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A Bayesian machine learning method to explain the error characteristics of global-scale soil moisture products

hal.structure.identifierUSDA-ARS : Agricultural Research Service
hal.structure.identifierGwangju Institute of Science and Technology [GIST]
dc.contributor.authorKIM, Hyunglok
hal.structure.identifierUSDA-ARS : Agricultural Research Service
dc.contributor.authorCROW, Wade
hal.structure.identifierVienna University of Technology = Technische Universität Wien [TU Wien]
dc.contributor.authorWAGNER, Wolfgang
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorLI, Xiaojun
hal.structure.identifierUSDA-ARS : Agricultural Research Service
dc.contributor.authorLAKSHMI, Venkataraman
dc.date.accessioned2024-04-08T11:37:39Z
dc.date.available2024-04-08T11:37:39Z
dc.date.issued2023-10
dc.identifier.issn0034-4257
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195008
dc.description.abstractEnEstimating accurate surface soil moisture (SM) dynamics from space, and knowing the error characteristics of these estimates, is of great importance for the application of satellite-based SM data throughout many Earth Science/Environmental Engineering disciplines. Here, we introduce the Bayesian inference approach to analyze the error characteristics of widely used passive and active microwave satellite-derived SM data sets, at different overpass times, acquired from the Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT) missions. In particular, we apply Bayesian hierarchical modeling (BHM) and triple collocation analysis (TCA) to investigate the relative importance of different environmental factors and human activities on the accuracy of satellite-based data.To start, we compare the BHM-based sensitivity analysis method to the classic multiple regression models using a frequentist approach, which includes complete pooling and no-pooling models that have been widely used for sensitivity analysis in the field of remote sensing and demonstrate the BHM's adaptability and great potential for providing insight into sensitivity analysis that can be used by various remote sensing research communities.Next, we conduct an uncertainty analysis on BHM's model parameters using a full range of uncertainties to assess the association of various environmental factors with the accuracy of satellite-derived SM data. We focus on investigating human-induced error sources such as disturbed surface soil layers caused by irrigation activities on microwave satellite systems, naturally introduced error sources such as vegetation and soil organic matter, and errors related to the disregard of SM retrieval algorithmic assumptions -such as the thermal equilibrium passive microwave systems. Based on the BHM-based sensitivity analysis, we find that assessments of SM data quality with a single variable should be avoided, since numerous other factors simultaneously influence their quality. As such, this provides a useful framework for applying Bayesian theory to the investigation of the error characteristics of satellite-based SM data and other time-varying geophysical variables.
dc.language.isoen
dc.publisherElsevier
dc.subject.enMicrowave satellite systems
dc.subject.enRemotely sensed soil moisture
dc.subject.enBayesian hierarchical model
dc.subject.enTriple collocation analysis
dc.subject.enUncertainty analysis
dc.title.enA Bayesian machine learning method to explain the error characteristics of global-scale soil moisture products
dc.typeArticle de revue
dc.identifier.doi10.1016/j.rse.2023.113718
dc.subject.halSciences de l'environnement
bordeaux.page113718
bordeaux.volume296
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-04474540
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04474540v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2023-10&rft.volume=296&rft.spage=113718&rft.epage=113718&rft.eissn=0034-4257&rft.issn=0034-4257&rft.au=KIM,%20Hyunglok&CROW,%20Wade&WAGNER,%20Wolfgang&LI,%20Xiaojun&LAKSHMI,%20Venkataraman&rft.genre=article


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