This study analyses a long-time series (from January 2016 to December 2021) of optical and SAR signatures. The data were processed into two contexts: 1) over a station-monitored field called Auradé in southwestern France (part of the ICOS network) to examine the impact of surface states on satellite signals and, 2) over 939 fields of sunflower to investigate the impact of climatic conditions on crop development. Results show that backscatters can be used with moderate confidence to replace or gap fill NDVI ($r_{6-years}^{Aurad\acute{e}} = 0.73$ over the Auradé field and $r_{6 - years}^{sunflower} = 0.77$ over the 939 fields of sunflower). Nevertheless, additional analyses highlighted the effectiveness of combining γ0VH, γ0VH/VV with NDVI, GAI (Green Area Index), and Fcover indices for complementary monitoring of crop phenology. Superpositions of satellite signals observed on sunflowers suggest that it is possible to detect development anomalies using optical or radar signals based on yearly climatic conditions. Results also show the importance of considering both sunflower architecture (orientation, inter-row spacing, Fcover) and radar acquisition geometry (orbit pass, viewing angles), in contrast to denser or more covering crops. The joint use of the 2 SAR orbits enabled the detection of phenological stages (from flowering to harvest) not as well detectable with a single orbit. This long-time series analysis provided insights under multiple climatic conditions, including the hottest year in France since the beginning of meteorological records.

Lakes are considered as sentinels of climate change. Water stored in lake is an important component of the land water cycle. Earth Observations offer a unique tool to monitor lakes extent, water levels variations, and hence changes in water volume combing these two variables or one of them with lakes bathymetry. We present here, surface water volume variations combining lake extent from MODIS and water levels from radar altimetry over 01/1995-04/2024 of Lake Titicaca, the second largest lake in South America. Comparisons between water levels from in-situ gauge and from radar altimetry including the recently launched SWOT mission are presented.

Soil Moisture Ocean Salinity (SMOS) mission was the first L-band radiometer launched in 2010 and is operational for retrieving global scale soil moisture and Vegetation Optical Depth (VOD). SMOS-INRA-CESBIO (SMOS-IC) version-2 is the latest retrieval algorithm for SMOS radiometers that outperforms existing SMOS retrieval algorithms. Research is underway to enhance the SMOS-IC product by improving surface roughness information that influences soil moisture and VOD retrievals. In the present study, we developed a new global parameterization of soil roughness using SMOS-IC retrievals. For this purpose, we retrieved the soil moisture and surface roughness (through the Hr parameter) values over bare soils using the SMOS-IC algorithm. A Random Forest (RF) model was trained with soil textural and terrain properties as inputs (explanatory variables) to model Hr over bare soils. Later, we extrapolated the Hr values obtained over bare soils to a global scale using the RF model. The newly calibrated Hr values were further used in the SMOS-IC algorithm for soil moisture and VOD retrievals (SMOS-IC v2.1). The SMOS-IC v2.1, the soil moisture product, is wetter than the original SMOS-IC v2 product and has improved performance compared to in situ ISMN soil moisture and modeled ECMWF soil moisture data sets. Regarding VOD, the SMOS-IC v2.1 VOD product showed improved spatial correlation with the reference aboveground biomass product. In addition, SMOS-IC v2.1 VOD product showed improved temporal correlation with MODIS NDVI over low-to-moderate vegetated regions.

Analyzing the spatio-temporal dynamics of groundwaters is crucial for a better understanding of the global hydrological and biogeochemical cycles, especially with climate change and the intensification of human activities. However, monitoring groundwaters is still complicated due to the lack of in situ data and the weak spatial coverage of these sites. Remote sensing is a good opportunity to address this problem. The method to study water level maps of the groundwater table is based on the use of a dense network of virtual stations produced using radar altimetry and surface water extent obtained by multispectral imagery. The interpolation of these two datasets allows us to produce minimum water level maps associated with the groundwater lebel base at a spatial resolution of 500 m from 2000 to 2022. First results show a link between groundwater anomalies and cumulative rainfall.

River floodplains and intertidal coastal environments are key areas for which the Surface Water and Ocean Topography (SWOT) altimetry mission is expected to provide valuable data, improving our ability to monitor these dynamic systems. SWOT provides water surface extent and elevation for continental water surfaces globally and regularly. This article presents two methods to derive the topography of such environments from SWOT data. The first one, the waterline method, is the approach currently developed to produce worldwide floodplain/intertidal DEM. The second method uses a direct approach by exploiting InSAR heights extracted directly from SWOT products. An example is shown for the Bay of Veys located in Normandy (France).

Since 2016, land surfaces can be monitored from optical and synthetic aperture radar sensors onboard Sentinel-1 and Sentinel-2 satellites at high spatial and temporal resolutions. Monitoring agricultural surfaces through satellite-based estimations of biophysical parameters is a key issue for agriculture sustainability in the context of an increasing climate change. With its all-weather vision capability, it is important to develop methods based on the use of Sentinel-1 images. In this study, we applied an original algorithm of change detection to monitor the two summer crops (sunflower and sorghum) grown in France and other parts according to a near-real-time detection method called CuSum-NRT applied to Sentinel-1 time-series of images.

Between 2018 and 2022, INRAE Bordeaux (IB) has developed a series of soil moisture (SM) and L-band Vegetation Optical depth (L-VOD) retrieval products from SMOS and SMAP, which are currently the only two operational L-band passive microwave satellite missions. These IB products rely on a two-parameter inversion of the L-MEB model (L-band Microwave Emission of the Biosphere) which requires little ancillary information. The products are found to be accurate, and very well-suited for application in hydrology, agriculture, climate and vegetation monitoring. In this communication, we present an overview of the development, evaluation and new applications of these IB SM or L-VOD products.

Analyzing the dynamics of surface water extent and volume is crucial for a better understanding of the global hydrological and biochemical cycles, especially in a context of climate change. However, due to the scarcity of in situ data and their inhomogenous spatial coverage, their use is limited. Remote sensing provides a good opportunity to solve this problem. In this study, we quantify surface water extent (from multispectral imagery) and volume (combining multispectral imagery and radar altimetry) over the Inner Niger Delta (IND) during the 2000-2022 period. The IND, located in Mali, has a wet season (from August to December) where important floods occured. Time series of surface water extent and volume show seasonal variations with maximum values in November and minimal values in April-May. These long time series show important droughts in 2002, 2004, 2011 and 2017, and maximum flood in 2003. In comparison with the digital flooding model, surface water extent from this method show difference of -20%.

Recent applications of passive L-band observations from space are summarized for ocean, land surface and cryosphere applications. The main limitation of the measurements performed by the current generation of sensors is the spatial resolution. The need of a mission ensuring the continuation of L-band measurements from space with high spatial resolution (10-15 km) is discussed.

Tropical forests are vulnerable to deforestation. This phenomenon led to the development of a wide number of forest monitoring systems based on remotely sensed data. These systems face multiple issues in tropical areas, one of them being the need of a good forest / non-forest map to use as reference for the monitoring. Several of these maps are available online, most of them being based on optical remote sensing data, which is known to be subject to limitations in tropical areas due to high cloud cover. Sentinel-1 C-band Synthetic Aperture Radar (SAR) dense time series of images has already been used for forest / non-forest mapping through combination with X-band SAR images. In this study, a change detection algorithm was applied on timeseries of Sentinel-1 images in the Parà State, Brazil. The Cumulative Sum (CuSum) algorithm was used to assess all non-forest areas as the hypothesis found by preliminary results was that these areas were more varying in terms of backscatter coefficient than undisturbed forests. The validation was made by comparison with a forest / non-forest map derived from Global Forest Watch data. The algorithm detected 78.0 % of the non-forested areas in the study zone of 6,855 km² with a 0.785 F1-score value.

In this study, water level estimates from the Global Ecosystem Dynamics Investigation Lidar (GEDI) were validated against in situ gauge station records over Lake Geneva. The performances of the first and second releases (respectively V1 and V2) of the GEDI data products were compared. The influence of the following parameters were analyzed: (1) the signal-over-noise ratio (SNR), (2) the width of water surface peak within the waveform (gwidth), (3) the amplitude of the water surface peak within the waveform (A), (4) the viewing angle of GEDI (VA), and (5) the acquiring beam. The comparison between V1 and V2 elevations showed that V2, overall, provided elevations with a more constant bias and fewer deviations to in situ data than V1. In addition, by choosing GEDI shots with VA ≤ 3.5°, the unbiased RMSE (ubRMSE) of GEDI elevations was 27.1 cm with V2 (r=0.66) and 42.8 cm with V1 (r=0.34). Results also show that the accuracy of GEDI (ubRMSE) does not seem to depend on the beam number and GEDI acquisition dates for the most accurate GEDI acquisitions (VA≤ 3.5°). Regarding the bias, a higher value was observed with V2 but with lower variability (54 cm) in comparison to V1 (35 cm). Finally, the bias showed a slight dependence on beam GEDI number.

Surface water storage is an essential component of the hydrological cycle. Remote sensing offers valuable tools for monitoring both surface water extent from satellite images and water levels from radar altimetry. Combining both information, we were able to estimate the variations of surface water extent and storage in the Lower Mekong Basin from 2000 to 2020. Signatures of the extreme climatic events - floods from 2000 to 2002, of 2011, drought of 2015 clearly appear on both extent and storage. The mean amplitude of these variables shows a strong decrease when comparing the periods of 2000–2010 and 2011–2020. Between these two periods, a large reduction of the annual average number of days with the presence of floods can be observed in most of the Lower Mekong Basin, except around the Tonle Sap (Cambodia) and in some parts of the delta.

Lakes and reservoirs are considered sentinels of climate and anthropogenic changes. Lakes and reservoirs surface water storage is an essential hydrological variable but poorly known as this information is scarce. Earth Observation data are a reliable source of information to overcome this scarcity. Among these, the combined use of satellite images, to derive water extent, and radar altimetry, which enables to estimate water levels, provides valuable information on water storage changes. Here, we used Synthetic Aperture Radar images from Sentinel-1 and radar altimetry data from Sentinel-3 to monitor the water volume changes of Lake Bracciano from 2016 to 2021. This lake was affected by a water crisis in 2017 and the water supply to the city of Rome (Italy) was interrupted September 2017 to preserve its ecosystem. Hence, we demonstrate how Sentinel-1 and Sentinel-3 data can be useful to monitor water extent and level, which can be profoundly changed by the climate crisis.

This study estimates monthly variation of surface water volume of Thac Mo water reservoir, located in Binh Phuoc province, in South Vietnam, for the 2016–2021 period, using SAR Sentinel-1 observations and Jason-3 altimetry data. SAR Sentinel-1 observations have been pre-processed mainly using the Google Earth Engine cloud computing platform to estimate variation of surface water extent, while Jason-3 radar altimetry data has been processed using the AlTiS software to estimate variation of surface water level of the reservoir. Results show a very good agreement between variations of the reservoir's surface water volume derived from satellite data and that derived from in situ measurement, with a correlation of 95.4%.

Earth Observation data is often used for land cover classification or change monitoring. It is rarely used for both goals in a single algorithm. The multi-change Cumulative Sum (CuSum) algorithm proposed in this study allows both classification and change monitoring in a single algorithm using Sentinel-1 C-SAR time series. The multi-change CuSum approach allowed to classify pixels belonging to the fused non-forest vegetation and bare soil classes apart from the pixels belonging to new cuts. The distinction of each class is better made using the two polarizations: VV is more accurate for detecting non-forest vegetation (Kappa coefficient of 0.62) and VH for detecting new cuts (Kappa coefficient of 0.65). The algorithm showed an accuracy up to 0.82.

Interannual variability in biomass represented by SMOS vegetation optical depth (VOD) and precipitation was assessed over the Contiguous United States. The greatest interannual variability in both VOD and precipitation occurred in shrubs and herbaceous (grasslands), with forests the least variable. At a continental scale, VOD was strongly correlated with annual precipitation. Results showed a significant correlation coefficient (∼ 0.93) between interannual variability of precipitation and biomass, indicating that the interannual variability of precipitation could be a good predictor of the interannual variability of biomass.

This study presents an analysis of radar signature at Ku-band for incidences ranging from 0° to 10° over the major bioclimatic zones, soil and vegetation types encountered in West-Africa, using data from Jason-3 and SWIM. Time-series of radar responses were built over the following environments: stone and sand deserts, Sahelian savannah and floodplain, flooded and non-flooded equatorial forests. Deserts and non-flooded equatorial forest exhibit almost constant responses, decreasing as the incidence angle increases. Similar seasonal variations of the backscattering coefficient between the dry and the wet season are observed at nadir for Jason-3 and SWIM with a decrease in dry season level and amplitude with the increase of the incidence angle. Backscattering at Ku-band can be related to soil roughness, vegetation cover and soil wetness.

Radar altimetry is commonly used to derive water levels over inland water bodies. If lakes and rivers are increasingly covered with radar altimetry virtual stations, wetlands and floodplains are still poorly monitored using this technique. In this study, an unsupervised classification of Ku-band radar altimetry backscattering coefficients from ENVISAT and Jason-2 is performed in the Congo Cuvette Centrale. Comparisons performed against a classification map of the study area shows a good agreement between the water and vegetation classes of the two datasets. Based on these results, radar altimetry-derived water levels are automatically derived over the water classes. Comparisons against radar altimetry-based water stages from Hydroweb database exhibits also a good agreement.

Passive microwave remote sensing observations at L-band provide key and global information on surface soil moisture (SM) and vegetation optical depth (VOD), which are related to the Earth water and carbon cycles. Only two spaceborne L-band sensors are currently operating: SMOS, launched end of 2009 and thus providing now a 11-year global dataset and SMAP, launched beginning of 2015. To ensure SM and L-VOD data continuity in the event of failure of one of the space-borne SMOS or SMAP sensors, we developed a consistent brightness temperature (TB) record by first producing consistent 40° SMOS and SMAP TB estimates based on SMOS-IC and SMAP enhanced data resp., and then fusing them via linear fusion method. We found that SMOS and SMAP TB are strongly correlated (R > 0.90 over most of the globe) but present a small bias at both the horizontal and vertical polarizations. The preliminary evaluation results show that this bias can be adjusted using a linear fit, but further evaluation procedures are still needed. In the near future, we will develop a long-term time series of SM and L-VOD products based on this merged SMOS-SMAP TB record.

Global and long-term vegetation optical depth (VOD) dataset are very useful to monitor the dynamics of the vegetation features, climate and environmental changes. In this study, the radar-based global ASCAT (Advanced SCATterometer) IB (INRAE-BORDEAUX) VOD was retrieved using a model which was recently calibrated over Africa. In order to assess the performance of IB VOD, the Saatchi biomass and three other VOD datasets (ASCAT V16, AMSR2 LPRM V5 and VODCA LPRM V6) derived from C-band observations were used in the comparison. The preliminary results show that IB VOD has a promising ability to predict biomass$(\mathrm{R}=0.74,\ \text{RMSE} =44.82\ \text{Mg}\ \text{ha}^{-1})$, which is better than V16 VOD$(\mathrm{R}=0.64,\ \text{RMSE} =51.27\ \text{Mg} \text{ha}^{-1})$and VODCA VOD$(\mathrm{R}=0.72,\ \text{RMSE} =47.14\ \text{Mg}\ \text{ha}^{-1})$. Some retrieval issues for IB VOD were found in boreal regions (e.g., Eastern America, Russia). In the future, we will focus on improving our algorithm in those regions, and produce a global and long-term dataset.

Assessment of the model-based surface soil temperature (ST) products is very important for hydrometeorological and ecological applications, as well as model refinements. Distinguished from previous regional validations using only in situ observations from sparse networks, this study focused on the evaluation of model-based ST products by considering ground observations from 15 dense networks worldwide from April 2015 to December 2017 covering a wide range of ground conditions. Four model-based ST products were selected for the assessment, including the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), the Goddard Earth Observing System Model version 5 Forward Processing (GEOS-5 FP), the ERA-Interim and its successor, the newly developed ERA5. The results indicate the GEOS-5 ST product slightly outperforms other ST products by showing an averaged ubRMSD of 1.84 K. All model-based ST products underestimate in situ ST with a negative bias. All four model-based ST products are demonstrated to well capture the temporal trends of ground observations with very promising$R$values larger than 0.97. The ERA5 shows visible improvements compared to its predecessor ERA-Interim by exhibiting smaller ubRMSD, absolute bias and larger$R$values. These findings are expected to provide useful suggestions for the enhancement and specific usage of the model-based ST products.

The aim of this study is to investigate the performance of SWOT in generating intertidal Digital Elevation Models (DEMs) using the waterline method. To this end, tests were made by generating intertidal DEMs from SWOT-type observables for two intertidal bays situated on the French coast: the Arcachon Bay and the Bay of Veys. SWOT-type observables were simulated using the SWOT Hydrology Toolbox developed by the French space agency (Centre National d'Études Spatiales (CNES)). By comparing SWOT-derived DEMs to the validation DEMs, SWOT showed a great potential for monitoring intertidal topographies. Mean Absolute Errors (MAEs) and Root Mean Square Errors (RMSEs) reached respectively 5.2 cm and 8.4 cm for the Arcachon Bay and 10.2 cm and 17.3 cm for the Bay of Veys. However, the accuracy of SWOT-derived DEMs is dependent on SWOT's orbit.

There is an increasing need for measuring the topography of intertidal environments in a regular manner. Spaceborne monitoring of intertidal environments is essentially performed using the waterline method. In this study we propose a new automatic approach for using the waterline method to derive intertidal Digital Elevation Models (DEMs). The changes include a faster, more efficient, and quasi-automatic detection and post-processing of waterlines. Using Sentinel-1 and Sentinel-2 images, DEMs were generated by this new waterline method approach for the Arcachon Bay and the Bay of Veys located both on the French Coast. The comparison of the generated DEMs with lidar observations showed an error of about 19–29 cm.

Sea ice concentration, sea ice thickness, and snow depth over sea ice are important physical parameters for modeling the cryosphere. We present here preliminary results of a method to combine passive radiometer, altimeter, and scatterometer, and to evidence their synergy. The benefit of merging the data from several instruments at level 1 is emphasized, through the use of a classification methodology. This work is performed in the framework of the preparation of the future Copernicus missions CIMR and CRISTAL, that will fly at the same time as ASCAT on board MetOp-SG.

Vegetation Optical Depth (VOD) plays an increasingly important role in studying global carbon, water and energy transformation [1], [2]. This study explores the performance of the X-MEB (X-band microwave emission of the biosphere) model at global scale. Similar to the L-MEB model, the X-MEB model, built by INRAE (Institut national de recherche pour l'agriculture, l'alimentation et l'environnement) Bordeaux, aims to retrieve VOD (referred to as IB X-VOD) at X-band. To avoid the ill-posed problem caused by retrieving two parameters of interest (soil moisture (SM) and VOD) from mono-angular and dual-polarized observations (AMSR2), which are strongly correlated, we used the ERA5 SM product as an input to the X-MEB inversion. At a first step, we produced global IB X-VOD in year 2015 using the parameters (soil roughness and effective scattering albedo) calibrated in the African continent and evaluated the retrieved X-VOD with three vegetation parameters including Above-Ground Biomass (AGB), Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI). The evaluation results indicate X-MEB model has a great potential for global VOD retrievals from AMSR2 satellite data.

Vegetation optical depth (VOD) is used to parameterize microwave extinction effects within the vegetation layer. Many studies have showed VOD presents interesting features for applications in ecology, water and carbon cycles, and VOD is only marginally impacted by signal disturbances and artefacts from atmospheric, cloud and sun illumination effects. As soil moisture (and not VOD) has generally been the main factor of interest in retrieval studies from microwave observations, there is room for improvement in the retrieved VOD products. In this context, INRAE Bordeaux recently developed alternate VOD products from the SMOS, AMSR2 and ASCAT sensors, by addressing specifically the ill-posed problem of retrieving both SM and VOD from observations which may be strongly cross-correlated. Promising results were obtained particularly in terms of spatial correlation of these alternate VOD indices with biomass.

Tropical forests are vulnerable to deforestation and various monitoring techniques have been developed based on remotely sensed data to map deforestation, but are facing multiple problems in the tropical areas. For instance, the techniques based optical data, which are widely used to monitor deforestation, face severe limitations in the humid tropical forest due to high cloud cover. Sentinel-l C-SAR dense time series can be used for a temporally more accurate monitoring. In this study, a change detection algorithm commonly used in the financial domain, the Cumulative Sum (CuSum) algorithm, was modified to be applied on time-series of Sentinel-l images in a forest concession of Democratic Republic of Congo (DRC) near Kisangani. The validation was made through the visual interpretation of PlanetScope OrthoScene images as in-situ data were missing. The results show a precision up to 0.75, an accuracy up to 0.95 and a kappa coefficient up to 0.40 for clear cut detection. The algorithm is able to detect forest degradation activities before the clear cuts.

Signal to Noise Ratio (SNR) time series acquired by a single Global Navigation System Satellites (GNSS) antenna are used to retrieve water levels during asymmetric tides and tidal bores in the Garonne River. As height variations are important, a dynamic SNR approach was applied, instead of the static case, to estimate the relative antenna height from the dominant frequencies. Evaluation of the results is performed against pressure measurements. Results on asymmetric tides are quite good overall$\text{(ubRMSD}=0.55\mathrm{m}$), and way better in the falling tide$(\text{ubRMSO}=0.12\mathrm{m}$), where the vertical velocity of the reflecting surface$h$. is lower than in the rising part$\text{(ubRMSD}=0.59\mathrm{m}$). The dynamic SNR method is limited for high h values as it is not able to extract the correct frequencies. For this reason, extremely rapid variations of the river surface during the tidal bore could not be retrieved.

The Geoscience Laser Altimeter System onboard the NASA Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) provided elevation measurements of Earth's surface between 2003 and 2009. The centroid and maximum-amplitude-peak (MAP) retracking methods have been designed and applied to process the returned laser waveforms for elevation measurements. Although these two methods work well in general, they may generate erroneous measurements when the returned waveform was complicated by adverse atmospheric conditions (clouds, ice fogs, blowing snow, and dust storms). The centroid retracking method is often more severely affected when compared with the MAP retracking method. In this study, we present a new retracking method that exploits the spatial contextual information from neighboring footprints along the satellite ground track, in addition to the single return waveform shape information. Our method uses a probabilistic relaxation (PR) algorithm to integrate the spatial contextual information and the waveform shape information to identify the waveform peak that most likely represents the true surface elevation, rather than simply detecting the peak with the maximum magnitude. For different types of land surfaces, such as inland lakes, polar tundra, ice sheet, and sand deserts, we demonstrate that our new PR retracking method is able to produce more reliable, consistent, and accurate elevation measurements than the standard NASA ICESat/GLAS data products. The root mean squares error (RMSE) is reduced from 0.85 to 0.17 m for inland lake, from 0.81 to 0.23 m for polar tundra, from 1.25 to 0.33 m for ice sheet, and from 2.48 to 2.34 m for sand desert.

Since the first version of the SMOS-IC retrieval product was released in 2017, its soil moisture (SM) and L-band Vegetation Optical depth (VOD) retrievals have proven to be a very interesting alternative product for the SMOS mission. This product relies on a two-parameter inversion of the L-MEB model (L-band Microwave Emission of the Biosphere) which is independent of auxiliary data, a key feature making it well-suited for application in hydrology, agriculture, climate, and carbon cycle. This paper describes the development and validation of the most recent SMOS-IC version (V2) soil moisture product. Compared with the previous version (V105), a new constraint was applied on VOD in the cost function which is minimized in the retrieval process. Soil moisture retrievals from SMOS-IC V2 & V105 were inter-compared against the “European Centre for Medium-Range Weather Forecasts” (ECMWF) modelled SM and the “International Soil Moisture Network” (ISMN) in-situ measurements during 2011-2017 over France. It was found that the average retrieval uncertainty of the new version product was lower than that of the old version, particularly when vegetation density increased. The new version of the SMOS-IC soil moisture product will be made available to the public through the CATDS (Centre Aval de Traitements des Données SMOS) website.

Vegetation Optical Depth (VOD) plays an important role in monitoring the earth ecosystems. There are many VOD products released based on different satellites and frequencies. But most of the VOD products are derived from passive microwave data, and very few active VOD products have been released to date. This study investigated retrievals of the active microwave VOD product from C-band ASCAT (Advanced SCATterometer) observations using the water cloud model in large areas. To achieve this, the ASCAT backscatter data and ECMWF soil moisture data were used as inputs to retrieve ASCAT VOD over the whole Africa. The correlation between the retrieved VOD product and proxies of vegetation density (Saatchi biomass) were used to evaluate the model performance.

Decade years of efforts on the retrieval of soil moisture based on radiative transfer model have largely improved the accuracy of soil moisture (SM). This paper focus on the other parameter, namely vegetation optical depth (VOD). We retrieved X-band VOD from AMSR-E and AMSR2 observations by inverting the L-MEB model (Wigneron et al. 2007 [1]) at X-band, considering that SM was known. As SM input to the L-MEB inversion we used the ECMWF SM product. This step avoids correlation between VOD and SM retrievals from the mono-angular AMSR-E observations. In a first step we evaluated the retrieved VOD with the Copernicus Global Land Service (CGLS) LAI. The evaluation results indicate our model has a great potential for VOD retrievals from AMSR-E/2 satellite data.

Radar altimetry backscattering coefficients can be related to the dynamics of surface properties over land and ice sheets. Temporal variations of the backscattering coefficients from Jason-3 and Sentinel-3A missions were analyzed over land surfaces. Comparisons at crossover points are presented to show the differences in response due to changes in incidence and acquisition modes.

Surface Soil Moisture (SSM) is a key parameter of water and energy balances in semi-arid areas. SSM is linearly related to the radar backscattering coefficients (σ0) over sand. Linear relationships are commonly used for inverting SSM in semi-arid areas from SAR and scatterometer data. Recent studies demonstrated that SSM can also be inversed from radar altimetry backscattering. An inversion method combining radar altimetry σ0and land surface model (LSM) outputs is proposed here.

Radar altimetry, initially developed to observe the surface topography of the ocean, is now commonly used for the monitoring of inland water bodies. In this study, an altimetry-based gauging network was built in the Ogooué River Basin (ORB), in Gabon, Central Africa. It is composed of altimetry-based virtual stations (VS) under the altimetry tracks of ERS-2/ENVISAT/ SARAL, Cryosat-2, Sentinel-3A and Jason-2/Jason-3. River discharge was estimated from 1995 to 2017 combining water level records from several virtual stations close to Lambaréné gauge station and the rating curve from this station. Very good agreement was found in terms of R (>0.95), RMSE and annual discharge.

Satellite radar altimetry was initially designed to measure the marine geoid. Thanks to the improvement in the orbit determination from the meter to the centimeter level, this technique has been providing accurate measurements of the sea surface topography over the open ocean since the launch of Topex/Poseidon in 1992. In spite of a decrease in the performance over land and coastal areas, it is now commonly used over these surfaces. This study analyzes the ability of satellite radar altimetry to retrieve for the very first time the topography of the intertidal zone in coastal lagoons.

This study presents the potential of GNSS Reflectometry (GNSS-R) for the monitoring of water levels in the Mekong river delta (Vietnam). A geodetic GNSS station was installed at the Tran De port, Soc Trang province, over 23-30 August 2016. The method used to retrieve water levels from GNSS-R data is based on the Interference Pattern Technic of the Signal-to-Noise Ratio (SNR). To validate our measurements, we used different radar altimetry data from Jason-2, SARAL, Jason-3 and Sentinel-3A missions during the same period for measuring sea surface height (SSH) and in-situ tide gauge station as reference for both tides and floods. The aim of this study is to evaluate the performances GNSS-R, in complex environment, for monitoring at the same time river/tide stages and the possibility to use this technique for calibration/validation of altimetry data. Early results show the good agreement between in -situ gauge and GNSS-R based estimates with correlations which reach ~ 0.85.

Altimetry-derived sea surface heights (SSH) from the recently launched Jason-3 and Sentinel-3A missions were validated against records from seven tide gauges located along the French Atlantic coast in the southern Bay of Biscay. Comparisons between Jason-2 and Jason-3 based SSH were performed along-track when they were orbiting 80 seconds apart during the tandem mission that lasted more than six months. The benefits of the Synthetic Aperture Radar (SAR) acquisition mode on Sentinel-3A is compared to the classical Low Resolution Mode (LRM) on Jason-2 and 3 missions are analyzed.

In this letter, Global Navigation Satellite System Reflectometry (GNSS-R) delay Doppler maps (DDMs) acquired by the U.K. TechDemoSat-1 (TDS-1) mission are analyzed to identify sea ice infesting waters in circumpolar areas. First, the sensitivity of actual DDM to sea ice is investigated using reference information provided by a Sentinel-1 synthetic aperture radar imagery and a Special Sensor Microwave/Imager based sea-ice concentration map. Then, a new product is proposed that consists of normalized radar cross section measurements derived from actual DDMs and arranged in gridded format. This product is shown to provide spatial information on the sea ice transition that cannot be obtained exploiting the conventional DDM product. Hence, this new product can be used to improve/complement conventional remote sensing techniques for sea ice observation.

Global Navigation Satellite Systems - Reflectometry (GNSSR) is an innovative and promising tool for remote sensing purposes. Several applications have been developed to extract geophysical information of the reflected scene from the measured delay waveforms and Delay-Doppler Maps (DDMs). Recently, techniques have been presented to deconvolve the DDMs to reconstruct the Normalize Radar Cross Section (NRCS) field of the observed area in order to provide added value products for imaging remote sensing. In this paper an effective reconstruction scheme, based on the 2-D Truncated Singular Value Decomposition (TSVD), is proposed and succesfully tested on actual Delay Doppler Maps (DDMs) acquired by TechDemoSat-1 (TDS-1) mission.

The behavior of the Ka-band backscattering coefficient at nadir and close-to-nadir angles for land applications is poorly documented. The measurements made during a ground-based campaign at Ka-band were performed at nadir and close-to-nadir angles over bare soils for different surface roughness and soil moisture conditions. The resulting backscattering levels exhibited a dynamic range of approximately 23 dB at nadir for soil moisture contents between 5% and 50% m3/m3over both smooth and rough surfaces. These results were then compared to the geometric optics (GO) and millimeter microwave (MMW) models. Generally, GO finely fits the backscattering coefficients close to nadir, and MMW appeared to fit for larger incidence angles or rough surfaces. The results obtained in this letter can address prelaunch science and engineering considerations for the interferometry-altimetry Surface Water and Ocean Topography mission operating at Ka-band.

The floods are an annual phenomenon on the Pacific coast of Ecuador and can become devastating during El Niño years, especially in the Guayas watershed (32,300 km2), the largest drainage basin on the South American western side of the Andes. In this study, we used ENVISAT ASAR GM SAR images with a spatial resolution of 1 km to map the flooded areas between 2004 and 2008 and study the spatio-temporal dynamics of floods in the Guayas Basin. Maximum of likelihood supervised classification performed on ASAR images acquired during four consecutive dry seasons allowed us to identify five classes of land cover consistent with land use map. From the four wet seasons, we computed standardized anomalies of backscattering coefficient to detect changes between dry and wet season and tested different thresholds to identify flooded areas.

Multipaths are one of the major source of error for positioning applications using Global Navigation Satellite Systems (GNSS). During the last decade, several studies demonstrated the potential of the inversion of Signal to Noise Ratio (SNR) due to multipaths for estimating environmental parameters such as sea level, soil moisture, and snow depth variations, vegetation growth and biomass quantification. Here, we propose i) a modeling approach contribution of the multipath signals to the SNR, ii) an inversion technique of the receiver height based on the modeled SNR.

As multipaths still represent a major problem for reaching precise GNSS positioning, the mitigation of their influence has been widely investigated. However, previous studies have lately proposed to use these interferences of GNSS electromagnetic waves to estimate parameters related to the reflecting surface (e.g., antenna heights, rugosity,...). Variations of the nature of the surface is likely to modify the properties of the reflected waves, and consequently lead to variations of amplitude / phase of the signal-to-noise ratio (SNR), e.g. recorded at 1 Hz by a GNSS receiver. By analyzing the time variations of SNR measurements linked to the dielectric constant of the surrounding soil, we use a method to recover the local fluctuations of the soil moisture content. It is simply based on the obvious linear correlation between SNR amplitude / phase and retrieved antenna height time series and independent measurements of humidity probe at 2 and 5 cm depths. This method of combination is applied to determine soil moisture in a corn and soya field at Lamasquère, France, for 21 successive days. Results show a good correlation (e.g. 0.96 with GPS PRN-01 satellite) between SNR inversion and humidity probes for most satellites.

Ocean altimetric applications of GNSS-R have particularly been developed through the last decades. Interference Pattern Technique (IPT) based on the analysis of the Signal-to-Noise Ratio (SNR) of a classical GNSS antenna presents the main advantage of being applicable everywhere by using a single geodetic antenna and a classical GNSS receiver, transforming them to real wave/tide gauges. Such a technique has been already tested in various configurations of acquisition of surface-reflected GNSS signals with an accuracy of a few centimeters. Nevertheless, the traditional method for estimating the reflecting surface-antenna height is limited by an approximation: the vertical velocity of the reflecting surface must be negligible. This article presents a significant improvement of the IPT technique to solve this problem and broaden the scope of SNR-based tide monitoring. This method was validated in situ, with an antenna placed at 60 meters above the Atlantic Ocean surface which variations reach ±3 meters, and amplitude rate of the semi-diurnal tide up to 0.5 mm/s. Over the three months of SNR records for sea level determination, we found linear correlations of 0.94 by comparing with a traditional tide gauge. Our SNR-based time series was also compared to the TUGO tide theoretical model [1] and amplitudes and phases of the main astronomical periods (6-, 12- and 24-h) were perfectly well detected.

This study presents three complementary approaches to determine the volume of water in small lakes (2altimetry=0.98, RMSEaltimetry=5.0%, R2imagery=0.90, and RMSEimagery=7.4%). The third method combines altimetry (to measure the lake level) and satellite images (of the lake surface) to estimate the volume changes of the lake and produces the best results (R2=0.98) of the three methods, demonstrating the potential of future Sentinel and SWOT missions to monitor small lakes and reservoirs for agricultural and irrigation applications.

This study presents a comprehensive comparison of radar signatures acquired over West-Africa at both Ku- and C- bands using nadir-looking altimeters and side-looking scatterometers that covers the major bioclimatic zones, soil and vegetation types encountered in this region. The backscattering coefficients time series measured by both radar instruments are generated over regional transects and local sites. Meridian transects from the wet tropics to the arid regions of the Sahara desert point out the spatial and temporal changes of the radar response over the West-African bioclimatic gradient in terms of surface roughness, land cover and soil wetness. Nadir looking altimetry appears more sensitive to variations of surface soil moisture than side-looking scatterometry at both C- and Ku-bands. In contrast, the impact of vegetation on the backscattered signals is found to be lower for nadir-looking instruments compared to side-looking ones.

This study presents the preliminary results of the 2013 Ibiza calibration campaign of Jason-2 and Saral altimeters. It took place from 14 to 16 September 2013 and was composed of two phases: the calibration of the GNSS buoys to estimate the antenna height of each of them and absolute calibration to estimate the altimeter bias (i.e., the difference of sea level measured by radar altimetry and GNSS). The first one was achieved in the Ibiza harbor at a close vicinity of the Ibiza tide gauge and the second one was at ∼ 40 km at the northwest of Ibiza Island at a crossover point of Jason-2 and Saral nominal groundtracks. Five buoys were used to delineate the crossover region and their measurements interpolated at the exact location of each overflight. The overflights occurred two consecutive days: 15 and 16 September 2013 for Jason-2 and Saral respectively. Absolute biases of 0.1 and −4 cm were found for Jason-2 and Saral respectively.

This paper analyses the potential of the radar altimeter onboard ENVISAT for estimating surface soil moisture in the semi-arid Gourma region in Northern Mali. To this end, the relationships between observed backscattering coefficients derived from 4 retracking algorithms, and ground data are investigated. The considered period is 2002-2010. Results show a strong linear relationship between the backscattering coefficients and surface soil moisture measured at six different stations along the satellite track. The best results are obtained with the Ice-1 and Ice-2 algorithms. Vegetation effects are found to be small due both to the nadir-looking configuration of the radar altimeter and to the low vegetation cover. Finally, the relationship between soil moisture and normalized backscattering coefficient is used to retrieve soil moisture from the altimeter data. These results highlight the high capabilities of Ku-band altimeters to provide an accurate estimation of surface soil moisture in semiarid regions.

Independent component analysis (ICA) is a blind separation method based on simple assumptions of the independence of sources and the non-Gaussianity of observations. An approach based on ICA is used here to extract hydrological signals over land and oceans from the polluting striping noise due to orbit repetitiveness and present in the gravity anomalies detected by the Gravity Recovery and Climate Experiment (GRACE) satellites. We took advantage of the availability of monthly level-2 GRACE solutions from three official providers (i.e., CSR, JPL, and GFZ) that can be considered as different observations of the same phenomenon. The efficiency of the methodology is demonstrated on a synthetic case. Applied to one month of GRACE solutions, it allows for clearly separating the total water storage change from the meridional-oriented spurious gravity signals on the continents but not on the oceans. This technique gives results equivalent to the destriping method for continental water storage.