ASCAT normalized backscatter (σ₄₀^o) and slope (σ^′) contain valuable information about soil moisture and vegetation. While σ₄₀^o 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 σ₄₀^o 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.

Continuous hydrocarbon production and steam/water injection cause compaction and expansion of the reservoir rock, leading to irregular downward and upward ground movements. Detecting such anthropogenic ground movements is of importance, as they may significantly influence the safety and sustainability of hydrocarbon production activities, in particular, enhanced oil recovery (EOR) and even lead to local hazards, e.g. earthquakes and sinkholes. As InSAR (Interferometric Synthetic Aperture Radar) can routinely deliver global ground deformation observations on a weekly basis, with millimetre-level precision, it can be a cost-effective, and less labour intensive tool to monitor surface deformation changes due to hydrocarbon production activities. Aimed at identifying the associated deformation pattern changes, this study focuses on InSAR deformation model optimization, in order to automatically detect irregularities, both spatially and temporally. We apply multiple hypothesis testing to determine the best model based on a library of physically realistic canonical deformation models. We develop a cluster-wise constrained least-squares estimation method for parameter estimation, in order to directly introduce contextual information, such as spatio-temporal correlation, into the mathematical model. Here a cluster represents a group of spatially correlated InSAR measurement points. Our approach is demonstrated over an enhanced oil recovery site using a stack of TerraSAR-X images.