Interannual variations in degree-2 Earth's gravity coefficients C-2,C-0, C-2,C-2, and S-2,S-2 reveal large-scale mass transfers of climatic origin

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Radon-augmented sentinel-2 satellite imagery to derive wave-patterns and regional bathymetry

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 A major issue is that the bathymetries are often non-existent or if they do exist, outdated.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

Wave-derived coastal bathymetry from satellite video imagery : a showcase with Pleiades persistent mode

This article shows the capacity do derive depth using the sub metric Pleiades satellite mission (Airbus/CNES) in persistent mode, which allows acquiring a sequence of images (12 images) at a regional scale (similar to 100 km(2)). To derive depths, a spatiotemporal cross-correlation method for estimating wave velocity and inverse bathymetry is presented and applied to the 12-image sequence. A good agreement is found with in-situ bathymetry measurements obtained during the COMBI 2017 Capbreton experiment (correlation of 0.8, RMSE = 1.4 m). Depth estimate saturation is found for depths > 35 m, mainly in a deep canyon just off the coast located in front of the entrance to Capbreton harbour. The image sequence is used to study the sensitivity of the number of images. The results show that the accuracy increases with the number of images in the sequence and with a fine resolution. Despite their noisy nature, newly available time-updated satellite bathymetries can be used to understand coastal evolution at several scales and improve risk mitigation strategies through modelling

Automatic unmixing of MODIS multi-temporal data for inter-annual monitoring of land use at a regional scale (Tensift, Morocco)

The objective of this study is to develop an approach for monitoring land use over the semi-arid Tensift-Marrakech plain, a 3000 km(2) 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

Remote sensing of glaciers

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

Long-term analysis of snow-covered area in the Moroccan High-Atlas through remote sensing

The High-Atlas Mountainous region in Morocco is a true Water tower for the neighbouring arid plains, where the water resources at e intensively and increasingly Subjected to exploitation for agriculture and tourism In order to manage this resource sustainably, it is necessary to describe accurately all the processes that contribute to the hydrological cycle of the area. and, in particular, to know the respective contributions of liquid and solid precipitations to runoff In this context, a seven-year time series of SPOT-VEGETATION images is used for mapping snow-covered areas The spatial and temporal variations of the snow cover are analyzed for the entire High-Atlas region as well as by altitudinal zones The spatial distribution of snow-covered areas appears logically controlled by elevation. and its temporal fluctuations can be clearly used to identify dry and wet seasons In addition, a possible control of snowfalls by the Northern Atlantic climate variability, and, in particular, the North Atlantic Oscillation, is highlighted Finally, this Study Shows how satellite remote sensing can be useful for the long-term observation of the intra- and inter-annual variability of snowpacks in rather inaccessible regions where the network of meteorological stations is deficient (C) 2009 Elsevier B V All rights reserve