Bayesian Estimation of Smooth Altimetric Parameters: Application to Conventional and Delay/Doppler Altimetry

International audienceThis paper proposes a new Bayesian strategy for the smooth estimation of altimetric parameters. The altimetric signal is assumed to be corrupted by a thermal and speckle noise distributed according to an independent and non-identically Gaussian distribution. We introduce a prior enforcing a smooth temporal evolution of the altimetric parameters which improves their physical interpretation. The posterior distribution of the resulting model is optimized using a gradient descent algorithm which allows us to compute the maximum a posteriori estimator of the unknown model parameters. This algorithm has a low computational cost that is suitable for real-time applications. The proposed Bayesian strategy and the corresponding estimation algorithm are evaluated using both synthetic and real data associated with conventional and delay/Doppler altimetry. The analysis of real Jason-2 and CryoSat-2 waveforms shows an improvement in parameter estimation when compared to state-of-the-art estimation algorithms

3D Target Detection and Spectral Classification for Single-photon LiDAR Data

3D single-photon LiDAR imaging has an important role in many applications. However, full deployment of this modality will require the analysis of low signal to noise ratio target returns and a very high volume of data. This is particularly evident when imaging through obscurants or in high ambient background light conditions. This paper proposes a multiscale approach for 3D surface detection from the photon timing histogram to permit a significant reduction in data volume. The resulting surfaces are background-free and can be used to infer depth and reflectivity information about the target. We demonstrate this by proposing a hierarchical Bayesian model for 3D reconstruction and spectral classification of multispectral single-photon LiDAR data. The reconstruction method promotes spatial correlation between point-cloud estimates and uses a coordinate gradient descent algorithm for parameter estimation. Results on simulated and real data show the benefits of the proposed target detection and reconstruction approaches when compared to state-of-the-art processing algorithm

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Likely weakening of the Florida Current during the past century revealed by sea-level observations

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Piecuch, C. G. Likely weakening of the Florida Current during the past century revealed by sea-level observations. Nature Communications, 11(1), (2020): 3973, doi:10.1038/s41467-020-17761-w.The Florida Current marks the beginning of the Gulf Stream at Florida Straits, and plays an important role in climate. Nearly continuous measurements of Florida Current transport are available at 27°N since 1982. These data are too short for assessing possible multidecadal or centennial trends. Here I reconstruct Florida Current transport during 1909–2018 using probabilistic methods and principles of ocean physics applied to the available transport data and longer coastal sea-level records. Florida Current transport likely declined steadily during the past century. Transport since 1982 has likely been weaker on average than during 1909–1981. The weakest decadal-mean transport in the last 110 y likely took place in the past two decades. Results corroborate hypotheses that the deep branch of the overturning circulation declined over the recent past, and support relationships observed in climate models between the overturning and surface western boundary current transports at multidecadal and longer timescales.Funding came from NSF awards OCE-1558966 and OCE-1834739. I acknowledge helpful conversations with M. Andres, L. Beal, S. Coats, S. Dangendorf, S. Elipot, T. Frederikse, N. Foukal, G. Gawarkiewicz, G. Gebbie, B. Hamlington, J. Heiderich, A. Kemp, Y.-O. Kwon, F. Landerer, C. Little, M. Thomas, T. Wahl, and S. Wijffels

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Variational fluid flow measurements from image sequences: synopsis and perspectives

[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]SPEEVariational approaches to image motion segmentation has been an active field of study in image processing and computer vision for two decades. We present a short overview over basic estimation schemes and report in more detail recent modifications and applications to fluid flow estimation. Key properties of these approaches are illustrated by numerical examples. We outline promising research directions and point out the potential of variational techniques in combination with correlation-based PIV methods, for improving the consistency of fluid flow estimation and simulation

Towards an Integrated Assessment of Sea-Level Observations Along the U.S. Atlantic Coast

Sea levels are rising globally due to anthropogenic climate change. However, local sea levels that impact coastal ecosystems often differ from the global trend, sometimes by a factor of two or more. Improved understanding of this regional variability provides insights into geophysical processes and has implications for coastal communities developing resilience to ongoing sea-level rise. This dissertation conducts an investigation of sea level and its contributing processes at multiple spatial scales. Focusing on primarily interannual time-scales and data-driven approaches, new data sources and technologies are utilized to reduce current uncertainties. First, sea-level trends are assessed over the global ocean and at coastlines using data from the recently launched ICESat-2 satellite. These trends agree well with independent measurements, while also filling observational gaps along undersampled coastlines and at high-latitudes. Next, the spatial focus is narrowed to the U.S. East Coast, which is experiencing exceptionally high rates of relative sea-level rise, largely due to land subsidence. By incorporating new state-of-the-art estimates of land-ice melt, an existing Bayesian hierarchical space-time model is expanded to assess the relative contributions of sea surface height and vertical land motion to 20th century relative-sea level change. Model results confirm previous findings that identified regional-scale geological processes as the primary driver of spatial variability in East Coast relative sea level. By rigorously quantifying uncertainties, constraints are placed on the current state of knowledge with clear directions for future research. Finally, small-scale vertical land motion in Hampton Roads, VA is investigated using the remote-sensing technology of Interferometric Synthetic Aperture Radar (InSAR). Two different data sources and processing strategies are implemented which independently reveal substantial rates of vertical land motion that vary over short spatial scales. The results highlight the importance of vertical land motion in exacerbating negative impacts of relative sea-level rise such as flooding and inundation. Overall, this study leverages new spaceborne sensors, an innovative statistical model, and state-of-the-art processing strategies to enhance our understanding of ongoing sea-level change

NOSS algorithm specifications for ocean current mapping, volume 2

The development of National Oceanic Satellite System (NOSS) algorithms for ocean current mapping and the verification of algorithm performance with SEASAT-A radar altimeter data is described