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As the first satellite altimetry mission operating in sar (delay-Doppler) mode, CryoSat-2 is an interesting mission to analyze its performance for water level monitoring over inland water bodies. It offers the opportunity to make comparison of sar and conventional altimeters by a multi-mode altimeter mounted on the same platform with a long repeat orbit. This comparison gives us more knowledge about the performance of the sar altimeter. Even tough it is not possible to perform it over same objects. In this paper we analyze the CryoSat-2 performance for water level monitoring via full- and sub-waveform retracking against in-situ gauge and L2 products of other satellite altimetry missions, e.g. Envisat and Jason-2. To this end, we retrack the full-waveforms and sub-waveforms with different empirical and physical retracking algorithms such as ocog , threshold, β -parameters and samosa 3. We evaluate its capability in all measurement modes, i.e. lrm , sar and sari n, over inland water bodies located in different climatic zones. We selected study areas with different shapes and sizes. Relative to in situ measurements we find a precision of the CryoSat-2 lrm mode of 15 cm rms over Qinghai lake (China) and 13 cm over Erie lake ( usa ). This is an improvement over Envisat, yielding precision of 17 cm in both cases. For the sar mode over Neagh lake (Northern Ireland) and Derg lake (Ireland) we obtain 15 cm and 13 cm where Envisat yields 28 cm and 100 cm , respectively. The sari n mode’s precision is assessed over Nasser lake (Egypt) with 25 cm rms and Athabasca lake (Canada) with 16 cm . Over these lakes Jason-2 achieved 54 cm and Envisat 19 cm , respectively. The most precise results of CryoSat-2 are obtained with our retracking and sub-waveform selection scenarios. Comparing CryoSat-2 results from our retracking scenarios using L1b data with those results obtained from L2 products (data) of this mission shows an improvement of 4–17 cm. The minimum and maximum improvements belong to Erie and Derg lakes respectively, the largest and smallest lakes. From the waveform analysis over lakes with different shapes and sizes, we found that the first and the mean-all sub-waveforms (mean correction from all sub-waveforms) retracked with the threshold and samosa 3 (only for sar mode) retrackers are appropriate to retrieve water level variation of small lakes and complex shaped lakes in this study. Over large lakes the full-waveform retracking leads to better results. In the case of icy-lake objects, sub-waveform retracking scenarios (the first and mean-all sub-waveforms) are more precise than the other scenarios. These are our findings over few samples, though more samples need to be analyzed to support them strongly.

This article aims to provide a tour of satellite missions for Coastal Hazards Monitoring, of relevant applications, as well as the downstream International Services such as the Copernicus Ocean and Land Monitoring Services. Earth observation (EO) satellite remote sensing provides global, repetitive and long-term observations with increasing resolution with every new generation of sensors. They permit the monitoring of small-scale signals like the ones impacting the coastal zone. EO missions are showcased in this article. Transforming the data products based on the satellite mission ground segment (usually called geophysical products, geophysical data records or so-called Level 2 products) into information useable by managers and decision-makers is done by downstream international services. This is an essential step to increase the uptake of satellite data for the benefit of society. Here, the type of services provided by, e.g., the European Copernicus Programme, is described along with examples of applications, such as monitoring storm surges.

Sentinel-3 is a multi-instrument mission designed to measure sea-surface topography, sea- and land-surface temperature, ocean color and land color with high resolution and accuracy. The S3 mission is based on a constellation of two (3A and 3B) polar-orbiting satellites and it is designed and operated in the framework of the Copernicus programme, with planned 3C and 3D to ensure continuity. The mission builds up the legacy of ERS-1, ERS-2, ENVISAT and particularly CryoSat for the altimeter. Seninel-3A was launched in February 2016 and Seninel-3B in April 2018. They are equipped with a dual-frequency (Ku- and C-band) altimeter and can work both in low resolution (LRM) and SAR mode, the latter being designed to achieve high along-track discrimination. The low-resolution mode exploits conventional pulse-limited altimeter operation at C band. To approximate LRM operation at Ku band, a pseudo low-resolution mode is achieved by properly processing SAR acquisitions. Recently, a new research project funded by the European Space Agency, i.e., ALtimetry for BIOMass (ALBIOM), has been initiated to study the possibility of deriving forest biomass using Sentinel-3 altimetry data. ALBIOM aims at improving biomass global dataset, which is defined and classified as an Essential Climate Variable. In the last two decades, the exploitation of radar altimetry for studying land parameters has received renewed interest, including processing for the characterization of vegetation features and soil moisture. The vegetation cover has two main effects on the nadir backscatter measured by the altimeter. It attenuates the coherent reflection of the soil and add an incoherent volume scattering contribution. The relative weight of the two contributions depends of course form the frequency. To assess in what extent radar altimetry data are sensitive to the presence of vegetation forest, a study of the dynamic of the Sentinel-3 power waveforms with respect to the above ground biomass is needed. More importantly, the way radar waveforms are affected by disturbing land parameters, such as soil moisture, topography and surface roughness, has to be understood. In this work, an analysis considering both high- and low-resolution data made available by the Copernicus hub service is carried out. The sensitivity study of Sentinel-3 altimetry data to forest biomass over Africa is based on calibrated Sentinel-3 waveforms combined in space and time with forest biomass maps and ancillary information on the soil topography derived from a Digital Elevation Model. Comparison among Ku- and C-band waveforms are discussed, highlighting the critical aspect of the correct positioning of the time-tracking window over land, which often appears partly or completely misplaced, determining waveforms either truncated or containing noise only. The detrimental effect of the waveform truncation for the estimation of biomass and the possible mitigation approach has been considered. The study revealed that both waveforms and NRCSs can be sensitive to the presence of biomass in the order of 100-400 tons/ha, even if they can be strongly influenced by the presence of irregular topography within the system footprint. Different sensitivities with respect to the three channels (i.e., bandwidths and resolution modes) have been observed. A study about the use of differential NRCSs, defined as the difference between two different bandwidths, proposed by previous studies, is under investigation. Further research activities also connected to a modelling approach are in progress and will be discussed at the conference.

The Earth energy imbalance (EEI) at the top of the atmosphere is responsible for the accumulation of heat in the climate system. Monitoring the EEI is therefore necessary to better understand the Earth's warming climate. Measuring the EEI is challenging as it is a globally integrated variable whose variations are small (0.5–1 W m−2) compared to the amount of energy entering and leaving the climate system (∼340 W m−2). Since the ocean absorbs more than 90 % of the excess energy stored by the Earth system, estimating the ocean heat content (OHC) change provides an accurate proxy of the EEI. This study provides a space geodetic estimation of the OHC changes at global and regional scales based on the combination of space altimetry and space gravimetry measurements. From this estimate, the global variations in the EEI are derived with realistic estimates of its uncertainty. The mean EEI value is estimated at +0.74±0.22 W m−2 (90 % confidence level) between August 2002 and August 2016. Comparisons against estimates based on Argo data and on CERES measurements show good agreement within the error bars of the global mean and the time variations in EEI. Further improvements are needed to reduce uncertainties and to improve the time series, especially at interannual timescales. The space geodetic OHC-EEI product (version 2.1) is freely available at https://doi.org/10.24400/527896/a01-2020.003 (Magellium/LEGOS, 2020).

ALtimetry for BIOMass (ALBIOM) is a research project funded by the European Space Agency to study the possibility of estimating above ground biomass by means of low- and high-resolution Sentinel-3 altimetry data. We present preliminary results of a sensitivity analysis, developed to assess in what extent waveforms and altimetry observables are affected by the presence of forests. Ku- and C-band altimetry data have been collected and processed to properly select well-tracked waveforms over land, which are then related to collocated biomass data. A statistical analysis has been performed, highlighting a sensitivity of the estimated normalized radar cross section with respect to forest biomass, even though it is strongly disturbed by the soil topography within the radar footprint. Additionally, the inaccurate positioning of the time-tracking window limits the number of useful data.

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.

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.

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.

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−2, with a mean value of 0.062. We also perform a sensitivity study to investigate a range of plausible error budgets.A dataset consisiting of a single NetCDF file containing local error levels, error variance-covariance matrices, SL trends and accelerations, along with corresponding uncertainties is provided (https://doi.org/10.17882/74862). Code to reproduce the study is also distributed (https://github.com/pierre-prandi/rsl). With this information, users should be able to reuse these error levels to derive uncertainties on any metric (e.g. inter annual variability) or time period.

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; 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.

The monitoring of rivers by satellite is an up-to-date subject in hydrological studies as confirmed by the interest of space agencies to finance specific missions that respond to the quantification of surface water flows. We address the problem by using multi-spectral sensors, in the near-infrared (NIR) band, correlating the reflectance ratio between a dry and a wet pixel extracted from a time series of images, the C/M ratio, with five river flow-related variables: water level, river discharge, flow area, mean flow velocity and surface width. The innovative aspect of this study is the use of the Ocean and Land Colour Instrument (OLCI) on board Sentinel-3 satellites, compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) used in previous studies. Our results show that the C/M ratio from OLCI and MODIS is more correlated with the mean flow velocity than with other variables. To improve the number of observations, OLCI and MODIS products are combined into multi-mission time series. The integration provides good quality data at around daily resolution, appropriate for the analysis of the Po River investigated in this study. Finally, the combination of only MODIS products outperforms the other configurations with a frequency slightly lower (~1.8 days).

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 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 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.

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.

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.

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.

Present-day global mean sea level rise is caused by ocean thermal expansion, ice mass loss from glaciers and ice sheets, as well as changes in terrestrial water storage. For that reason, sea level is one of the best indicators of climate change as it integrates the response of several components of the climate system to internal and external forcing factors. Monitoring the global mean sea level allows detecting changes (e.g., in trend or acceleration) in one or more components. Besides, assessing closure of the sea level budget allows us to check whether observed sea level change is indeed explained by the sum of changes affecting each component. If not, this would reflect errors in some of the components or missing contributions not accounted for in the budget. Since the launch of TOPEX/Poseidon in 1992, a precise 27-year continuous record of sea level change is available. It has allowed major advances in our understanding of how the Earth is responding to climate change. The last two decades are also marked by the launch of the GRACE satellite gravity mission and the development of the Argo network of profiling floats. GRACE space gravimetry allows the monitoring of mass redistributions inside the Earth system, in particular land ice mass variations as well as changes in terrestrial water storage and in ocean mass, while Argo floats allow monitoring sea water thermal expansion due to the warming of the oceans. Together, satellite altimetry, space gravity, and Argo measurements provide unprecedented insight into the magnitude, spatial variability, and causes of present-day sea level change. With this observational network, we are now in a position to address many outstanding questions that are important to planning for future sea level rise. Here, we detail the network for observing sea level and its components, underscore the importance of these observations, and emphasize the need to maintain current systems, improve their sensors, and supplement the observational network where gaps in our knowledge remain.

A major challenge for managing impacts and implementing effective mitigation measures and adaptation strategies for coastal zones affected by future sea level (SL) rise is our limited capacity to predict SL change at the coast on relevant spatial and temporal scales. Predicting coastal SL requires the ability to monitor and simulate a multitude of physical processes affecting SL, from local effects of wind waves and river runoff to remote influences of the large-scale ocean circulation on the coast. Here we assess our current understanding of the causes of coastal SL variability on monthly to multi-decadal timescales, including geodetic, oceanographic and atmospheric aspects of the problem, and review available observing systems informing on coastal SL. We also review the ability of existing models and data assimilation systems to estimate coastal SL variations and of atmosphere-ocean global coupled models and related regional downscaling efforts to project future SL changes. We discuss (1) observational gaps and uncertainties, and priorities for the development of an optimal and integrated coastal SL observing system, (2) strategies for advancing model capabilities in forecasting short-term processes and projecting long-term changes affecting coastal SL, and (3) possible future developments of sea level services enabling better connection of scientists and user communities and facilitating assessment and decision making for adaptation to future coastal SL change., RP was funded by NASA grant NNH16CT00C. CD was supported by the Australian Research Council (FT130101532 and DP 160103130), the Scientific Committee on Oceanic Research (SCOR) Working Group 148, funded by national SCOR committees and a grant to SCOR from the U.S. National Science Foundation (Grant OCE-1546580), and the Intergovernmental Oceanographic Commission of UNESCO/International Oceanographic Data and Information Exchange (IOC/IODE) IQuOD Steering Group. SJ was supported by the Natural Environmental Research Council under Grant Agreement No. NE/P01517/1 and by the EPSRC NEWTON Fund Sustainable Deltas Programme, Grant Number EP/R024537/1. RvdW received funding from NWO, Grant 866.13.001. WH was supported by NASA (NNX17AI63G and NNX17AH25G). CL was supported by NASA Grant NNH16CT01C. This work is a contribution to the PIRATE project funded by CNES (to TP). PT was supported by the NOAA Research Global Ocean Monitoring and Observing Program through its sponsorship of UHSLC (NA16NMF4320058). JS was supported by EU contract 730030 (call H2020-EO-2016, “CEASELESS”). JW was supported by EU Horizon 2020 Grant 633211, Atlantos.

Coastal zones are highly dynamical systems affected by a variety of natural and anthropogenic forcing factors that include sea level rise, extreme events, local oceanic and atmospheric processes, ground subsidence, etc. However, so far, they remain poorly monitored on a global scale. To better understand changes affecting world coastal zones and to provide crucial information to decision-makers involved in adaptation to and mitigation of environmental risks, coastal observations of various types need to be collected and analyzed. In this white paper, we first discuss the main forcing agents acting on coastal regions (e.g., sea level, winds, waves and currents, river runoff, sediment supply and transport, vertical land motions, land use) and the induced coastal response (e.g., shoreline position, estuaries morphology, land topography at the land-sea interface and coastal bathymetry). We identify a number of space-based observational needs that have to be addressed in the near future to understand coastal zone evolution. Among these, improved monitoring of coastal sea level by satellite altimetry techniques is recognized as high priority. Classical altimeter data in the coastal zone are adversely affected by land contamination with degraded range and geophysical corrections. However, recent progress in coastal altimetry data processing and multi-sensor data synergy, offers new perspective to measure sea level change very close to the coast. This issue is discussed in much detail in this paper, including the development of a global coastal sea-level and sea state climate record with mission consistent coastal processing and products dedicated to coastal regimes. Finally, we present a new promising technology based on the use of Signals of Opportunity (SoOp), i.e., communication satellite transmissions that are reutilized as illumination sources in a bistatic radar configuration, for measuring coastal sea level. Since SoOp technology requires only receiver technology to be placed in orbit, small satellite platforms could be used, enabling a constellation to achieve high spatio-temporal resolutions of sea level in coastal zones., Part of this work has been carried out with the financial support of the following research projects: Spanish research grant ESP2015-70014-C2-2-R (MINECO/FEDER) and Spanish Ministry of Science, Innovation and Universities: RTI2018-099008-B-C22, ESA contract Sea_Level_cci (No. 4000126561/19/I-NB).

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.

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.

International audience; Low relief oceanic islands often suffer from scarcity of freshwater resources. Remote sensing has proved to be an effective tool to generate valuable data for hydrological analysis and has improved the management of ecosystems and water. However, remotely sensed data are often tested over areas with existing validation databases and not always where the need is greatest. In this paper we address the need for topographical data to understand the hydrological system of Santa Cruz Island (Galapagos archipelago) so that management of freshwater ecosystems and resources can take place. No high resolution, high accuracy topographical data exist for Santa Cruz Island, and its growing population has created an urgent need for water resource management and protection of unique and pristine ecosystems. Inaccessible National Park land covers more than 97% of Galapagos territory, which makes the use of remote sensing methods indispensable. SRTM data was insufficient in terms of grid size (90 m) to carry out the needed data analysis. We used ASAR data (ENVISAT) in VV polarization image mode for Digital Elevation Model (DEM) generation, in order to extract drainage network, watersheds, and flow characteristics from a morpho-structural analysis. Results show the high potential of these data for both interferometric and radargrammetric generation methods. Although interferometry suffered from low coherence over highly vegetated areas, it showed high precision over the rest of the island. Radargrammetry gave consistent results over the entire island, and details were enhanced by integrating the 90 m SRTM data as an external DEM. Accuracy of the SRTM and the combined radargrammetric/SRTM DEM was similar, with the radargrammetric having a finer pixel-based resolution (20 m). Validation of the extracted drainage networks and watersheds was carried out using ground-based field observations and comparison to mapped river networks visually extracted from aerial photographs and high resolution (1 m) satellite imagery available on GoogleEarth(C). For the first time, watershed characteristics and flow paths were made available for an island of the Galapagos archipelago. Furthermore, the drainage network is shown to be strongly influenced by observed and extracted structural discontinuities. Having characterized freshwater flow, water balance calculations were carried out for Pelican Bay watershed, where urban areas, agricultural land and Galapagos National Park land are concomitant.

The annual cycle is a major contribution to the non-tidal variability in sea level. Its characteristics can vary substantially even at a regional scale, particularly in an area of high variability such as the coastal ocean. This study uses previously validated coastal altimetry solutions (from ALES dataset) and the reference ESA Sea Level Climate Change Initiative dataset to improve the understanding of the annual cycle during the Envisat years (2002-2010) in the North Sea - Baltic Sea transition area. This area of study is chosen because of the complex coastal morphology and the availability of in-situ measurements.To our knowledge, this is the first time that the improvements brought by coastal satellite altimetry to the description of the annual variability of the sea level have been evaluated and discussed. The findings are interpreted with the help of a local climatology and wind stress from a reanalysis model.The coastal amplitude of the annual cycle estimated from ALES altimetry data is in better agreement with estimations derived from in-situ data than the one from the reference dataset. Wind stress is found to be the main driver of annual cycle variability throughout the domain, while different steric contributions are responsible for the differences within and among the sub-basins.We conclude that the ALES coastal altimetry product is a reliable dataset to study the annual cycle of the sea level at a regional scale and the strategy described in this research can be applied to other areas of the coastal ocean where the coverage from the tide gauges is not sufficient.

8th Coastal Altimetry Workshop; Konstanz, Germany, 23–24 October 2014

Sea level is one of the 50 Essential Climate Variables (ECVs) listed by the Global Climate Observing System (GCOS) in climate change monitoring. In the last two decades, sea level has been routinely measured from space using satellite altimetry techniques. In order to address a number of important scientific questions such as: "Is sea level rise accelerating?", "Can we close the sea level budget?", "What are the causes of the regional and interannual variability?", "Can we already detect the anthropogenic forcing signature and separate it from the internal/natural climate variability?", and "What are the coastal impacts of sea level rise?", the accuracy of altimetry-based sea level records at global and regional scales needs to be significantly improved. For example, the global mean and regional sea level trend uncertainty should become better than 0.3 and 0.5 mm year−1, respectively (currently of 0.6 and 1–2 mm year−1). Similarly, interannual global mean sea level variations (currently uncertain to 2–3 mm) need to be monitored with better accuracy. In this paper, we present various respective data improvements achieved within the European Space Agency (ESA) Climate Change Initiative (ESA CCI) project on "Sea Level" during its first phase (2010–2013), using multi-mission satellite altimetry data over the 1993–2010 time span. In a first step, using a new processing system with dedicated algorithms and adapted data processing strategies, an improved set of sea level products has been produced. The main improvements include: reduction of orbit errors and wet/dry atmospheric correction errors, reduction of instrumental drifts and bias, inter-calibration biases, intercalibration between missions and combination of the different sea level data sets, and an improvement of the reference mean sea surface. We also present preliminary independent validations of the SL_cci products, based on tide gauges comparison and sea level budget closure approach, as well as comparisons with ocean re-analyses and climate model outputs.

Soil moisture was recently included in the list of Essential Climate Variables (ECVs) that are deemed essential for IPCC (Intergovernmental Panel on Climate Change) and UNFCCC (United Nations Framework Convention on Climate Change) needs and considered feasible for global observation. ECVs data records should be as long, complete and consistent as possible, and in the case of soil moisture this means that the data record shall be based on multiple data sources, including but not limited to active (scatterometer) and passive (radiometer) microwave observations acquired preferably in the low-frequency microwave range. Among the list of sensors that can be used for this task are the C-band scatterometers on board of the ERS and METOP satellites and the multi-frequency radiometers SMMR, SSM/I, TMI, AMSR-E, and Windsat. Together, these sensors already cover a time period of more than 30 years and the question is how can observations acquired by these sensors be merged to create one consistent data record? This paper discusses on a high-level possible approaches for fusing the individual satellite data. It is argued that the best possible approach for the fusion of the different satellite data sets is to merge Level 2 soil moisture data derived from the individual satellite data records. This approach has already been demonstrated within the WACMOS project (http://wacmos.itc.nl/) funded by European Space Agency (ESA) and will be further improved within the Climate Change Initiative (CCI) programme of ESA (http://www.esa-cci.org/).

In this paper, we will showcase the main outcomes of the COASTALT project, including improved corrections (with special emphasis on the tropospheric effect) and novel re-tracking techniques built on established research results and the processing chain development with the generation and validation of ENVISAT test data sets over a selection of regional sites. We will also dive in further and explore how coastal altimetry might be exploited to its full potential in the coastal zone. This should be of interest to a broad range of data integrators that have an interest in using these improved altimeter data in their operational products or services.

[Departement_IRSTEA]DS [TR1_IRSTEA]METHODO / SYNERGIE; International audience; Terrestrial waters-including snowpack, glaciers, water in aquifers and other geological formations, water in the plant root zone, rivers, lakes, man-made reservoirs, wetlands, and inundated areas-represent less than a mere 1% of the total amount of water on Earth. However, they have a crucial impact on terrestrial life and human needs and play a major role in climate variability. Land waters are continuously exchanged with the atmosphere and oceans in vertical and horizontal mass fluxes through evaporation, transpiration, and surface and subsurface runoff. Although it is now recognized that improved description of the terrestrial branch of the global water cycle is of major importance for climate research and for inventory and management of water resources, the global distribution and spatial-temporal variations of terrestrial waters are still poorly known because routine in situ observations are not available globally. So far, global estimates of spatial-temporal change of land water stored in soils and in the snowpack essentially rely on hydrological models, either coupled with atmosphere/ocean global circulation models and/or forced by observations.

Within the ESA Ground Segment, all the Radar Altimeter (RA-2) and Microwave Radiometer (MWR) received data will be systematically processed to Level 1B (geolocated and calibrated engineering parameters) and to Level 2 (geophysical data record). All data will be processed in near real time (availability to users within three hours from data take), then reprocessed as Intermediate GDR (within 3 days) and later in off-line (few weeks after data take). The three version of the Level 2 product have the same layout and provide the typical information of the classical GDR altimeter product. The main difference among them is constituted by the increasing quality of the used orbit. Another main feature of the Level 2 RA-2 product is that every received waveform is processed in parallel by four different algorithms (each one optimised for a particular type of surface: ocean, sea ice, ice 1, ice 2). The Level 2 product (in all its versions) contains the results of the four retracking processes. The Level 1b product NRT and Off-line and the Level 2 NRT products are generated in the ESA station, while the IGDR and the Off-line GDR are produced for ESA by the French Processing and Archiving Centre. Concerning the geophysical corrections, it is important to note that the corresponding MWR measurements are used to correct for the wet tropospheric component, while the dry tropospheric component is corrected making use of the pressure field predicted or reconstructed by ECMWF (European Centre for Medium-Range Weather Forecast).

The scientific community involved in the research and development of applications of satellite altimetry in the coastal zone met for the 6th Coastal Altimetry Workshop. Research in coastal altimetry is going through exciting times: Experimental data sets for the coastal zone are now available, such as those from the French PISTACH project for the Jason-2 satellite and from the European Space Agency (ESA)–funded COASTALT project for the Envisat satellite, and many diverse applications were shown at the workshop. These range from observing coastal currents to monitoring river and lake levels, ice margins, and storm surges to gravity mapping in coastal zones.

[1] Fourth Coastal Altimetry Workshop; Porto, Portugal, 14–15 October 2010; Satellite altimetry is now a mature Earth observation technique with great impact in many areas of study, including ocean circulation, marine geoid, bathymetry, and assimilation into models, as well as sea level trends and their impacts on the understanding of global climate change. Until recently, the centimeter-level accuracy achieved by this technique had been limited to open ocean about 50 kilometers off the coast. Aiming to develop products for coastal zones with an accuracy similar to that achieved in the open ocean, several research projects in the past few years, such as Prototype Innovant de Systeme de Traitement pour les Applications Cotieres et l'Hydrologie (PISTACH), Development of Radar Altimetry Data Processing in the Coastal Zone (COASTALT), Centre de Topographie des Oceans et de l'Hydrosphere (CTOH), and more recently the European Space Agency Climate Change Initiative (ESA CCI), have been supported by various agencies. In parallel, a set of Coastal Altimetry Workshops was initiated (Silver Spring, Md., 2008; Pisa, Italy, 2008; Frascati, Italy, 2009). The Fourth Coastal Altimetry Workshop followed on this successful series of workshops and was hosted by the University of Porto, with support mainly from ESA and 10 other institutions and space agencies.

International audience; Altimetry missions in the last 16 years (TOPEX/Poseidon, ERS-1/2, GFO, Jason-1 and ENVISAT) and the recently-launched Jason-2 mission have resulted in great advances in deep ocean research and operational oceanography. However, oceanographic applications using satellite altimeter data have become very challenging over regions extending from near-shore to the continental shelf and slope (Cipollini et al. 2008). In these regions, intrinsic difficulties in the corrections (e.g., the high frequency ocean response to tidal and atmospheric loading, the mean sea level, etc.) and issues of land contamination in the radar altimeter and radiometer footprints result in systematic flagging and rejection of these data. Forthcoming altimeter missions (SARAL/AltiKa, SWOT, Sentinel-3, etc.) are designed to be better-suited for use in the coastal ocean. However, a number of studies have dealt with the problem of re-analysing, improving and exploiting the existing archive to monitor coastal dynamics. The early encouraging results (Vignudelli et al. 2005; Bouffard et al. 2008, Birol et al. submitted J Mar Syst 2009) support the need for continued research in coastal altimetry, with the opportunity of providing input and recommendations for future missions. This chapter reviews the current status of the X-TRACK processing application (Roblou et al. 2007), whose objectives are to improve both the quantity and quality of altimeter sea surface height (SSH) estimates in coastal regions by reprocessing a posteriori (the standard Geophysical Data Records) (GDR) as delivered by operational centres, i.e. by improving the post-processing stage. Latest improvements on along-track spatial resolution (high rate data streams and removal of large-scale errors) that promise improved monitoring of coastal dynamics are also detailed. In addition, with a view to integrating coastal-oriented altimeter datasets into models for coastal ocean state analysis, methodologies for matching models with observations are discussed.