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Across the oceans shorelines, monitoring the topography of the intertidal zone is generally challenging. The present study is motivated by the recognized role of the intertidal topography in the near-shore hydrodynamics. We consider the region of Chittagong (northern Bay of Bengal) because of its propensity to powerful cyclone surges and associated inundation hazard. So as to curb the lack of in situ knowledge of intertidal topography, we present an original procedure relying on spaceborne optical imagery. Our method essentially amounts to a water line detection performed at various tidal levels. We apply our procedure to the recent PROBA-V (Project for On-Board Autonomy-Vegetation) multi-spectral imagery mission. The first step of our procedure concerns the shoreline extraction. PROBA-V imagery consists of four bands (Red, Blue, near-infrared - NIR, short-wave infrared - SWIR), which are then combined to generate an artificial red-green-blue (RGB) image. This RGB image is then converted into the hue-saturation-value (HSV) colour space. A simple thresholding is applied to hue and value channels to separate water masses from land masses. This process is applied to several images taken at different water levels (i.e. different parts of the tidal cycle) and the corresponding water lines are inferred. To estimate the altitude level of the water lines, we rely on tidal observations from two gauges located at Chittagong and Cox's Bazar. We operate an ad-hoc extrapolation of the point-wise gauge data to generate a synthetic tidal water level record all along the shoreline. These synthetic tidal heights are then combined with the shorelines to generate the final digital elevation model (DEM). The DEM we generated covers a 40-km long stretch of shoreline around Chittagong city. We assessed this DEM by comparison with two independent data sets based on in situ surveys as well as on Pléiades spaceborne stereoscopy. We conclude that our DEM is accurate within 1 m to 2 m, which is within the error bar of these validation data sets. Our procedure being essentially objective, it is easy to automate, for processing of other imagery satellite, including at high resolution and/or in real time. [ABSTRACT FROM AUTHOR]

Over the past decade, sea-ice freeboard has been monitored with various satellite altimetric missions with the aim of producing long-term time series of ice thickness. While recent studies have demonstrated the capacity of the CryoSat-2 mission (2010-present) to provide accurate freeboard measurements, the current estimates obtained with the Envisat mission (2002-2012) still require some large improvements. In this study, we first estimate Envisat and CryoSat-2 radar freeboard by using the exact same processing algorithms. We then analyse the freeboard difference between the two estimates over the common winter periods (November 2010-April 2011 and November 2011-March 2012). The analysis of along-track data and gridded radar freeboard in conjunction with Envisat pulse-peakiness (PP) maps suggests that the discrepancy between the two sensors is related to the surface properties of sea-ice floes and to the use of a threshold retracker. Based on the relation between the Envisat pulse peakiness and the radar freeboard difference between Envisat and CryoSat-2, we produce a monthly CryoSat-2-like version of Envisat freeboard. The improved Envisat data set freeboard displays a similar spatial distribution to CryoSat-2 (RMSD D1.5 cm) during the two ice growth seasons and for all months of the period of study. The comparison of the altimetric data sets with in situ ice draught measurements during the common flight period shows that the improved Envisat data set (RMSED12-28 cm) is as accurate as CryoSat-2 (RMSED15-21 cm) and much more accurate than the uncorrected Envisat data set (RMSED178-179 cm). The comparison of the improved Envisat radar freeboard data set is then extended to the rest of the Envisat mission to demonstrate the validity of PP correction from the calibration period. The good agreement between the improved Envisat data set and the in situ ice draught data set (RMSED13-32 cm) demonstrates the potential of the PP correction to produce accurate freeboard estimates over the entire Envisat mission lifetime. [ABSTRACT FROM AUTHOR]

Glaciers are one of the terrestrial essential climate variables (ECVs) as they respond very sensitively to climate change. A key driver of their response is the glacier surface mass balance that is typically derived from field measurements. It deserves to be quantified over long time scales to better understand the accumulation and ablation processes at the glacier surface and their relationships with inter-annual changes in meteorological conditions and long-term climate changes. Glaciers with in situ monitoring of surface mass balance are scarce at the global scale, and satellite remote sensing provides a powerful tool to increase the number of monitored glaciers. In this study, we present a review of three optical remote sensing methods developed to quantify seasonal and annual glacier surface mass balances. These methodologies rely on the multitemporal monitoring of the end-of-summer snow line for the equilibrium-line altitude (ELA) method, the annual cycle of glacier surface albedo for the albedo method and the mapping of the regional snow cover at the seasonal scale for the snow-map method. Together with a presentation of each method, an application is illustrated. The ELA method shows promising results to quantify annual surface mass balance and to reconstruct multi-decadal time series. The other two methods currently need a calibration on the basis of existing in situ data; however, a generalization of these methods (without calibration) could be achieved. The two latter methods show satisfying results at the annual and seasonal scales, particularly for the summer surface mass balance in the case of the albedo method and for the winter surface mass balance in the case of the snow-map method. The limits of each method (e.g., cloud coverage, debris-covered glaciers, monsoon-regime and cold glaciers), their complementarities and the future challenges (e.g., automating of the satellite images processing, generalization of the methods needing calibration) are also discussed. [ABSTRACT FROM AUTHOR]

We explore a new method to retrieve seasonal glacier mass balances (MBs) from low-resolution optical remote sensing. We derive annual winter and summer snow maps of the Alps during 1998–2014 using SPOT/VEGETATION 1 km resolution imagery. We combine these seasonal snow maps with a DEM to calculate a ‘mean regional’ altitude of snow (Z) in a region surrounding a glacier. Then, we compare the interannual variation of Z with the observed winter/summer glacier MB for 55 Alpine glaciers over 1998–2008, our calibration period. We find strong linear relationships in winter (mean R2 = 0.84) and small errors for the reconstructed winter MB (mean RMSE = 158 mm (w.e.) a−1). This is lower than errors generally assumed for the glaciological MB measurements (200–400 mm w.e. a−1). Results for summer MB are also satisfying (mean R2 and RMSE, respectively, 0.74 and 314 mm w.e. a−1). Comparison with observed seasonal MB available over 2009–2014 (our evaluation period) for 19 glaciers in winter and 13 in summer shows good agreement in winter (RMSE = 405 mm w.e. a−1) and slightly larger errors in summer (RMSE = 561 mm w.e. a−1). These results indicate that our approach might be valuable for remotely determining the seasonal MB of glaciers over large regions. [ABSTRACT FROM PUBLISHER]

Complementary analysis of satellite mission data (altimetry, Moderate Resolution Imaging Spectroradiometers (MODIS)) and climate fields over 2000–2009 was conducted to investigate the variability of the water cover surfaces and the geographical source of water inflowing into the Lake Poopó system. The results suggest that over the time span of 2000–2009 a great part of the variability of the Poopó system originates from geographic sources other than Lake Titicaca. Possible alternative causes include climate change inducing increased temperatures and greater evaporation rates along the Altiplano; increased glacier and snow melting over the Andes Cordillera; and a potential increased anthropogenic water use (such as irrigation, minery, etc.) throughout the path of the Desaguadero River (which connects lakes Titicaca and Poopó). This change in the hydrology of this region could lead to a collapse of water supplies and endanger the already fragile Poopó system as well as the regional socioeconomic system, which closely depends upon it. [ABSTRACT FROM AUTHOR]

The objective of this study is to develop an approach for monitoring land use over the semi-arid Tensift–Marrakech plain, a 3000 km2 intensively cropped area in Morocco. In this objective, the linear unmixing method is adapted to process a 6-year archive of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) 16-day composite data at 250 m spatial resolution. The result of the processing is a description of land use in terms of fractions of three predominant classes: orchard, non-cultivated area and annual crop. The typical signatures of land classes – endmembers – are retrieved on a yearly basis using an automated algorithm that detects the most pure pixels in the study area. The algorithm first extracts typical NDVI profiles as potential endmembers, then selects the profiles that have the best ability to reproduce the variability of MODIS NDVI time series over the study area. The endmembers appear stable over the 6 years of study and coherent with the vegetation seasonality of the three targeted land classes. Validation data allow us to quantify the error on land-use fractions to about 0.10 at 1 km resolution. Land-use estimates are consistent in space and time: the orchard class is stable, and differences in water availability (irrigation and rainfall) partly explain a part of the inter-annual variations observed for the annual crop class. The advantages and drawbacks of the approach are discussed. [ABSTRACT FROM AUTHOR]

Time series of remotely sensed imagery have proven to be a valuable tool to study the dynamics of the Earth surface. Some important features have been put in evidence in time series of greenness (measured as Normalized Difference Vegetation Index (NDVI)) fields at global and continental scales, and linked to changes in atmospheric variables and/or direct human impact. Analysing the same kind of information at regional scales is more challenging, as the increased detail implies increased complexity. We present a case of study of visualization coupled to statistical analysis based on Empirical Orthogonal Functions (EOF) of the seasonal pattern of greenness in SW Europe (52° N, 11° W to 30° N, 5° E) between 1999 and 2003. Our goal was to determine whether trends could be identified in the inter-annual change of some regions. We have found that for most of the area of study, inter-annual change in seasonal pattern of greenness consisted of an oscillation along a linear trajectory on the plane defined by the first two EO components. Nevertheless, a few scattered sites and two larger regions show trends, as both the direction and the sense of the inter-annual changes remained consistent from year to year during the period of study. Considering the dependency of the seasonal course of greenness on annual patterns of temperature and precipitation, the extent covered by these areas and the diversity of land cover that they include strongly suggest a response to changes in the seasonal patterns of key atmospheric variables for the same period. While the period of study is short, the particular response of these regions deserves further attention, including the analysis of longer series of NDVI and other satellite products, as well as the exploration of the actual relationships to atmospheric variables. [ABSTRACT FROM AUTHOR]

Remote sensing is a helpful tool for crop monitoring or vegetation-growth estimation at a country or regional scale. However, satellite images generally have to cope with a compromise between the time frequency of observations and their resolution (i.e. pixel size). When concerned with high temporal resolution, we have to work with information on the basis of kilometric pixels, named mixed pixels, that represent aggregated responses of multiple land cover. Disaggreggation or unmixing is then necessary to downscale from the square kilometer to the local dynamic of each theme (crop, wood, meadows, etc.). Assuming the land use is known, that is to say the proportion of each theme within each mixed pixel, we propose to address the downscaling issue through the generalization of varying-time regression models for longitudinal data and/or functional data by introducing random individual effects. The estimators are built by expanding the mixed pixels trajectories with B-splines functions and maximizing the log-likelihood with a backfitting-ECME algorithm. A BLUP formula allows then to get the 'best possible' estimations of the local temporal responses of each crop when observing mixed pixels trajectories. We show that this model has many potential applications in remote sensing, and an interesting one consists of coupling high and low spatial resolution images in order to perform temporal interpolation of high spatial resolution images (20 m), increasing the knowledge on particular crops in very precise locations. The unmixing and temporal high-resolution interpolation approaches are illustrated on remote-sensing data obtained on the South-Western France during the year 2002. [ABSTRACT FROM AUTHOR]

Climatological changes occur globally but have local impacts. Increased storminess, sea level rise and more powerful waves are expected to batter the coastal zone more often and more intense. To understand climate change impacts, regional bathymetry information is paramount. A major issue is that the bathymetries are often non-existent or if they do exist, outdated. This sparsity can be overcome by space-borne satellite techniques to derive bathymetry. Sentinel-2 optical imagery is collected continuously and has a revisit-time around a few days depending on the orbital-position around the world. In this work, Sentinel-2 imagery derived wave patterns are extracted using a localized radon transform. A discrete fast-Fourier (DFT) procedure per direction in Radon space (sinogram) is then applied to derive wave spectra. Sentinel-2 time-lag between detector bands is employed to compute the spectral wave-phase shift and depth using the gravity wave linear dispersion. With this novel technique, regional bathymetries are derived at the test-site of Capbreton, France with an root mean squared (RMS)-error of 2.58 m and a correlation coefficient of 0.82 when compared to the survey for depths until 30 m. With the proposed method, the 10 m Sentinel-2 resolution is sufficient to adequately estimate bathymetries for a wave period of 6.5 s or greater. For shorter periods, the pixel resolution does not allow to detect a stable celerity. In addition to the wave-signature enhancement, the capability of the Radon Transform to augment Sentinel-2 20 m resolution imagery to 10 m is demonstrated, increasing the number of suitable bands for the depth inversion. [ABSTRACT FROM AUTHOR]

Calibration/Validation (C/V) studies using sites in the oceans have a long history and protocols are well established. Over lakes, C/V allows addressing problems such as the performance of the various retracking algorithms and evaluating the accuracy of the geophysical corrections for continental waters. This is achievable when measurements of specific and numerous field campaigns and a ground permanent network of level gauges and weather stations are processed. C/V consists of installation of permanent sites (weather stations, limnigraphs, and GPS reference points) and the organization of regular field campaigns. The lake Issykkul serves as permanent site of C/V, for a multi-mission purpose. The objective of this paper is to calculate the altimeter biases of Jason-3 and Sentinel-3A, both belonging to an operational satellite system which is used for the long-term monitoring of lake level variations. We have also determined the accuracy of the altimeters of these two satellites, through a comparison analysis with in situ data. In 2016 and 2017, three campaigns have been organized over this lake in order to estimate the absolute bias of the nadir altimeter onboard the Jason-3 and Sentinel-3A. The fieldwork consisted of measuring water height using a GPS system, carried on a boat, along the track of the altimeter satellite across the lake. It was performed at the time of the pass of the altimeter. Absolute altimeter biases were calculated by averaging the water height differences along the pass of the satellite (GPS from the boat system versus altimetry). Jason-3 operates in a Low Resolution Mode (LRM), while the Sentinel-3A operates in Synthetic Aperture Radar (SAR) mode. In this study we found that the absolute biases measured for Jason-3 were −28 ± 40 mm with the Ocean retracker and 206 ± 30 mm with the Ice-1 retracker. The biases for Sentinel-3A were −14 ± 20 mm with the Samosa (Ocean like) retracker and 285 ± 20 mm with the OCOG (Ice-1-like) retracker. We have also evaluated the accuracy of these two altimeters over Lake Issykkul which reached to 3 cm, for both the instruments, using the Ocean retracker. [ABSTRACT FROM AUTHOR]