Data

Daily 100 m near-surface soil moisture prediction from in-situ data upscaled to Landsat footprint in the Yanco agricultural region during 2016-2021

Commonwealth Scientific and Industrial Research Organisation
Yu, Yi ; Malone, Brendan ; Renzullo, Luigi ; Burton, Chad ; Tian, Siyuan ; Searle, Ross ; Bishop, Thomas ; Walker, Jeffrey
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/dhxv-nz94&rft.title=Daily 100 m near-surface soil moisture prediction from in-situ data upscaled to Landsat footprint in the Yanco agricultural region during 2016-2021&rft.identifier=https://doi.org/10.25919/dhxv-nz94&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=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.&rft.creator=Yu, Yi &rft.creator=Malone, Brendan &rft.creator=Renzullo, Luigi &rft.creator=Burton, Chad &rft.creator=Tian, Siyuan &rft.creator=Searle, Ross &rft.creator=Bishop, Thomas &rft.creator=Walker, Jeffrey &rft.date=2025&rft.edition=v1&rft.relation=http://doi.org/10.1109/IGARSS53475.2024.10642763&rft.coverage=westlimit=144.5; southlimit=-36.0; eastlimit=147.5; northlimit=-33.0; projection=WGS84&rft_rights=Creative Commons Attribution 4.0 International Licence https://creativecommons.org/licenses/by/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) The Australian National University, CSIRO, The University of Sydney 2025.&rft_subject=Soil moisture&rft_subject=Spatial prediction&rft_subject=Upscaling&rft_subject=Landsat&rft_subject=Machine learning&rft_subject=Area of applicability&rft_subject=Agricultural hydrology&rft_subject=Agriculture, land and farm management&rft_subject=AGRICULTURAL, VETERINARY AND FOOD SCIENCES&rft_subject=Machine learning not elsewhere classified&rft_subject=Machine learning&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft_subject=Spatial statistics&rft_subject=Statistics&rft_subject=MATHEMATICAL SCIENCES&rft.type=dataset&rft.language=English Access the data
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Creative Commons Attribution 4.0 International Licence
https://creativecommons.org/licenses/by/4.0/

Data is accessible online and may be reused in accordance with licence conditions

All Rights (including copyright) The Australian National University, CSIRO, The University of Sydney 2025.

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Brief description

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.

Available: 2025-04-28

Data time period: 2016-01-01 to 2021-12-31

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Subjects
Agricultural, Veterinary and Food Sciences | Agricultural Hydrology | Agriculture, Land and Farm Management | Area of applicability | Information and Computing Sciences | Landsat | Mathematical Sciences | Machine Learning | Machine learning | Machine Learning Not Elsewhere Classified | Soil moisture | Spatial prediction | Spatial Statistics | Statistics | Upscaling |

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