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DeepSoil: a science-guided framework for generating high precision soil moisture maps by reconciling measurement profiles across in-situ and remote sensing

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dc.contributor.authorKhandelwal, Paahuni, author
dc.contributor.authorPallickara, Sangmi Lee, author
dc.contributor.authorPallickara, Shrideep, author
dc.contributor.authorACM, publisher
dc.date.accessioned2024-12-17T19:12:10Z
dc.date.available2024-12-17T19:12:10Z
dc.date.issued2024-10-29
dc.description.abstractSoil moisture plays a critical role in several domains and can be used to inform decision-making in agricultural settings, drought forecasting, forest fire predictions, and water conservation. Soil moisture is measured using in-situ and remote-sensing equipment. Depending on the type of equipment that is used, some challenges must be reconciled, including the density of observations, the measurement precision, and the resolutions at which these measurements are available. In particular, in-situ measurements are high-precision but sparse, while remote sensing measurements benefit from spatial coverage, albeit at lower precision and coarser resolutions. The crux of this study is to produce higher-precision soil moisture estimates at high resolutions (30m). Our methodology combines scientific models, deep networks, topographical characteristics, and information about ambient conditions alongside both in-situ and remote sensing data to accomplish this. Domain science infuses several aspects of our methodology. Our empirical benchmarks profile several aspects and demonstrate that our methodology accounts for spatial variability while accounting for both static (soil properties and elevation) and dynamically varying phenomena to generate accurate, high-precision 30m resolution soil moisture content maps.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationPaahuni Khandelwal, Sangmi Lee Pallickara, and Shrideep Pallickara. 2024. DeepSoil: A Science-guided Framework for Generating High Precision Soil Moisture Maps by Reconciling Measurement Profiles Across In-situ and Remote Sensing Data. In The 32nd ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’24), October 29-November 1, 2024, Atlanta, GA, USA. ACM, New York, NY, USA, 14 pages. https://doi.org/10.1145/3678717.3691261
dc.identifier.doihttps://doi.org/10.1145/3678717.3691261
dc.identifier.urihttps://hdl.handle.net/10217/239727
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofPublications
dc.relation.ispartofACM DL Digital Library
dc.rights©Paahuni Khandelwal, et al. ACM 2024. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in SIGSPATIAL '24, https://dx.doi.org/10.1145/3678717.3691261.
dc.subjectscience-guided learning
dc.subjectKGML
dc.subjectbig data
dc.subjectspatiotemporal phenomena
dc.subjectsoil moisture
dc.subjectdeep neural networks
dc.titleDeepSoil: a science-guided framework for generating high precision soil moisture maps by reconciling measurement profiles across in-situ and remote sensing
dc.typeText

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