<div class="csl-bib-body">
<div class="csl-entry">Shan, X., Steele-Dunne, S., Hahn, S., Wagner, W., Bonan, B., Albergel, C., Calvet, J.-C., & Ku, O. (2024). Assimilating ASCAT normalized backscatter and slope into the land surface model ISBA-A-gs using a Deep Neural Network as the observation operator: Case studies at ISMN stations in western Europe. <i>Remote Sensing of Environment</i>, <i>308</i>, Article 114167. https://doi.org/10.1016/j.rse.2024.114167</div>
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dc.identifier.issn
0034-4257
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/197508
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dc.description.abstract
ASCAT normalized backscatter (σo₄₀) and slope (σ′) contain valuable information about soil moisture and vegetation. While σo₄₀ has been assimilated to constrain soil moisture, sometimes together with Leaf Area Index (LAI), this study is the first to assimilate σ′ directly to constrain vegetation states. Here, we assimilate σo₄₀ and slope σ′ into the ISBA-A-gs LSM using the Simplified Extended Kalman Filter (SEKF) using a Deep Neural Network (DNN) as the observation operator. The performances of the data assimilation (DA) and open loop (OL) are evaluated against in-situ soil moisture observations from the International Soil Moisture Network (ISMN), and LAI observations from the Copernicus Global Land Service (CGLS). Given an accurate and physically plausible observation operator, along with well-defined model and observation errors, the data assimilation system should yield improved estimates of the model states. However, results show that the DA performance is neutral compared to the OL in terms of the median unbiased root mean square error (ubRMSE) and Pearson correlation coefficient (ρ) across all validation sites. In addition, an analysis of the residuals and innovations confirms that DA had limited or no impact. This poor performance is perplexing. Furthermore, given the growing interest in the use of machine-learning-based observation operators, it is essential to understand the role that the use of the DNN may be playing in this poor performance. While representativeness errors and error specification play some part, it is demonstrated that the key factor constraining the efficacy of the SEKF is the correct estimation of the Jacobians that control the degree to which the observations update the states in the SEKF. It is argued that the DNN relating model states to satellite observations must have physically-plausible and robust Jacobians for the DNN to be effective in a data assimilation framework.
en
dc.language.iso
en
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dc.publisher
ELSEVIER SCIENCE INC
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dc.relation.ispartof
Remote Sensing of Environment
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
ASCAT
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dc.subject
Radar
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dc.subject
Vegetation
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dc.subject
Data assimilation
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dc.subject
Land surface model
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dc.subject
Machine learning
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dc.subject
Soil moisture
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dc.title
Assimilating ASCAT normalized backscatter and slope into the land surface model ISBA-A-gs using a Deep Neural Network as the observation operator: Case studies at ISMN stations in western Europe