Surface deformation in California's Central Valley (CV) has long been linked to changes in groundwater storage. Recent advances in remote sensing have enabled the mapping of CV deformation and associated changes in groundwater resources at increasingly higher spatiotemporal resolution. Here, we use interferometric synthetic aperture radar (InSAR) from the Sentinel‐1 missions, augmented by continuous Global Positioning System (cGPS) positioning, to characterize the surface deformation of the San Joaquin Valley (SJV, southern two‐thirds of the CV) for consecutive dry (2016) and wet (2017) water years. We separate trends and seasonal oscillations in deformation time series and interpret them in the context of surface and groundwater hydrology. We find that subsidence rates in 2016 (mean −42.0 mm/yr; peak −345 mm/yr) are twice that in 2017 (mean −20.4 mm/yr; peak −177 mm/yr), consistent with increased groundwater pumping in 2016 to offset the loss of surface‐water deliveries. Locations of greatest subsidence migrated outwards from the valley axis in the wetter 2017 water year, possibly reflecting a surplus of surface‐water supplies in the lowest portions of the SJV. Patterns in the amplitude of seasonal deformation and the timing of peak seasonal uplift reveal entry points and potential pathways for groundwater recharge into the SJV and subsequent groundwater flow within the aquifer. This study provides novel insight into the SJV aquifer system that can be used to constrain groundwater flow and subsidence models, which has relevance to groundwater management in the context of California's 2014 Sustainable Groundwater Management Act (SGMA).

Earth surface displacements from interferometric synthetic aperture radar (InSAR) have long been used to study deformation from a wide range of geophysical processes. Whereas deformation rates can be robustly estimated from InSAR by averaging many individual deformation observations, noise in these observations has limited their utility for generating deformation time series. In this article, we introduce a novel combination of InSAR and Global Positioning System (GPS) data that align InSAR displacements to an absolute reference and reduces long-wavelength spatial errors prior to InSAR time series construction. We test our GInSAR (GPS-enhanced InSAR) methodology on Sentinel-1 data over the southern Central Valley, CA, USA, comparing GInSAR displacement velocities and time series with those from three other referencing techniques. We find that the GInSAR approach outperforms alternative methods, yielding mm-level displacement differences with respect to collocated cGPS. By contrast, other referencing methods can overestimate peak subsidence velocities in the Central Valley by upwards of 10%, deviate by tens of millimeters relative to cGPS validation time series, and contain spatial biases absent in the GInSAR methodology. We also present a modification to the widely used small baseline subset (SBAS) technique for time series estimation, whereby we use a temporal connectedness constraint to regularize the mathematical inversion and increase the number of InSAR pixels with valid time series estimates.

The fusion of global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) deformation data can leverage the advantages of GNSS high temporal resolution and InSAR high spatial resolution, and obtain more abundant deformation data for constraints on geophysical structural and mechanical parameters. Existing studies seldom consider the spatial heterogeneity of largescale deformation data, which easily leads to obvious spatial aggregation of errors in the results of fusion. Here, we propose a novel multiresolution segmentation fusion (MRSF) method that uses a multiresolution segmentation algorithm to automatically classify the spatial heterogeneity of InSAR deformation data with similar deformation characteristics. We applied the MRSF method to the fusion of GNSS and InSAR deformation data covering the central valley aquifer system (CVAS) in southern California to verify its precision and robustness. Results show that the MRSF method can accurately reflect spatiotemporal evolution characteristics of displacement data and reliably estimate deformation for the times and locations of missing data. We then tested this method for geophysical parameter estimation by constructing three different sets of data, including dense GNSS sites, sparse GNSS sites, and sparse GNSS sites fused with InSAR data using MRSF, to invert the slip distribution of the Cascadia subduction zone. Results show that the inverted slip of the fused InSAR and GNSS data is comparable to that of the dense GNSS sites. Therefore, the MRSF method can obtain deformation results with high precision and high spatiotemporal resolution and effectively compensate for the lack of data caused by sparse GNSS sites during the geophysical inversion process.

Recharge is a critical component for understanding aquifer systems and the sustainable management of groundwater resources, yet this process is challenging to measure at policy-relevant spatiotemporal scales. Building upon previous research, we tested the hypothesis that InSAR can be used to observe river-sourced recharge if the underlying recharge pathways are associated with sufficient clay content. Our analysis leveraged the decomposition of InSAR time series with interpretations of 3D resistivity models derived from airborne electromagnetic (AEM) surveys. We focused our analysis on two study sites where high density AEM data were available and river-sourced recharge is determined to have occurred during wet years: (1) near Fresno, California and (2) near Visalia, California. Sediment type and hydrogeological structure from AEM supported our hypothesis with the InSAR signature attributed to river-sourced recharge occurring only in the study site with semi-confined to confined conditions and relatively high fraction of interbedded clay within recharge pathways. The timing to peak amplitude, the key feature we wanted to isolate in the InSAR data, near Visalia was interpreted as a pressure pulse front associated with river-sourced recharge propagating into the San Joaquin Valley. This study further validated the potential of InSAR, coupled with AEM data, to map and monitor river-sourced recharge in aquifer systems. As InSAR data become more accessible, this approach holds promise for broader applications in groundwater science and management worldwide.