Search

The launch of Sentinel-3A in February 2016 represented the beginning of a new long-term series of operational satellite radar altimeters, which will provide Delay-Doppler altimetry measurements over ice sheets for decades to come. Given the potential benefits that these satellites can offer to a range of glaciological applications, it is important to establish their capacity to monitor ice sheet elevation and elevation change. Here, we present the first analysis of Sentinel-3 Delay-Doppler altimetry over the Antarctic ice sheet, and assess the accuracy and precision of retrievals of ice sheet elevation across a range of topographic regimes. Over the low-slope regions of the ice sheet interior, we find that the instrument achieves both an accuracy and a precision of the order of 10 cm, with ∼98 % of the data validated being within 50 cm of co-located airborne measurements. Across the steeper and more complex topography of the ice sheet margin, the accuracy decreases, although analysis at two coastal sites with densely surveyed airborne campaigns shows that ∼60 %–85 % of validated data are still within 1 m of co-located airborne elevation measurements. We then explore the utility of the Sentinel-3A Delay-Doppler altimeter for mapping ice sheet elevation change. We show that with only 2 years of available data, it is possible to resolve known signals of ice dynamic imbalance and to detect evidence of subglacial lake drainage activity. Our analysis demonstrates a new, long-term source of measurements of ice sheet elevation and elevation change, and the early potential of this operational system for monitoring ice sheet imbalance for decades to come.

Studies of the global sea-level budget (SLB) and the global ocean-mass budget (OMB) are essential to assess the reliability of our knowledge of sea-level change and its contributions. Here we present datasets for times series of the SLB and OMB elements developed in the framework of ESA's Climate Change Initiative. We use these datasets to assess the SLB and the OMB simultaneously, utilising a consistent framework of uncertainty characterisation. The time series, given at monthly sampling, include global mean sea-level (GMSL) anomalies from satellite altimetry; the global mean steric component from Argo drifter data with incorporation of sea surface temperature data; the ocean mass component from Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry; the contribution from global glacier mass changes assessed by a global glacier model; the contribution from Greenland Ice Sheet and Antarctic Ice Sheet mass changes, assessed from satellite radar altimetry and from GRACE; and the contribution from land water storage anomalies assessed by the WaterGAP global hydrological model. Over the period Jan 1993–Dec 2016 (P1, covered by the satellite altimetry records), the mean rate (linear trend) of GMSL is 3.05 ± 0.24 mm yr−1. The steric component is 1.15 ± 0.12 mm yr−1 (38 % of the GMSL trend) and the mass component is 1.75 ± 0.12 mm yr−1 (57 %). The mass component includes 0.64 ± 0.03 mm yr−1 (21 % of the GMSL trend) from glaciers outside Greenland and Antarctica, 0.60 ± 0.04 mm yr−1 (20 %) from Greenland, 0.19 ± 0.04 mm yr−1 (6 %) from Antarctica, and 0.32 ± 0.10 mm yr−1 (10 %) from changes of land water storage. In the period Jan 2003–Aug 2016 (P2, covered by GRACE and the Argo drifter system), GMSL rise is higher than in P1 at 3.64 ± 0.26 mm yr−1. This is due to an increase of the mass contributions (now about 2.22 ± 0.15 mm yr−1, 61 % of the GMSL trend), with the largest increase contributed from Greenland. The SLB of linear trends is closed for P1 and P2, that is, the GMSL trend agrees with the sum of the steric and mass components within their combined uncertainties. The OMB budget, which can be evaluated only for P2, is also closed, that is, the GRACE-based ocean-mass trend agrees with the sum of assessed mass contributions within uncertainties. Combined uncertainties (1-sigma) of the elements involved in the budgets are between 0.26 and 0.40 mm yr−1, about 10 % of GMSL rise. Interannual variations that overlie the long-term trends are coherently represented by the elements of the SLB and the OMB. Even at the level of monthly anomalies the budgets are closed within uncertainties, while also indicating possible origins of remaining misclosures.

Information on sea level and its temporal and spatial variability is of great importance for various scientific, societal and economic issues. This article reports about a new sea level dataset for the North Sea (named NorthSEAL) of monthly sea level anomalies (SLA), absolute sea level trends and sea level mean annual amplitudes over the period 1995–2019. Uncertainties and quality flags are provided together with the data. The dataset has been created from multi-mission cross-calibrated altimetry data, preprocessed 5 with coastal dedicated approaches and gridded with innovative methods to a 6–8 km wide triangular mesh. The comparison of SLA and tide gauge time series shows a good consistency with average correlations of 0.85 and maximum correlations of 0.93. The improvement with respect to existing global gridded altimetry solutions amounts to 8–10 %, and it is most pronounced in complicated coastal environments such as river mouths or regions sheltered by islands. The differences in trends at tide gauge locations depend on the vertical land motion model used to correct relative sea level trends. The best 10 consistency with a median difference of 0.04 ± 1.15 mm/year is reached by applying a recent glacial isostatic adjustment (GIA) model. With the presented sea level dataset, for the first time, a regionally optimized product for the entire North Sea is made available. It will enable further investigations of ocean processes, sea level projections and studies on coastal adaptation measures. The NorthSEAL data is available at https://doi.org/10.17882/79673 (Müller et al., 2021).

The scope of this presentation is to feature and provide an update on the ESA G-POD/SARvatore family of altimetry services portfolio for the exploitation of CryoSat-2 and Sentinel-3 data from L1A (FBR) data products up to SAR/SARin Level-2 geophysical data products. At present, the following on-line & on-demand services compose the portfolio:- The SARvatore (SAR Versatile Altimetric TOolkit for Research & Exploitation) for CryoSat-2 and Sentinel-3 services developed by the Altimetry Team in the R&D division at ESA-ESRIN. These processor prototypes are versatile and allow the users to customize and adapt the processing at L1b & L2 according to their specific requirements by setting a list of configurable options. The scope is to provide users with specific processing options not available in the operational processing chains (e.g. range walk correction, stack sub-setting, extended receiving window, zero padding, high-posting rate and burst weighting at L1b & SAMOSA+, SAMOSA++ and ALES+ SAR retrackers at L2). AJoin & Share Forum (https://wiki.services.eoportal.org/tiki-custom_home.php) allows users to post questions and report issues. A data repository is also available to the Community to avoid the redundant reprocessing of already processed data (https://wiki.services.eoportal.org/tiki-index.php?page=SARvatore+Data+Repository&highlight=repository).- The TUDaBo SAR-RDSAR (Technical University Darmstadt – University Bonn SAR-Reduced SAR) for CryoSat-2 and Sentinel-3 service. It allows users to generate reduced SAR, unfocused SAR & LRMC data. Several configurable L1b & L2 processing options and retrackers (BMLE3, SINC2, TALES, SINCS) are available. The processor will be extended during an additional activity related to the ESA HYDROCOASTAL Project (https://www.satoc.eu/projects/hydrocoastal/) to account in the open ocean for the vertical motion of the wave particles (VMWP) in unfocused SAR and in a simplified form of the fully focused SAR called here Low Resolution Range Cell Migration Correction-Focused (LRMC-F). - The ALES+ SAR for CryoSat-2 and Sentinel-3 service. It allows users to process official L1b data and produces L2 NetCDF products by applying the empirical ALES+ SAR subwaveform retracker, including a dedicated SSB solution, developed by the Technische Universität München in the frame of the ESA Sea Level CCI (http://www.esa-sealevel-cci.org/) & BALTIC+ SEAL Projects (http://balticseal.eu/).- The Aresys Fully Focused SAR for CryoSat-2 service. Currently under development, it will provide the capability to produce CS-2 FF-SAR L1b products thanks to the Aresys 2D transformed frequency domain AREALT-FF1 processor prototype. Output products will also include geophysical corrections and threshold peak & ALES-like subwaveform retracker estimates.The G-POD graphical interface allows users to select, in all the services, a geographical area of interest within the time-frame related to the L1A (FBR) & L1b data products availability in the service catalogue. After the task submission, users can follow, in real time, the status of the processing. The output data products are generated in standard NetCDF format, therefore being compatible with the multi-mission “Broadview Radar Altimetry Toolbox” (BRAT, http://www.altimetry.info) and typical tools.Services are open, free of charge (supported by ESA) for worldwide scientific applications and available, after registration and activation (to be requested for each chosen service to eo-gpod@esa.int), at https://gpod.eo.esa.int.

Climate-related sea level changes in the world coastal zones result from the superposition of the global mean rise due to ocean warming and land ice melt, regional changes mostly caused by non-uniform ocean thermal expansion and salinity changes, and small-scale coastal processes (e.g., shelf currents, wind & waves changes, fresh water input from rivers, etc.). So far, satellite altimetry has provided global gridded sea level time series up to 10-15 km to the coast only, preventing estimation of sea level changes very close to the coast. In the context of the ESA Climate Change Initiative coastal sea level project, we have developed a complete reprocessing of high-resolution (20 Hz) Jason-1, 2 and 3 altimetry data along the world coastal zones using the ALES (Adaptative Leading Edge Subwaveform) retracker combined with the XTRACK system dedicated to improve geophysical corrections at the coast. Here we present coastal sea level trends over the period 2002-2020 along the whole African continent. Different coastal sea level trend behaviors are observed over the study period. We compare the computed coastal trends in Africa with results we previously obtained in other regions (Mediterranean Sea, Northeastern Europe, north Indian Sea, southeast Asia and Australia).

STREAM -SaTellite based Runoff Evaluation And Mapping- is a conceptual hydrological model able to derive daily river discharge and runoff estimates from satellite soil moisture, precipitation and terrestrial water storage anomalies observations. The model is very simple and versatile: It requires a limited number of parameters (only eight) to simulate river discharge.The model simulates river discharge and gridded runoff at daily time scale with a 25 km spatial resolution. Forced by TRMM 3B42 rainfall data and ESA CCI soil moisture data and GRACE over five pilot large basins (Mississippi, Amazon, Niger, Danube and Murray Darling) the model already provided good runoff estimates especially over Amazon basin, with a Kling-Gupta efficiency (KGE) index greater than 0.92 both at the basin outlet and over several inner stations in the basin. Good results have been also obtained for Mississippi, Niger and Danube with KGE index greater than 0.75 for all the gauging stations.By considering the good performances of the STREAM model and by the continuous availability (in space and time) of satellite observations, this work presents an attempt to regionalize the STREAM model parameters. The Mississippi river basin has been taken as case study and specific relationships between model parameters and different predictors (climate variables such as precipitation and evaporation, soil vegetation and topography characteristics) have been developed. By using these relationships, STREAM parameter values have been directly obtained from readily available climatic and physiographic basin characteristics and model performances are still satisfactory (median KGE over the basin equal to 0.60). The capability to use these relationships in other hydrologically similar catchments will be investigated for the Danube and Amazon river basins. The final target is to obtain global relationships as to provide to provide daily, 25 km, global runoff maps from the STREAM approach.

The ALtimetry for BIOMass (ALBIOM) project is an ESA-funded Permanent Open Call Project that aims to retrieve forest biomass using Copernicus Sentinel-3 (S3) altimeter data. The overall goal of ALBIOM is to estimate biomass with sufficient accuracy to be able to increase existing satellite data for biomass retrieval, as well as to improve the global mapping and monitoring of this fundamental variable. The project core tasks consist of 1) an analysis of the sensitivity of altimetry backscatter data on land parameters; 2) the development and validation of a Sentinel-3 altimeter backscatter simulator, including the effect of both topography and vegetation and 3) the development and validation of a machine-learning biomass estimation algorithm.Here we present a summary of the results obtained from the project. The sensitivity analysis reveals that both the altimetric waveforms and the corresponding Normalised Radar Cross Sections (NRCSs) can be sensitive to the presence of biomass in the order of 100-400 tons/ha, but they are also influenced by topography and water bodies. Different sensitivities with respect to the different frequencies and resolution modes are observed, highlighting non-linear behaviours of the NRCSs. The use of differential NRCSs, defined as the difference among those calculated over two different bandwidths, was demonstrated to be not necessarily more sensitive to vegetation, as it was instead highlighted by previous studies like [Papa et al., 2003].The tracking window often appears partly or completely misplaced, when the tracking mode is in open-loop mode prescribing a predetermined range, and its size is often not long enough when collecting data over land, especially over regions with complex topography. The length and correct positioning of the tracking window over land represent therefore critical aspects for a study like ALBIOM.The modelling work has been focused on the development of a merged model approach to simulate altimeter waveforms over vegetated areas. The merging is obtained via the simultaneous use of the modifiedTor Vergata Scattering Model (TOVSM) [Ferrazzoli and Guerriero, 1995, 1996] to simulate the waveform of a flat surface covered by forest vegetation, and the use of the Soil And Vegetation Reflection Simulator (SAVERS) [Pierdicca et al., 2014], originally conceived for GNSS-Reflectometry, and here adapted to the Altimetry system. The simulator developed within ALBIOM shows promising ability to reproduce the general characteristics of the S3 waveforms. The simulations related to forested surfaces present at least two peaks, due to the top of canopy and to the ground, but the presence of topography may introduce other peaks in the waveforms, making the identification of vegetation and topographic effects challenging.Initial results on the algorithm development using Artificial Neural Networks (ANN) highlight some promising biomass estimates over specific areas (e.g. Central Africa) but also differences in algorithm performances among different regions. The corrected “ice” backscatter coefficient showed the highest sensitivity to biomass, but its values are often invalid over land, which limits the number of meaningful retrievals. The different altimeter tracking mode of Sentinel-3 over different areas of the globe (i.e., open loop and closed loop) could also be responsible for the differences in results.

This paper presents an innovative approach, STREAM – SaTellite based Runoff Evaluation And Mapping – to derive daily river discharge and runoff estimates from satellite soil moisture, precipitation and terrestrial water storage anomalies observations. Within a very simple model structure, the first two variables (precipitation and soil moisture) are used to estimate the quick-flow river discharge component while the terrestrial water storage anomalies are used for obtaining its complementary part, i.e., the slow-flow river discharge component. The two are then summed up to obtain river discharge and runoff estimates. The method is tested over the Mississippi river basin for the period 2003–2016 by using Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) rainfall data, European Space Agency Climate Change Initiative (ESA CCI) soil moisture data and Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage data. Despite the model simplicity, relatively high-performance scores are obtained in river discharge simulations, with a Kling-Gupta efficiency index greater than 0.65 both at the outlet and over several inner stations used for model calibration highlighting the high information content of satellite observations on surface processes. Potentially useful for multiple operational and scientific applications (from flood warning systems to the understanding of water cycle), the added-value of the STREAM approach is twofold: 1) a simple modelling framework, potentially suitable for global runoff monitoring, at daily time scale when forced with satellite observations only, 2) increased knowledge on the natural processes, human activities and on their interactions on the land.

Satellite altimetry missions provide a quasi-global synoptic view of sea level variations over more than 25 years and provide regional sea level (SL) indicators such as trends and accelerations. Estimating realistic uncertainties on these quantities is crucial to address current climate science questions. While uncertainty estimates are available for the global mean sea level (GMSL), information is not available at local scales so far. We estimate a local satellite altimetry error budget and use it to derive local error variance-covariance matrices, and estimate confidence intervals on trends and accelerations at the 90% confidence level. Over 1993–2019, we find that the average local sea level trend uncertainty is 0.83 mm.yr−1 with values ranging from 0.78 to 1.22 mm.yr−1. For accelerations, uncertainties range from 0.057 to 0.12 mm.yr−1, with a mean value of 0.062. We also perform a sensitivity study to investigate a range of plausible error budgets. Local error levels, error variance-covariance matrices, SL trends and accelerations, along with corresponding uncertainties are provided., Measurement(s) sea surface height Technology Type(s) satellite radar altimetry Factor Type(s) year of data collection Sample Characteristic - Environment sea • ocean Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13297757

International audience; In the context of the ESA Climate Change Initiative project, a new coastal sea level altimetry product has been developed in order to support advances in coastal sea level variability studies. Measurements from Jason-1,2&3 missions have been retracked with the Adaptive Leading Edge Subwaveform (ALES) Retracker and then ingested in the X-TRACK software with the best possible set of altimetry corrections. These two coastal altimetry processing approaches, previously successfully validated and applied to coastal sea level research, are combined here for the first time in order to derive a 16-year-long (June 2002 to May 2018), high-resolution (20-Hz), along-track sea level dataset in six regions: Northeast Atlantic, Mediterranean Sea, West Africa, North Indian Ocean, Southeast Asia and Australia. The study demonstrates that this new coastal sea level product called X-TRACK/ALES is able to extend the spatial coverage of sea level altimetry data ~3.5 km in the land direction, when compared to the X-TRACK 1-Hz dataset. We also observe a large improvement in coastal sea level data availability from Jason-1 to Jason-3, with data at 3.6 km, 1.9 km and 0.9 km to the coast on average, for Jason-1, Jason-2 and Jason-3, respectively. When combining measurements from Jason-1 to Jason-3, we reach a distance of 1.2-4 km to the coast. When compared to tide gauge data, the accuracy of the new altimetry near-shore sea level estimations also improves. In terms of correlations with a large set of independent tide gauge observations selected in the six regions, we obtain an average value of 0.77. We also show that it is now possible to derive from the X-TRACK/ALES product an estimation of the ocean current variability up to 5 km to the coast. This new altimetry dataset, freely available, will provide a valuable contribution of altimetry in coastal marine research community.

International audience; Since 2010, the CryoSat-2 satellite mission has enabled to largely improve sea ice freeboard estimations. But due to the complexity of radar echoes over sea ice, freeboard retrieval from altimetry still presents some errors and biases that further limit the potential of these observations for climate studies or for assimilation into models. Various methods have been explored, producing a large range of freeboard estimations. In this study, we analyze the main steps of the radar freeboard computation developed as part of the Cryo-SeaNice Project. The objective is to quantify the impacts of each processing method and to identify optimal strategies to improve freeboard estimations from SAR altimetry measurements. We consider two SAR processing options: the Hamming Window (HW) and with the Zero-Padding (ZP), and 2 retrackers: the Threshold First Maximum Retracker Algorithm (TFMRA) based on heuristic measurements and SAMOSA+ a retracker declined from model based analysis of the surface back-scatter. Four freeboard solutions are generated from combinations of the 2 processing options (HW and ZP or ZP only) and the 2 types of retrackers. In addition, an alternative to the Hamming Window method to filter out side-lobes errors is presented. The impacts of the different approaches to estimate freeboard are quantified from comparisons with Operation Ice Bridge (OIB) and the Beaufort Gyre Exploration project (BGEP) in situ data. Our results show that SAMOSA+ provides more precise freeboard estimations. This new time-series is available on CTOH website. We also identified some impacts of the Hamming Window for both retrackers. Finally, we present the potential of using the simpler threshold retracker but with a correction to account for the surface roughness that is calibrated against SAMOSA+.

The monitoring of rivers is not the primary objective of the Sentinel-3 mission. The first satellite of the constellation was launched in February 2016 and so far no study has investigated the joint use of altimeter, near-infrared and thermal sensors for discharge estimation. Nevertheless, similar sensors onboard other platforms have showed their ability to estimate river discharge also in scarcely gauged areas. The advantage of altimetry lies in the observation of water surface elevation, which can be proficiently used in approaches based on rating curve, empirical formulae or hydraulic modeling. Even though their use is limited, near-infrared sensors are successfully used to detect the variability of river discharge thanks to their high capacity to discriminate water from land. Thermal sensors are nearly completely unused, but the unique study that uses the difference in temperature of the river water between day and night for the estimation of water level, encourages its use for river discharge assessment as well. To improve the estimation of river discharge and foster studies that are aimed at monitoring ungauged rivers, the combination of the sensors is considered a viable path. The aim of this manuscript is to review these studies to show the limitations and the potentials of each sensor onboard the Sentinel-3 satellite and to investigate the added value of using these three sensors co-located on the same platform for river discharge monitoring.

Information on sea level and its temporal and spatial variability is of great importance for various scientific, societal, and economic issues. This article reports about a new sea level dataset for the North Sea (named North SEAL) of monthly sea level anomalies (SLAs), absolute sea level trends, and amplitudes of the mean annual sea level cycle over the period 1995–2019. Uncertainties and quality flags are provided together with the data. The dataset has been created from multi-mission cross-calibrated altimetry data preprocessed with coastal dedicated approaches and gridded with an innovative least-squares procedure including an advanced outlier detection to a 6–8 km wide triangular mesh. The comparison of SLAs and tide gauge time series shows good consistency, with average correlations of 0.85 and maximum correlations of 0.93. The improvement with respect to existing global gridded altimetry solutions amounts to 8 %–10 %, and it is most pronounced in complicated coastal environments such as river mouths or regions sheltered by islands. The differences in trends at tide gauge locations depend on the vertical land motion model used to correct relative sea level trends. The best consistency with a median difference of 0.04±1.15 mm yr−1 is reached by applying a recent glacial isostatic adjustment (GIA) model. With the presented sea level dataset, for the first time, a regionally optimized product for the entire North Sea is made available. It will enable further investigations of ocean processes, sea level projections, and studies on coastal adaptation measures. The North SEAL data are available at https://doi.org/10.17882/79673 (Müller et al., 2021).

Coastal zones have large social, economic and environmental values. They are more densely populated than the hinterland and concentrate large economic assets, critical infrastructures and human activities such as tourism, fisheries, navigation. Furthermore, coastal oceans are home to a wealth of living marine resources and very productive ecosystems. Yet, coastal zones are exposed to various natural and anthropogenic hazards. To reduce the risks associated with marine hazards, sustained coastal zone monitoring programs, forecasting and early warning systems are increasingly needed. Earth observations (EO), and in particular satellite remote sensing, provide invaluable information: satellite-borne sensors allow an effective monitoring of the quasi-global ocean, with synoptic views of large areas, good spatial and temporal resolution, and sustained time-series covering several years to decades. However, satellite observations do not always meet the precision required by users, in particular in dynamic coastal zones, characterized by shorter-scale variability. A variety of sensors are used to directly monitor the coastal zone and their observations can also be integrated into numerical models to provide a full 4D monitoring of the ocean and forecasts. Here, we review how EO, and more particularly satellite observations, can monitor coastal hazards and their drivers. These include coastal flooding, shoreline changes, maritime security, marine pollution, water quality, and marine ecology shifts on the one hand, and several physical characteristics (bathymetry, topography, vertical land motion) of coastal zones, meteorological and oceanic (metocean) variables that can act as forcing factors for coastal hazards on the other hand.

The Earth Energy Imbalance (EEI) is a key indicator to understand the Earth’s changing. However, measuring this indicator is challenging since it is a globally integrated variable whose variations are small, of the order of several tenth of W.m-2, compared to the amount of energy entering and leaving the climate system of ~340 W.m-2. Recent studies suggest that the EEI response to anthropogenic GHG and aerosols emissions is 0.5-1 W.m-2. It implies that an accuracy of the direct measurement of in situ temperature based on temperature/Salinity profiles (e.g. ARGO floats), the measurement of the net ocean surface heat fluxes from space (CERES), the estimate from ocean reanalyses that assimilate observations from both satellite and in situ instruments, the measurement of the thermal expansion of the ocean from space based on differences between the total sea-level content derived from altimetry measurements and the mass content derived from GRACE data (noted “Altimetry-GRACE”). To date, the best results are given by the first method based on ARGO network. However ARGO measurements do no sample deep ocean below 2000 m depth and marginal seas as well as the ocean below sea ice. Re-analysis provides a more complete estimation but large biases in the polar oceans and spurious drifts in the deep ocean mask a significant part of the OHC signal related to EEI. The method based on estimation of ocean net heat fluxes (CERES) is not appropriate for OHC calculation due to a too strong uncertainty (±15 W.m-2). In the MOHeaCAN project supported by ESA, we are being developed the “Altimetry-GRACE” approach which is promising since it provides consistent spatial and temporal sampling of the ocean, it samples the entire global ocean, except for polar regions, and it provides estimates of the OHC over the ocean’s entire depth. Consequently, it complements the OHC estimation from ARGO. However, to date the uncertainty in OHC from this method is close to 0.5 W.m-2, and thus greater than the requirement of 0.3 W.m-2 needed to a good EEI estimation. Therefore the scientific objective of the MOHeaCan project is to improve these estimates :by developing novel algorithms in order to reach the challenging target for the uncertainty quantification of 0.3 W. m−2; by estimating realistic OHC uncertainties thanks to an error budget of measurements applying a rigorous mathematical formalism; by developing a software prototype systems that allow to perform sensitivities studies and OHC product and its uncertainty generation; by assessing our estimation by performing comparison against independent estimates based on ARGO network, and based on the Clouds and the Earth’s Radiant energy System (CERES) measurements at the top of the atmosphere.

Water is at the centre of economic and social development; it is vital to maintain health, grow food, manage the environment, produce renewable energy, support industrial processes and create jobs. Despite the importance of water, to date over one third of the world's population still lacks access to drinking water resources and this number is expected to increase due to climate change and outdated water management. As over half of the world’s potable water supply is extracted from rivers, either directly or from reservoirs, understanding the variability of the stored water on and below landmasses, i.e., runoff, is of primary importance. Apart from river discharge observation networks that suffer from many known limitations (e.g., low station density and often incomplete temporal coverage, substantial delay in data access and large decline in monitoring capacity), runoff can be estimated through model-based or observation-based approaches whose outputs can be highly model or data dependent and characterised by large uncertainties. On this basis, developing innovative methods able to maximize the recovery of information on runoff contained in current satellite observations of climatic and environmental variables (i.e., precipitation, soil moisture, terrestrial water storage anomalies and land cover) becomes mandatory and urgent. In this respect, within the European Space Agency (ESA) STREAM Project (SaTellite based Runoff Evaluation And Mapping), a solid “observational” approach, exploiting space-only observations of Precipitation (P), Soil Moisture (SM) and Terrestrial Water Storage Anomalies (TWSA) to derive total runoff has been developed and validated. Different P and SM products have been considered. For P, both in situ and satellite-based (e.g., Tropical Rainfall Measuring Mission, TRMM 3B42) datasets have been collected; for SM, Advanced SCATterometer, ASCAT, and ESA Climate Change Initiative, ESA CCI, soil moisture products have been extracted. TWSA time series are obtained from the latest Goddard Space Flight Center’s global mascon model, which provides storage anomalies and their uncertainties in the form of monthly surface mass densities per approximately 1°x1° blocks. Total runoff estimates have been simulated for the period 2003-2017 at 5 pilot basins across the world (Mississippi, Amazon, Niger, Danube and Murray Darling) characterised by different physiographic/climatic features. Results proved the potentiality of satellite observations to estimate runoff at daily time scale and at spatial resolution better than GRACE spatial sampling. In particular, by using satellite TRMM 3B42 rainfall data and ESA CCI soil moisture data, very good runoff estimates have been obtained over Amazon basin, with a Kling-Gupta efficiency (KGE) index greater than 0.92 both at the closure and over several inner stations in the basin. Good results found for Mississippi and Danube are also encouraging with KGE index greater than 0.75 for both the basins.

The advent of SAR (delay-Doppler) altimetry allowed the production of data with a high spatial resolution (300 m along-track). Investigations in the inland water domain clearly benefited from SAR data and future processing strategies (e.g. the fully-focused SAR, FF-SAR) are expected to improve further the quantity of data points over water bodies of a reduced size.The proposed work aims at investigating the quality of Sentinel-3 water level retrievals over three targets of different characteristics: the Ohio River, the Columbia River and the Great Salt Lake. Data are processed through the ESA G-POD SARvatore online and on-demand processing service for the exploitation of CryoSat-2 and Sentinel-3 data (https://gpod.eo.esa.int/services/SENTINEL3_SAR/) and obtained by using the SAMOSA2, SAMOSA+ & SAMOSA++ retrackers. The selected posting rate of measurements is 80 Hz to optimize the location of data points over the Ohio and Columbia River (an estimate every 80 m along-track), however a comparison with the 20 Hz posting rate is being made. Empirical retrackers outputs, available in the official 20 Hz Sentinel-3 LAN products, are also considered for comparison and water masks from (Pekel et al., 2016) are used to select data points acquired over water bodies.The main goal of this study is to analyse the key parameters characterizing both the L1b SAR waveform and the retracking (e.g. the Pulse Peakiness, the Misfit…) to define a robust error characterization method that is expected to filter out an increased number of outliers. A validation exercise using in situ data will be presented to demonstrate that the proposed method leads to the definition of a reduced, highly reliable dataset, associated with a realistic error characterization model.The study is expected to unlock possible synergies with SWOT and support the comparison of SAR estimates to FF-SAR estimates obtained at a comparable along-track resolution.

The unique contribution of satellite radar altimetry to river monitoring is well understood, with ‘ altimeter virtual gauge’ heights increasingly ingested into river basin models. However, altimeters gather a wealth of additional information. Waveform shapes reflect underlying topographic variation, surface composition and roughness, and distribution of surface water within the footprint. Backscatter measurements allow soil surface moisture under the satellite track to be determined, using DRy EArth ModelS (DREAMS) crafted from multi-mission altimeter data and ground truth. Initially developed over desert areas, DREAMs are now being built over river basins to extend the scope of altimeter soil moisture measurement.This paper investigates the potential contribution of these additional data to river basin analysis and modelling. The following key questions are addressed. 1) How useful are the data encoded in complex waveform shapes? 2) Can altimeter soil moisture estimates contribute to modelling in river basins?A series of example river basins were chosen in different topographic and climate situations, including the Amazon, Orinoco, Nile, Niger and Congo basins, and wetlands including the Okavango delta.This paper presents outcomes from analysis of multi-mission altimetry, with ERS-1/2, Envisat, Topex, Jason-1/2, Cryosat-2 and Sentinel-3A/B, plus a database of over 86,000 river and lake timeseries.The analysis outcomes demonstrate the value of altimeter soil surface moisture estimates, both as co-temporal and co-spatial data with inland water height measurements, and as an independent validation dataset to assess soil moisture estimates derived from other remote sensing techniques. The precise backscatter cross-calibration of altimeters on successive missions allows derivation of long soil moisture time series. The ability of nadir-pointing altimeters to penetrate vegetation canopy gives a unique perspective in rainforest areas, with information on underlying water height and extent as well as surface soil moisture. Waveform shape classification allows diverse information to be gleaned, particularly at the higher pulse repetition frequencies of the new generation of SAR Altimeters. In conclusion, satellite radar altimeters collect a wealth of information over river basins; this valuable resource is not yet fully exploited.

The scope of this presentation is to feature the G-POD SARvatore service to users for the exploitation of CryoSat-2 and Sentinel-3 data, which was designed and developed by the Altimetry Team in the R&D division at ESA-ESRIN. The G-POD service coined SARvatore (SAR Versatile Altimetric Toolkit for Ocean Research & Exploitation) is a web platform that allows any scientist to process on-line, on-demand and with user-selectable configuration CryoSat-2 SAR/SARin and Sentinel-3 SAR data, from L1A (FBR) data products up to SAR/SARin Level-2 geophysical data products. The G-POD graphical interface allows users to select a geographical area of interest within the time-frame related to the Cryosat-2 SAR/SARin FBR and Sentinel-3 L1A data products availability in the service catalogue. The processor prototype is versatile, allowing users to customize and to adapt the processing according to their specific requirements by setting a list of configurable options. Pre-defined processing configurations (Official CryoSat-2, Official Sentinel-3, Open Ocean, Coastal Zone, Inland Water (20Hz & 80Hz), Ice and Sea-Ice) are available. After the task submission, users can follow, in real time, the status of the processing. The output data products are generated in standard NetCDF format, therefore being compatible with the multi-mission “Broadview Radar Altimetry Toolbox” (BRAT, http://www.altimetry.info) and typical tools.Initially, the processing was designed and optimized uniquely for open ocean studies. It was based on the SAMOSA model developed for the Sentinel-3 Ground Segment. However, since June 2015, the SAMOSA+ retracker is available as a dedicated retracker for coastal zone, inland water and sea-ice/ice-sheet. A new retracker (SAMOSA++) has been recently developed and will be made available in the future. The scope is to maximize the exploitation of CryoSat-2 and Sentinel-3 data over all surfaces providing user with specific processing options not available in the default processing chains.Recent improvements include: 1) A Join & Share Forum to allow users to post questions and report issues (https://wiki.services.eoportal.org/tiki-custom_home.php); 2) A data repository to better support the growing Altimetry Community avoiding the redundant reprocessing of already processed data (https://wiki.services.eoportal.org/tiki-index.php?page=SARvatore+Data+Repository&highlight=repository); 3) A new function in the GUI allowing users to compute the geodetic distance between selected points on the map; 4) A new function in the GUI to filter the products search to a specific RON (Relative Orbit Number) and to a specific pass direction (Ascending or Descending). Furthermore, users will find in the folder SUM_RESDIR of the output data package a short summary report with information on the products that have not been processed and instructions on how to eventually try to re-process the missing data.To respond to the request of hydrologists, and simulate data that a river gauge would provide, SARvatore will soon include a post-processing service to convert water level estimates in L2 data to virtual station water level values, which are typically required by hydrologists. Validation of SARvatore data over river targets will be presented to demonstrate the potential of both the SAMOSA+/++ retrackers and the innovative processing configurations not available in the default CryoSat-2 and Sentinel-3 processing chains.

RIDESAT - RIver flow monitoring and Discharge Estimation by integrating multiple SATellite data, is an ESA-funded Permanent Open Call project aimed to develop a new methodology for estimating river discharge through the combination of radar altimeter, optical and thermal satellite sensors. The combination of multi-sensor measurements can provide significant advantages over single sensors contributing to improve the quality of the final products also in terms of spatial and temporal coverage.The methodology developed in the project includes two phases. First, the single-instrument products (altimeter, optical and thermal sensors) are independently processed to generate a dataset of proxies of hydraulic variables strongly linked with river discharge (e.g. water level, flow velocity, width). Successively, these proxies are implemented as integrated techniques for the final estimation of the river discharge.To test the ability of the approach to retrieve river discharge at global scale, 20 pilot sites are selected all over the world, based on the availability of in-situ measurements and the climatic characteristics of the basins. The availability of large datasets of in situ measurements is used for: 1) the validation of single-instrument products and the river discharge product; 2) the evaluation of the uncertainty attributed to the combination process; 3) the evaluation of the limitation of the procedure.

The use of satellite altimetry at high latitudes and coastal regions is currently limited by the presence of seasonal sea ice coverage, and the proximity to the coast. The semi-enclosed Baltic Sea features seasonal coverage of sea-ice in the northern and coastal regions, and complex jagged coastlines with a huge number of small islands. However, as a semi-enclosed sea with a considerable extent, the Baltic Sea features a much-reduced tidal signal, both open- and coastal- waters, and an extensive multi-national network of tide-gauges. These factors maximise opportunities to drive improvements in sea-level estimations for coastal, and seasonal-ice regions.The ESA Baltic SEAL project, launched in April 2019, aims to exploit these opportunities. It is generating and validating a suite of enhanced multi-mission sea level products. Processing is developed specifically for coastal regions, with the objective of achieving a consistent description of the sea-level variability in terms of long-term trends, seasonal variations and a mean sea-surface. These will advance knowledge on adapting processing algorithms, to account for seasonal ice, and complex coastlines. Best practice approaches will be available to update current state-of-the-art datasets.In order to fulfill these goals, a novel altimeter re-tracking strategy has been developed. This enables the homogeneous determination of sea-surface heights for open-ocean, coastal and sea-ice conditions (ALES+). An unsupervised classification algorithm based on artificial intelligence routines has been developed and tailored to ingest data from all current and past satellite altimetry missions. This identifies radar echoes, reflected by narrow cracks within the sea-ice domain. Finally, the improved altimetry observations are gridded onto a triangulated surface mesh, featuring a spatial resolution greater than 1/4 degree. This is more suitable for utility for coastal areas, and use by coastal stakeholders.In addition to utilizing a wide range of altimetry data (Delay-Doppler and Pulse-Limited systems), the Baltic SEAL initiative harnesses the Baltic Seas unique characteristics to test novel geophysical corrections (e.g. wet troposphere correction), use the latest generation of regional altimetry datasets, and evaluate the benefits of the newest satellite altimetry missions. This presentation outlines the methodology and results achieved to date. These include estimations of a new regional mean sea surface, and insights into the trends of the sea level along the altimetry tracks with the longest records. The transfer of advances to other regions and sea-level initiatives are also highlighted.

The ALtimetry for BIOMass project (ALBIOM) is ESA-funded Permanent Open Call Project that proposes to derive forest biomass using Copernicus Sentinel-3 (S3) SAR altimeter data. The project targets the need to improve our current global observations of biomass as an Essential Climate Variable (ECV) and crucial for bioenergy, risk mitigation activities, and sustainable management of forests. The overall goal is to estimate biomass with sufficient accuracy to be able to increase the existing satellite data for biomass retrieval and to improve the global mapping and monitoring of this fundamental variable.The project originates from evidence that radar altimetry backscatter over land responds to a variety of land parameters, including vegetation-related parameters, at the different bands used by past and existing altimeters.To achieve the scientific objectives, the project is structured into six conceptual tasks. After a review of the literature and of the existing user needs, a sensitivity analysis is performed to understand the relationship between SAR altimetry backscatter data and land parameters themselves. This is followed by the development of a Sentinel-3 SAR altimeter backscatter simulator over vegetated areas, and then by the development of a biomass inversion algorithm, testing different retrieval methodologies, both theoretical and empirical. A validation task for both the model and the algorithm is carried out over specific test sites of boreal and tropical forests, to finally generate a prototype of biomass product to be reviewed by potential users.The sensitivity analysis allows to understand how the S3 Level 1 backscatter power waveforms change with respect to varying biomass, but also how they are affected by other land parameters such as soil moisture, land cover, topography and roughness. This analysis is carried out considering both the single-looked and the multi-looked waveforms, and considering primarily the high-resolution SAR mode, but also the Pseudo Low Resolution Mode (PLRM). The outcome of the sensitivity analysis provides indication of what waveforms, acquisition mode, observables derived from the waveforms and characteristics of the waveforms themselves respond more strongly to biomass variations, and on the degree of influence of the other auxiliary parameters, informing on the best strategies and approaches to adopt for the development of the retrieval algorithm.Subsequent to the sensitivity analysis, the S3 altimetry backscatter simulator is developed over vegetated areas, reproducing both the coherent scattering component, which represents the echo from the ground, and incoherent scattering component arising from the forest layers between the ground and the top of canopy. The approach followed is similar to that of the SAVERS simulator, developed for GNSS-Reflectometry, with the signal backscatter attenuation introduced by branches, leaves and trunks modelled through the discrete approach of the Tor Vergata Scattering Model.Results from the sensitivity analysis and the initial stages of the simulation development will be presented and discussed at the conference, together with the foreseen approaches for the development of the biomass retrieval algorithm.

The scope of this work is to showcase the BRAT (Broadview Radar Altimetry Toolbox) and GUT (GOCE User Toolbox) toolboxes.The Broadview Radar Altimetry Toolbox (BRAT) is a collection of tools designed to facilitate the processing of radar altimetry data from all previous and current altimetry missions, including Sentinel-3A L1 and L2 products. A tutorial is included providing plenty of use cases on Geodesy & Geophysics, Oceanography, Coastal Zone, Atmosphere, Wind & Waves, Hydrology, Land, Ice and Climate, which can also be consulted in http://www.altimetry.info/radar-altimetry-tutorial/.BRAT's last version (4.2.1) was released in June 2018. Based on the community feedback, the front-end has been further improved and simplified whereas the capability to use BRAT in conjunction with MATLAB/IDL or C/C++/Python/Fortran, allowing users to obtain desired data bypassing the data-formatting hassle, remains unchanged. Several kinds of computations can be done within BRAT involving the combination of data fields, that can be saved for future uses, either by using embedded formulas including those from oceanographic altimetry, or by implementing ad-hoc Python modules created by users to meet their needs. BRAT can also be used to quickly visualise data, or to translate data into other formats, e.g. from NetCDF to raster images.The GOCE User Toolbox (GUT) is a compilation of tools for the use and the analysis of GOCE gravity field models. It facilitates using, viewing and post-processing GOCE L2 data and allows gravity field data, in conjunction and consistently with any other auxiliary data set, to be pre-processed by beginners in gravity field processing, for oceanographic and hydrologic as well as for solid earth applications at both regional and global scales. Hence, GUT facilitates the extensive use of data acquired during GRACE and GOCE missions.In the current version (3.2), GUT has been outfitted with a graphical user interface allowing users to visually program data processing workflows. Further enhancements aiming at facilitating the use of gradients, the anisotropic diffusive filtering, and the computation of Bouguer and isostatic gravity anomalies have been introduced. Packaged with GUT is also GUT's Variance/Covariance Matrix (VCM) tool, which enables non-experts to compute and study, with relative ease, the formal errors of quantities – such as geoid height, gravity anomaly/disturbance, radial gravity gradient, vertical deflections – that may be derived from the GOCE gravity models.On our continuous endeavour to provide better and more useful tools, we intend to integrate BRAT into SNAP (Sentinel Application Platform). This will allow our users to easily explore the synergies between both toolboxes. During 2020 we will start going from separate toolboxes to a single one.BRAT and GUT toolboxes can be freely downloaded, along with ancillary material, at https://earth.esa.int/brat and https://earth.esa.int/gut.

SCOOP (SAR Altimetry Coastal & Open Ocean Performance) is a project funded under the ESA SEOM (Scientific Exploitation of Operational Missions) Programme Element, to characterise the expected performance of Sentinel-3 SRAL SAR mode altimeter products, and then to develop and evaluate enhancements to the baseline processing scheme in terms of improvements to ocean measurements. Another objective is to develop and evaluate an improved Wet Troposphere correction for Sentinel-3.The SCOOP studies are based on two 2-year test data sets derived from CryoSat-2 FBR data, produced for 10 regions. The first Test Data Set was processed with algorithms equivalent to the Sentinel-3 baseline, and the second with algorithms expected to provide an improved performance.We present results from the SCOOP project that demonstrate the excellent performance of SRAL at the coast in terms of measurement precision, with noise in Sea Surface Height 20Hz measurements of less than 5cm to within 5km of the coast.We then report the development and testing of new processing approaches designed to improve performance, including, for L1B to L2:Application of zero-padding Application of intra-burst Hamming windowing Exact beam forming in the azimuthal direction Restriction of stack processing to within a specified range of look angles. Along-track antenna compensation And for L1B to L2Application of alternative re-trackers for SAR and RDSAR. Based on the results of this assessment, a second test data set was generated and we present an assessment of the performance of this second Test Data Set generated, and compare it to that of the original Test Data Set.Regarding the WTC for Sentinel-3A, the correction from the on-board MWR has been assessed by means of comparison with independent data sets such as the GPM Microwave Imager (GMI), Jason-2, Jason-3 and Global Navigation Satellite Systems (GNSS) derived WTC at coastal stations. GNSS-derived path Delay Plus (GPD+) corrections have been derived for S3A. Results indicate good overall performance of S3A MWR and GPD+ WTC improvements over MWR-derived WTC, particularly in coastal and polar regions. Based on the outcomes of this study we provide recommendations for improving SAR mode altimeter processing and priorities for future research.

Sea level change is an important indicator of climate change. Our study focuses on the sea level budget assessment of the Arctic Ocean using: (1) the newly reprocessed satellite altimeter data with major changes in the processing techniques; (2) ocean mass change data derived from GRACE satellite gravimetry; (3) and steric height estimated from gridded hydrographic data for the GRACE/Argo time period (2003–2016). The Beaufort Gyre (BG) and the Nordic Seas (NS) regions exhibit the largest positive trend in sea level during the study period. Halosteric sea level change is found to dominate the area averaged sea level trend of BG, while the trend in NS is found to be influenced by halosteric and ocean mass change effects. Temporal variability of sea level in these two regions reveals a significant shift in the trend pattern centered around 2009–2011. Analysis suggests that this shift can be explained by a change in large-scale atmospheric circulation patterns over the Arctic. The sea level budget assessment of the Arctic found a residual trend of more than 1.0 mm/yr. This nonclosure of the sea level budget is further attributed to the limitations of the three above mentioned datasets in the Arctic region.

Satellite altimetry is a key technique to measure water level change in continental water bodies. Altimetry-based water level time series of rivers are typically constructed at locations where the satellite ground tracks intersect the rivers, the so-called virtual stations. The relatively low sampling frequency (10–27 days) of the repeat missions may result in an under-sampling of the hydrological regime in rivers with sub-monthly to weekly events. We are currently in a unique position with more than a handful satellite altimetry missions simultaneously mapping the surface elevation of the Earth. In combination, these missions contain an unexploited potential to obtain a more detailed picture of the hydrological regime of many of the Earth's rivers. The task of combining water levels measured at different locations and/or by satellites with different orbits is however, challenging due to e.g. topography, intermission biases, variation in river morphology, and other unidentified causes.In this work, we present a new method to combine multi-mission altimetry-based water levels from a river reach. This will also enable the use of geodetic missions like CryoSat-2 and SARAL/AltiKa (after June 2016) in water level time series. To combine the data we set up a state-space model where the process part is a first-order autoregressive process. The observations as a function of time and distance along the reach are described as a sum of the water level at a given time scaled by a distance-dependent factor, the mean water level at the given distance, and an error term. The scale factor and the mean water level are modeled with spline functions. We employ the model for the six rivers Lena, Solimões, Mississippi, Danube, Po, and Red, which range in width from 3 km to a few hundred meters. For each river, we consider a reach of 200–300 km and apply water levels from the satellite altimetry missions CryoSat-2, Sentinel-3A/3B, and SARAL/AltiKa. The selected reach must have a continuous elevation profile and preferable no major tributaries, which might alter the hydrological regime considerably. The length of the reach is a compromise of ensuring enough data but not violating the aforementioned criteria.When validated against in situ data we find a root mean square error ranging from 0.34 m (Solimões River) to 2.53 m (Lena River) and a correlation ranging from 0.83 (Danube River) to 0.99 (Solimões River). These summary statistics are based on approximately 2000–3000 pairs of in situ and modeled water levels. We find the largest increase in detail for the reconstructed water levels for the Danube, Po, and Red Rivers, where the water level variations are under-sampled at the virtual stations. For the Po River, we can detect sub-weekly events with the model and for the Lena River, the spring flood related to ice and snowmelt is better captured when combining the data. An additional advantage of the approach is that the water level time series can be reconstructed at all locations along the considered reach.

The Arctic Ocean is a challenging region for tidal modelling. The accuracy of the global tidal models decreases by several centimeters in the Polar Regions, which has a large impact on the quality of the satellite altimeter sea surface heights and the altimetry-derived products. NOVELTIS, DTU Space and LEGOS have developed Arctide2017, a regional, high-resolution tidal atlas in the Arctic Ocean, in the framework of an extension of the CryoSat Plus for Ocean (CP4O) ESA STSE (Support to Science Element) project. In particular, this atlas benefits from the assimilation of the most complete satellite altimetry dataset ever used in this region, including Envisat data up to 82°N and CryoSat-2 data between 82°N and 88°N. The combination of these satellite altimetry missions gives the best possible coverage of altimetry-derived tidal constituents. The available tide gauge data were also used for data assimilation and validation. This paper presents the implementation methodology and the performance of this new regional tidal model in the Arctic Ocean, compared to the existing global and regional tidal models.

Significant wave height (SWH) and surface wind speed (WS) products from the CryoSat-2 Delay-Doppler, which is commonly known as Synthetic Aperture Radar (SAR), Mode are validated against operational ECMWF atmospheric and wave model results in addition to available observations from buoys, platforms and Jason-2 altimeter. The CryoSat-2 SAR Mode data are processed from Level 1A (also known as Full Bit Rate, FBR, in the CryoSat-2 terminology) up to L1B in accordance to the Delay-Doppler algorithm, and then retracked using SAMOSA (SAR Altimetry MOde Studies and Applications) SAR return waveform model, as implemented in the Grid Processing on Demand (G-POD) service called SAR Versatile Altimetric Toolkit for Ocean Research and Exploitation (SARvatore). The data cover two geographic boxes: one in the northeast Atlantic Ocean (NE Atlantic Box) for the period from 6 September 2010 to 30 June 2014 and the other is in the eastern Pacific (Pacific Box) for the period from 7 May 2012 to 30 June 2014. The amount of data is limited by the CryoSat-2 SAR Mode acquisition mask over ocean but is large enough to ensure robustness and significance of the results. The results show that the quality of both CryoSat-2 SAR SWH and WS products is very high when compared to typical altimetry mission requirements. When compared against model and in-situ data, the correlation coefficients are as high as 0.98 for SWH and 0.95 for WS while the bias and standard deviation of the difference is less than 5% and 0.3 m, respectively, for SWH and less than 0.3 m/s and 1.3 m/s, respectively for WS. The results show that the quality of both CryoSat-2 SAR SWH and WS products is in line with Jason-2 performances and satisfies the typical altimetry mission requirements.

This study uniquely processes Cryosat-2 Full Bit Rate (FBR) SAR Level 1A data to recover inland water heights. The processing methodology involves an azimuthal Fast Fourier Transform (FFT) for the burst echo data followed by beam formation directed towards equi-angular ground points, stacking, slant range correction, multi-looking and finally retracking. It is seen that speckle in the burst echo data affects the recovered heights with precise heights recovered only through stacking and forming multi-look waveforms. Also investigated is the effect of different numbers of multi-looks in the stack to form the final waveform for retracking. A number of empirical retrackers are utilized over inland waters and compared against the oceanic SAMOSA2 and the OCOG/Threshold retrackers. Use of the SAMOSA2 retracker is shown to be inappropriate for inland waters. The use of 81 multi-looks from the stack centred on the nadir direction is shown to be preferred across Tonle Sap with the RMS of height residuals in the range 4–6 cm. External validation across Tonle Sap using gauge data shows that CryoSat-2 heights (RMS 42.1 cm) are comparable to OSTM (RMS 42.6 cm) despite the CryoSat-2 non-repeating orbit which precludes the use of a mean profile. Validation against gauge data at Kratie on the Mekong gives an RMS of 59.9 cm for Cryosat-2 against an RMS of 35.5 cm and 52.2 cm derived from Envisat. The CryoSat-2 results utilize an approximate correction for river slope as the river crossings span 5 km upstream to 80 km downstream of the gauge while the repeat pass crossings of Envisat are at 7 km and 43 km from the gauge. Validation of Amazon altimetric Surface Water Elevation (SWE) showed RMS agreement of 27.3 cm with Obidos gauge data and 56.3 cm at Manacapuru 650 km upstream of Obidos. Overall validations showed that CryoSat-2 altimetric river heights are more accurate than those from TOPEX/Poseidon, OSTM and Envisat for relatively large water bodies but less accurate than the Ka band SARAL (Satellite with ARgos and ALtiKa).

Water level from sea ice-covered oceans is particularly challenging to retrieve with satellite radar altimeters due to the different shapes assumed by the returned signal compared with the standard open ocean waveforms. Valid measurements are scarce in large areas of the Arctic and Antarctic Oceans, because sea level can only be estimated in the openings in the sea ice (leads and polynyas). Similar signal-related problems affect also measurements in coastal and inland waters. This study presents a fitting (also called retracking) strategy (ALES+) based on a subwaveform retracker that is able to adapt the fitting of the signal depending on the sea state and on the slope of its trailing edge. The algorithm modifies the existing Adaptive Leading Edge Subwaveform retracker originally designed for coastal waters, and is applied to Envisat and ERS-2 missions. The validation in a test area of the Arctic Ocean demonstrates that the presented strategy is more precise than the dedicated ocean and sea ice retrackers available in the mission products. It decreases the retracking open ocean noise by over 1 cm with respect to the standard ocean retracker and is more precise by over 1 cm with respect to the standard sea ice retracker used for fitting specular echoes. Compared to an existing open ocean altimetry dataset, the presented strategy increases the number of sea level retrievals in the sea ice-covered area and the correlation with a local tide gauge. Further tests against in-situ data show that also the quality of coastal retrievals increases compared to the standard ocean product in the last 6 km within the coast. ALES+ improves the sea level determination at high latitudes and is adapted to fit reflections from any water surface. If used in the open ocean and in the coastal zone, it improves the current official products based on ocean retrackers. First results in the inland waters show that the correlation between water heights from ALES+ and from in-situ measurement is always over 0.95.

ISI Document Delivery No.: FV7JXTimes Cited: 4Cited Reference Count: 249Tauro, Flavia Selker, John van de Giesen, Nick Abrate, Tommaso Uijlenhoet, Remko Porfiri, Maurizio Manfreda, Salvatore Caylor, Kelly Moramarco, Tommaso Benveniste, Jerome Ciraolo, Giuseppe Estes, Lyndon Domeneghetti, Alessio Perks, Matthew T. Corbari, Chiara Rabiei, Ehsan Ravazzani, Giovanni Bogena, Heye Harfouche, Antoine Brocca, Luca Maltese, Antonino Wickert, Andy Tarpanelli, Angelica Good, Stephen Alcala, Jose Manuel Lopez Petroselli, Andrea Cudennec, Christophe Blume, Theresa Hut, Rolf Grimaldi, SalvatoreTaylor & francis ltdAbingdon; To promote the advancement of novel observation techniques that may lead to new sources of information to help better understand the hydrological cycle, the International Association of Hydrological Sciences (IAHS) established the Measurements and Observations in the XXI century (MOXXI) Working Group in July 2013. The group comprises a growing community of tech-enthusiastic hydrologists that design and develop their own sensing systems, adopt a multi-disciplinary perspective in tackling complex observations, often use low-cost equipment intended for other applications to build innovative sensors, or perform opportunistic measurements. This paper states the objectives of the group and reviews major advances carried out by MOXXI members toward the advancement of hydrological sciences. Challenges and opportunities are outlined to provide strategic guidance for advancement of measurement, and thus discovery.

Satellite radar altimetry provides a unique sea level data set that extends over more than 25 years back in time and that has an almost global coverage. However, when approaching the coasts, the extraction of correct sea level estimates is challenging due to corrupted waveforms and to errors in most of the corrections and in some auxiliary information used in the data processing. The development of methods dedicated to the improvement of altimeter data in the coastal zone dates back to the 1990s, but the major progress happened during the last decade thanks to progress in radar technology [e.g., synthetic aperture radar (SAR) mode and Ka-band frequency], improved waveform retracking algorithms, the availability of new/improved corrections (e.g., wet troposphere and tidal models) and processing workflows oriented to the coastal zone. Today, a set of techniques exists for the processing of coastal altimetry data, generally called “coastal altimetry.” They have been used to generate coastal altimetry products. Altimetry is now recognized as part of the integrated observing system devoted to coastal sea level monitoring. In this article, we review the recent technical advances in processing and the new technological capabilities of satellite radar altimetry in the coastal zone. We also illustrate the fast-growing use of coastal altimetry data sets in coastal sea level research and applications, as high-frequency (tides and storm surge) and long-term sea level change studies.

By convention the absolute bias in sea surface height (SSH) is the difference between the altimeter and the in-situ reference SSH heights above the Earth ellipsoid. Both the absolute and the relative bias of the CryoSat-2 and Sentinel-3A missions are derived in this study at four stations along the German coasts.

The quality of existing bathymetry models for the Arctic Ocean is evaluated through visual comparison and the response of modelled tides. The high resolution ArcTide 2017 hydrodynamic model was used to evaluate the bathymetry in selected shallow water regions where tides are significant. The Southern Barents Sea was identified as a problematic region where inconsistencies were identified, resulting from methods used to patch in regional models and incorrect definitions of coastlines and depths. More generally, the investigation shows that careful verifications are needed to ensure seamless transitions between bathymetry datasets.

Floods are one of the most dramatic natural disasters, in terms of casualties and economic damage. Remote sensing offers an efficient and reliable tool for detecting the inundation extent over the affected areas and for monitoring the water level within the river channel. The latter is important for forecasting flood extreme events and 25 years of satellite radar altimetry, even if not properly designed for inland water, provide an important instrument for monitoring rivers and floods. This chapter investigates the use of altimetry and its synergy with other satellite sensors for monitoring water surface elevation and river discharge with particular attention to estimating and forecasting floods.

This chapter provides an overview of the satellite altimetry measurement system, how it works and how the water surface elevation is derived. Satellite altimetry had originally been designed for open ocean studies, however a decade of progress has significantly improved the retrieval of data in coastal zones and inland waters. Advances in observational techniques (Ka-band and delay-Doppler/SAR-mode), revisited data processing, and improved corrections led to a higher accuracy in water surface elevation retrieval in these challenging areas. The available data sets with consistent coastal processing as well as the existing products dedicated to inland waters are presented including their potential accuracy for exploitation. The latest developments and capabilities of the various altimetric missions around coasts and inland are illustrated, with an emphasis on describing the benefits to studies of extreme events (heavy river discharge and storm surges). The future satellite altimetry missions such as Sentinel-3 and Sentinel-6/Jason-CS are expected to increase capabilities and coverage and represent a great opportunity to stimulate further exploitation of altimeter data in coastal and inland water targets.

Because it integrates changes in several components of the climate system in response to external forcing factors and internal climate variability, sea level is one of the best indicators of global climate change.

A regional cross-calibration between the first Delay-Doppler altimetry dataset from Cryosat-2 and a retracked Envisat dataset is here presented, in order to test the benefits of the Delay-Doppler processing and to expand the Envisat time series in the coastal ocean. The Indonesian Seas are chosen for the calibration, since the availability of altimetry data in this region is particularly beneficial due to the lack of in-situ measurements and its importance for global ocean circulation. The Envisat data in the region are retracked with the Adaptive Leading Edge Subwaveform (ALES) Retracker, which has been previously validated and applied successfully to coastal sea level research.The study demonstrates that CryoSat-2 is able to decrease the 1-Hz noise of sea level estimations by 0.3 cm within 50 km of the coast, when compared to the ALES-reprocessed Envisat dataset. It also shows that Envisat can be confidently used for detailed oceanographic research after the orbit change of October 2010. Cross-calibration at the crossover points indicates that in the region of study a sea state bias correction equal to 5% of the significant wave height is an acceptable approximation for Delay-Doppler altimetry.The analysis of the joint sea level time series reveals the geographic extent of the semiannual signal caused by Kelvin waves during the monsoon transitions, the larger amplitudes of the annual signal due to the Java Coastal Current and the impact of the strong La Niña event of 2010 on rising sea level trends.

About 97 % of the total amount of water on Earth is found in the oceans and 2 % is stored in the Greenland and Antarctic ice sheets. It is only the remaining 1 % that is the amount of water available for the biospheric processes and for all human needs. This fresh water component is stored in both surface and subsurface reservoirs. On the surface, the storage volumes consist of rivers, lakes, man-made reservoirs, wetlands and inundated areas.

This chapter provides some basics of the interpretation of satellite altimetry measurements and explains how to use altimetry for hydrology. Satellite altimetry has been extensively used over the last two decades to calculate the water height variations over the Earth's lakes, rivers, reservoirs, and floodplains. Time series of altimetry-based water levels can be used to detect the signature of climate variability and extreme climatic events on the surface water storage. Echoes are acquired due to a tracking system placed on board the satellite known as "trackers". In the nominal tracking mode, the Jason-2 Poseidon-3 altimeter uses the "median tracker" concept, which again ensures the independence of shape pulse power weighting similar to the Free Model Tracker design on board Envisat. A dry tropospheric correction must be applied to the ranges in order to account for the delay of propagation of the radar pulse through the atmosphere.

International audience; About 10% of the global population is currently livingalong the coasts. In many regions, populations are exposed to a variety of natural hazards (e.g., extreme weather such as damaging cyclones and their associated storm surges), as well as to the effects of global climate change (e.g., sea level rise), and to the impacts of human activities (e.g., urbanization). Today, our knowledge regarding these processes still remains limited by the lack of observations. For example, the proportion of the world's shorelines currently affected by erosion still remains uncertain. This lack of information not only prevents us from addressing important scientific questions, but it has also practical implications for coastal managers in charge of managing coastal risks and adapting to climate change. In this poster, we present the outcome of the International Space Science Institute (ISSI) Forum on " Monitoring the evolution of coastal zones under various forcing factors using space-based observing systems " (http://www.issibern.ch/forum/costzoneevo/) held at ISSI, Bern, Switzerland on 11-12 October 2016. This poster first reviews the scientific questions with high societal significance, where improved remote sensing observations are needed: this includes (1) separating the contributions of climate-induced sea-level changes and vertical ground motions (uplift and subsidence) in relative (coastal) sea-level changes; (2) understanding the roles, for each different coastal geomorphological setting, of human interventions, extreme events, seasonal interannual and multidecadal variability and trends in driving coastal evolution. In a second step, we review theobservations currently available or needed to address these questions. Overall, we show that since the publication of the latest IGOS report on coastal zone observational requirements (2006), the availability of high resolution topographic data, hydrometeorological reanalysis (e.g., wind, waves, pressures) and historical surge databases have greatly improved the ability to understand and model coastal flooding. In addition, there is a continued need for tide gauges collocated with GNSS and other geodetic data. However, research is needed in many other topics such as the retrieval of changing topographic and bathymetric features at the required accuracy and frequency, and in processing radar altimetry measurements in the coastal ocean. Concerning ocean color, global analyses are expected to provide useful information (e.g. on suspended materials). Besides the improvements of the current observing infrastructure, there is a need of strengthening the exchanges between different scientists and stakeholders concerned with coastal risks and climate change. Today, information on the evolution of coastal zones is managed at local to regional scales by coastal observatories. These entitieslinkscienceinformation to operational observations (including space-based)and coastal stakeholders. We argue that establishing links between global providers of Earth Observation data (such as space agencies), and the emerging networks of coastal observatories, can be beneficial to both coastal science and the management of coastal risks.

10th Coastal Altimetry Workshop; Florence, Italy, 21–24 February 2017