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. The collection includes multiple independent validation datasets from field campaigns, different in-situ networks, and SMAP L2 retrievals for further evaluations.
Daily 100 m near-surface soil moisture prediction from in-situ data upscaled to Landsat footprint in the Yanco agricultural region during 2016-2021