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Accurate estimates of ocean mass change are necessary to infer steric sea level change from sea level changes measured with satellite altimeters. Published studies using the Gravity Recovery and Climate Experiment (GRACE) satellite mission indicated a large range in trends (∼1–2 mm/year) with reported standard errors of 0.1–0.3 mm/year. Here we show that a large part of this discrepancy (up to 0.6 mm/year) can be explained by which model is used to account for the effect of glacial isostatic adjustment (GIA). The second largest contribution (0.3–0.4 mm/year) is related to the way how different studies have restored atmospheric and oceanic signals which have been removed during the GRACE gravity estimation process. Here two processing strategies, which previously resulted in differing ocean mass trends, are considered. The “direct” method uses the standard GRACE Stokes coefficients, while the “inverse” method applies a joint inversion of data from GRACE and altimetry. After accounting for differences in processing corrections, global mean ocean mass estimates from the direct, the mascon, and inverse approach agree with each other on global scales within less than 0.1 mm/year. Using the A et al. (2013; https://doi.org/10.1093/gji/ggs030) GIA model, we provide a reconciled monthly time series of global mean ocean mass, which suggests that ocean mass has increased by 1.43 mm/year over 2002.6–2014.5, with an amplified rate of 1.75 mm/year over 2002.6–2016.5 which covers almost the complete GRACE time span. However, we note that estimates as low as 1.05 mm/year cannot be ruled out when other published GIA corrections with lower mass‐equivalent signals over Antarctica are used.

Monitoring surface soil moisture (SSM) variability is essential for understanding hydrological processes, vegetation growth, and interactions between land and atmosphere. Due to sparse distribution of in-situ soil moisture networks, over the last two decades, several active and passive radar satellite missions have been launched to provide information that can be used to estimate surface conditions and subsequently soil moisture content of the upper few cm soil layers. Some recent studies reported the potential of satellite altimeter backscatter to estimate SSM, especially in arid and semi-arid regions. They also pointed out some difficulties of such technique including: (i) the noisy behavior of the backscatter estimations mainly caused by surface water in the radar footprint , (ii) the assumptions for converting altimetry backscatter to SSM, and (iii) the need for interpolating between the tracks. In this study, we introduce a new inversion framework to retrieve soil moisture information from along-track altimetry measurements. First, 20 Hz along-track nadir radar backscatter is estimated by post-processing waveforms from Jason-2 (Ku-and C-Band during 2008-2014) and Envisat (Ku-and S-Band during 2002-2008). This provides backscatter measurements every ∼300 m along-track within every ∼10 days from Jason, and every ∼35 days from Envisat observations. Empirical orthogonal base-functions (EOFs) are then derived from soil moisture simulations of a hydrological model, and used as constraints within the inversion. Finally, along-track altimetry reconstructed surface soil moisture (ARSSM) storage is inverted by fitting these EOFs to the altimeter backscatter. The framework is tested in arid and semi-arid Western Australia, for which a high resolution hydrological model (the Australian Water Resource Assessment, AWRA

Satellite radar altimetry is one of the most powerful techniques for measuring sea surface height variations, with applications ranging from operational oceanography to climate research. Over open oceans, altimeter return waveforms generally correspond to the Brown model, and by inversion, estimated shape parameters provide mean surface height and wind speed. However, in coastal areas or over inland waters, the waveform shape is often distorted by land influence, resulting in peaks or fast decaying trailing edges. As a result, derived sea surface heights are then less accurate and waveforms need to be reprocessed by sophisticated algorithms. To this end, this work suggests a novel Spatio-Temporal Altimetry Retracking (STAR) technique. We show that STAR enables the derivation of sea surface heights over the open ocean as well as over coastal regions of at least the same quality as compared to existing retracking methods, but for a larger number of cycles and thus retaining more useful data. Novel elements of our method are (a) integrating information from spatially and temporally neighboring waveforms through a conditional random field approach, (b) sub-waveform detection, where relevant sub-waveforms are separated from corrupted or non-relevant parts through a sparse representation approach, and (c) identifying the final best set of sea surfaces heights from multiple likely heights using Dijkstra's algorithm. We apply STAR to data from the Jason-1, Jason-2 and Envisat missions for study sites in the Gulf of Trieste, Italy and in the coastal region of the Ganges-Brahmaputra-Meghna estuary, Bangladesh. We compare to several established and recent retracking methods, as well as to tide gauge data. Our experiments suggest that the obtained sea surface heights are significantly less affected by outliers when compared to results obtained by other approaches.

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