Towards constraining soil and vegetation dynamics in land surface models: Modeling ASCAT backscatter incidence-angle dependence with a Deep Neural Network

• Main Authors: Albergel, C. (Author), Bonan, B. (Author), Calvet, J.-C (Author), Georgievska, S. (Author), Hahn, S. (Author), Huber, M. (Author), Ku, O. (Author), Shan, X. (Author), Steele-Dunne, S. (Author), Wagner, W. (Author)
• Format: Article
• Language: English
• Published: Elsevier Inc. 2022
• Subjects: Advanced scatterometers; ASCAT; Backscattering; Deep Neural Network; Deep neural networks; Dynamics; Land surface; Land surface model; Land surface models; Machine learning; Machine-learning; Meteorological instruments; Normalised sensitivity coefficients; Plant water; Plant water dynamic; Plant water dynamics; Radar; Scatterometry; Soil moisture; Surface measurement; Surface variables; Vegetation; Water content; Water dynamics
• Online Access: View Fulltext in Publisher
• ISBN: 00344257 (ISSN)
• DOI: 10.1016/j.rse.2022.113116

A Deep Neural Network (DNN) is used to estimate the Advanced Scatterometer (ASCAT) C-band microwave normalized backscatter (σ40o), slope (σ′) and curvature (σ″) over France. The Interactions between Soil, Biosphere and Atmosphere (ISBA) land surface model (LSM) is used to produce land surface variables (LSVs) that are input to the DNN. The DNN is trained to simulate σ40o, σ′ and σ″ from 2007 to 2016. The predictive skill of the DNN is evaluated during an independent validation period from 2017 to 2019. Normalized sensitivity coefficients (NSCs) are computed to study the sensitivity of ASCAT observables to changes in LSVs as a function of time and space. Model performance yields a near-zeros bias in σ40o and σ′. The domain-averaged values of ρ are 0.84 and 0.85 for σ40o and σ′, compared to 0.58 for σ″. The domain-averaged unbiased RMSE is 8.6% of the dynamic range for σ40o and 13% for σ′, with land cover having some impact on model performance. NSC results show that the DNN-based model could reproduce the physical response of ASCAT observables to changes in LSVs. Results indicated that σ40o is sensitive to surface soil moisture and LAI and that these sensitivities vary with time, and are highly dependent on land cover type. The σ′ was shown to be sensitive to LAI, but also to root zone soil moisture due to the dependence of vegetation water content on soil moisture. The DNN could potentially serve as an observation operator in data assimilation to constrain soil and vegetation water dynamics in LSMs. © 2022 The Author(s)