliable. 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