Retrieval of Ocean Surface Currents and Winds Using Satellite SAR backscatter and Doppler frequency shift

Ocean surface winds and currents play an important role for weather, climate, marine life, ship navigation, oil spill drift and search and rescue. In-situ observations of the ocean are sparse and costly. Satellites provide a useful complement to these observations. Synthetic aperture radar (SAR) is particularly attractive due to its high spatial resolution and its capability to extract both sea surface winds and currents day and night and almost independent of weather.The work in this thesis involves processing of along-track interferometric SAR (ATI-SAR) data, analysis of the backscatter and Doppler frequency shift, and development of wind and current retrieval algorithms. Analysis of the Doppler frequency shift showed a systematic bias. A calibration method was proposed and implemented to correct for this bias. Doppler analysis also showed that the wave contribution to the SAR Doppler centroid often dominates over the current contribution. This wave contribution is estimated using existing theoretical and empirical Doppler models. For wind and current retrieval, two methods were developed and implemented.The first method, called the direct method, consists of retrieval of the wind speed from SAR backscatter using an empirical backscatter model. In order to retrieve the radial current, the retrieved wind speed is used to correct for the wave contribution. The current retrieval was assessed using two different (theoretical and empirical) Doppler models and wind inputs (model and SAR-derived). It was found that the results obtained by combining the Doppler empirical model with the SAR-derived wind speed were more consistent with ocean models.The second method, called Bayesian method, consists of blending the SAR observables (backscatter and Doppler shift) with an atmospheric and an oceanic model to retrieve the total wind and current vector fields. It was shown that this method yields more accurate estimates, i.e. reduces the models biases against in-situ measurements. Moreover, the method introduces small scale features, e.g. fronts and meandering, which are weakly resolved by the models.The correlation between the surface wind vectors and the SAR Doppler shift was demonstrated empirically using the Doppler shift estimated from over 300 TanDEM-X interferograms and ECMWF reanalysis wind vectors. Analysis of polarimetric data showed that theoretical models such as Bragg and composite surface models over-estimate the backscatter polarization ratio and Doppler shift polarization difference. A combination of a theoretical Doppler model and an empirical modulation transfer function was proposed. It was found that this model is more consistent with the analyzed data than the pure theoretical models.The results of this thesis will be useful for integrating SAR retrievals in ocean current products and assimilating SAR observables in the atmospheric, oceanic or coupled models. The results are also relevant for preparation studies of future satellite missions

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

Measurements of Sea Surface Currents in the Baltic Sea Region Using Spaceborne Along-Track InSAR

The main challenging problems in ocean current retrieval from along-track interferometric (ATI)-synthetic aperture radar (SAR) are phase calibration and wave bias removal. In this paper, a method based on differential InSAR (DInSAR) technique for correcting the phase offset and its variation is proposed. The wave bias removal is assessed using two different Doppler models and two different wind sources. In addition to the wind provided by an atmospheric model, the wind speed used for wave correction in this work is extracted from the calibrated SAR backscatter. This demonstrates that current retrieval from ATI-SAR can be completed independently of atmospheric models. The retrieved currents, from four TanDEM-X (TDX) acquisitions over the 6resund channel in the Baltic Sea, are compared to a regional ocean circulation model. It is shown that by applying the proposed phase correction and wave bias removal, a good agreement in spatial variation and current direction is achieved. The residual bias, between the ocean model and the current retrievals, varies between 0.013 and 0.3 m/s depending on the Doppler model and wind source used for wave correction. This paper shows that using SAR as a source of wind speed reduces the bias and root-mean-squared-error (RMSE) of the retrieved currents by 20% and 15%, respectively. Finally, the sensitivity of the sea current retrieval to Doppler model and wind errors are discussed

Meteo-Marine Parameters from Sentinel-1 SAR Imagery: Towards Near Real-Time Services for the Baltic Sea

A method for estimating meteo-marine parameters from satellite Synthetic Aperture Radar (SAR) data, intended for near-real-time (NRT) service over the Baltic Sea, is presented and validated. Total significant wave height data are retrieved with an empirical function CWAVE_S1-IW, which combines spectral analysis of Sentinel-1A/B Interferometric Wide swath (IW) subscenes with wind data derived with common C-Band Geophysical Model Functions (GMFs). In total, 15 Sentinel-1A/B scenes (116 acquisitions) over the Baltic Sea were processed for comparison with off-shore sea state measurements (52 collocations) and coastal wind measurements (357 colocations). Sentinel-1 wave height was spatially compared with WAM wave model results (Copernicus Marine Environment Monitoring Service (CMEMS). The comparison of SAR-derived wave heights shows good agreement with measured wave heights correlation r of 0.88 and with WAM model (r = 0.85). The wind speed estimated from SAR images yields good agreement with in situ data (r = 0.91). The study demonstrates that the wave retrievals from Sentinel-1 IW data provide valuable information for operational and statistical monitoring of wave conditions in the Baltic Sea. The data is valuable for model validation and interpretation in regions where, and during periods when, in situ measurements are missing. The Sentinel-1 A/B wave retrievals provide more detailed information about spatial variability of the wave field in the coastal zone compared to in situ measurements, altimetry wave products and model forecast. Thus, SAR data enables estimation of storm locations and areal coverage. Methods shown in the study are implemented in NRT service in German Aerospace Center’s (DLR) ground station Neustrelitz

Ocean remote sensing techniques and applications: a review (Part II)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version

Enhancing Surface Soil Moisture Estimation through Integration of Artificial Neural Networks Machine Learning and Fusion of Meteorological, Sentinel-1A and Sentinel-2A Satellite Data

For many environmental and agricultural applications, an accurate estimation of surface soil moisture is essential. This study sought to determine whether combining Sentinel-1A, Sentinel-2A, and meteorological data with artificial neural networks (ANN) could improve soil moisture estimation in various land cover types. To train and evaluate the model’s performance, we used field data (provided by La Tuscia University) on the study area collected during time periods between October 2022, and December 2022. Surface soil moisture was measured at 29 locations. The performance of the model was trained, validated, and tested using input features in a 60:10:30 ratio, using the feed-forward ANN model. It was found that the ANN model exhibited high precision in predicting soil moisture. The model achieved a coefficient of determination (R2) of 0.71 and correlation coefficient (R) of 0.84. Furthermore, the incorporation of Random Forest (RF) algorithms for soil moisture prediction resulted in an improved R2 of 0.89. The unique combination of active microwave, meteorological data and multispectral data provides an opportunity to exploit the complementary nature of the datasets. Through preprocessing, fusion, and ANN modeling, this research contributes to advancing soil moisture estimation techniques and providing valuable insights for water resource management and agricultural planning in the study area

Evaluation of wave retrieval for Chinese Gaofen-3 synthetic aperture radar

The goal of this study was to investigate the performance of a spectral-transformation wave retrieval algorithm and confirm the accuracy of wave retrieval from C-band Chinese Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) images. More than 200 GF-3 SAR images of the coastal China Sea and the Japan Sea for dates from January to July 2020 were acquired in the Quad-Polarization Strip (QPS) mode. The images had a swath of 30 km and a spatial resolution of 8 m pixel size. They were processed to retrieve Significant Wave Height (SWH), which is simulated from a numerical wave model called Simulating WAves Nearshore (SWAN). The first-guess spectrum is essential to the accuracy of Synthetic Aperture Radar (SAR) wave spectrum retrieval. Therefore, we proposed a wave retrieval scheme combining the theocratic-based Max Planck Institute Algorithm (MPI), a Semi-Parametric Retrieval Algorithm (SPRA), and the Parameterized First-guess Spectrum Method (PFSM), in which a full wave-number spectrum and a non-empirical ocean spectrum proposed by Elfouhaily are applied. The PFSM can be driven using the wind speed without calculating the dominant wave phase speed. Wind speeds were retrieved using a Vertical-Vertical (VV) polarized geophysical model function C-SARMOD2. The proposed algorithm was implemented for all collected SAR images. A comparison of SAR-derived wind speeds with European Center for Medium-Range Weather Forecasts (ECMWF) ERA-5 data showed a 1.95 m/s Root-Mean-Squared Error (RMSE). The comparison of retrieved SWH with SWAN-simulated results demonstrated a 0.47 m RMSE, which is less than the 0.68 m RMSE of SWH when using the PFSM algorithm.Output Status: Forthcoming/Available Onlin

Estimation of water resources on continental surfaces by multi-sensor microwave remote sensing

L'estimació dels recursos hídrics de les superfícies continentals a escala regional i global és fonamental per a una bona gestió dels recursos hídrics. Aquesta estimació cobreix una àmplia gamma de temes i camps, incloent-hi la caracterització dels sòls i dels recursos hídrics a l’escala de la conca, la modelització hidrològica i la predicció i la cartografia d'inundacions. En aquest context, la caracterització dels estats de la superfície continental, per a obtenir millors paràmetres d’entrada als models hidrològics, és essencial per millorar la precisió en la simulació de cabals, sequeres i inundacions. L’estimació del contingut d’aigua en el sistema, incloses les diferents masses d’aigua i l’aigua lliure en el sòl, és especialment necessària per a una descripció precisa dels processos hidrològics i, en general, del cicle de l’aigua a les superfícies continentals. Per caracteritzar millor els processos hidrològics, les intervencions antropogèniques no es poden negligir. L'home influeix en el cicle de l'aigua, principalment mitjançant el reg i la construcció de preses, fet que s’ha de quantificar correctament. L’objectiu de la tesi és la millora de l’estimació remota dels recursos hídrics, incloent-hi la quantificació dels factors antròpics, mitjançant l’ús de diversos sensors llançats recentment, aprofitant recents desenvolupaments en la tecnologia de teledetecció. Amb l'arribada de les constel·lacions Sentinel (Sentinel-1, 2, 3), disposem de millors eines per estimar els recursos hídrics, incloent-hi els impactes humans, amb una major precisió i cobertura. Aquest treball de tesi consta principalment de dues línies de recerca on s’estimen les intervencions humanes en el cicle hidrològic: la cartografia del reg (com a aplicació en humitat del sòl), i el forçament d’embassaments en simulacions hidrològiques (com a aplicació de l’altimetria). En la primera linia s’estima la humitat del sòl a partir de l’anàlisi estadística de les dades SAR de Sentinel-1. Es desenvolupen dues metodologies per obtenir la humitat del sòl amb una resolució espacial de 100 m basant-se en la interpretació de les dades de Sentinel-1 obtingudes amb la polarització VV (vertical-vertical), que es combina amb dades òptiques Sentinel-2 per a l'anàlisi dels efectes de la vegetació. Com aplicació de la humitat del sòl, es cartografia el reg en diverses condicions meteorològiques, i amb una alta resolució espacial i temporal. Es proposa una metodologia per a la cartografia del reg mitjançant dades SAR obtingudes en polaritzacions VV (vertical-vertical) i VH (vertical-horitzontal). A partir de la sèrie temporal Sentinel-1, s’analitzen diferents estadístiques i mètriques, incloent-hi el valor mitjà, la variància del senyal, la longitud de la correlació i la dimensió fractal, a partir dels quals es classifiquen els arbres irrigats, els cultius irrigats i els cultius no irrigats. En la segona línia, s’estima el nivell dels embassaments a partir de les dades d’altimetria de Sentinel-3, amb l’altímetre SAR (SRAL), basant-se en diferents algorismes per millorar la precisió. Aquest estudi presenta tres algorismes especialitzats o retrackers destinats a obtenir el nivell de la superfície dels cossos d’aigua estudiats, minimitzant la contaminació de les formes d’ona degut al sòl que els envolta. Es compara el rendiment del mètode proposat de selecció de la porció d’ona amb tres retrackers, és a dir, un retracker de llindar, el retracker del centre de gravetat (OCOG) i un retracker de base física de dos passos. S’obtenen sèries temporals del nivell de la làmina d’aigua d’embassaments situats a la conca del riu Ebre (Espanya). Com aplicació, les sèries de nivell dels embassaments obtingudes s’utilitzen per a forçar els embassaments en simulacions hidrològiques.La estimación de los recursos hídricos de las superficies continentales a escala regional y global es fundamental para una buena gestión de los recursos hídricos. Esta estimación cubre una amplia gama de temas y campos, incluyendo la caracterización de los suelos y de los recursos hídricos a escala de cuenca, la modelización hidrológica y la predicción y la cartografía de inundaciones. En este contexto, la caracterización de los estados de la superficie continental, para obtener mejores parámetros de entrada para los modelos hidrológicos, es esencial para mejorar la precisión en la simulación de caudales, sequías e inundaciones. La estimación del contenido de agua en el sistema, incluidas las diferentes masas de agua y el agua libre en el suelo, es especialmente necesaria para una descripción precisa de los procesos hidrológicos y, en general, del ciclo del agua en las superficies continentales. Una caracterización precisa de los procesos hidrológicos requiere no descuidar las intervenciones humanas. El hombre influye en el ciclo del agua, principalmente mediante el riego y la construcción de embalses, lo que se debe cuantificar correctamente. El objetivo de la tesis es la mejora de la estimación remota de los recursos hídricos, incluyendo la cuantificación de los factores humanos, mediante el uso de varios sensores lanzados recientemente, aprovechando recientes desarrollos en la tecnología de teledetección. Con la llegada de las constelaciones Sentinel (Sentinel-1, 2, 3), disponemos de mejores herramientas para estimar los recursos hídricos, incluyendo los impactos humanos, con una mayor precisión y cobertura. Este trabajo de tesis consta principalmente en dos ejes de investigación donde se estiman las intervenciones humanas en el ciclo hidrológico: la cartografía del riego (como aplicación en humedad del suelo), y el forzamiento de embalses en simulaciones hidrológicas (como aplicación de la altimetría). En relación al primer eje, se estima la humedad del suelo a partir del análisis estadístico de los datos SAR de Sentinel-1. Se desarrollan dos metodologías para obtener la humedad del suelo con una resolución espacial de 100 m basándose en la interpretación de los datos de Sentinel-1 obtenidas con la polarización VV (vertical-vertical), que se combina con datos ópticas Sentinel-2 para el análisis de los efectos de la vegetación. Como aplicación de la humedad del suelo, se cartografía el riego en diversas condiciones meteorológicas, y con una alta resolución espacial y temporal. Se propone una metodología para la cartografía del riego mediante datos SAR obtenidos en polarizaciones VV (vertical-vertical) y VH (vertical-horizontal). A partir de la serie temporal Sentinel-1, se analizan diferentes estadísticas y métricas, incluyendo el valor medio, la varianza de la señal, la longitud de la correlación y la dimensión fractal, a partir de los cuales se clasifican los árboles irrigados, los cultivos irrigados y los cultivos no irrigados. En el segundo eje, se estima el nivel de los embalses a partir de los datos de altimetría de Sentinel-3, con el altímetro SAR (SRAL), basándose en diferentes algoritmos para mejorar la precisión. Este estudio presenta tres algoritmos especializados o retrackers destinados a obtener el nivel de la superficie de los cuerpos de agua estudiados, minimizando la contaminación de las formas de onda debido al suelo que los rodea. Se compara el rendimiento del método propuesto de selección de la porción de onda con tres retrackers, es decir, un retracker de umbral, el retracker del centro de gravedad (OCOG) y un retracker de base física de dos pasos. Se obtienen series temporales del nivel de la lámina de agua de embalses situados en la cuenca del río Ebro (España). Como aplicación, las series de nivel de los embalses obtenidas se utilizan para forzar los embalses en simulaciones hidrológicas.The estimation of the water resources of the continental surfaces at a regional and global scale is fundamental for good water resources management. This estimation covers a wide range of topics and fields, including the characterisation of soils and water resources at the basin scale, hydrological modelling and flood prediction and mapping. In this context, the characterisation of the states of the continental surface, to obtain better input parameters for hydrological models, is essential to improve the precision in the simulation of flows, droughts, and floods. The estimation of the water content in the system, including the different water bodies and the free water in the soil, is especially necessary for a precise description of the hydrological processes and, in general, of the water cycle on the continental surfaces. To better characterise hydrological processes, human interventions cannot be neglected. Humans influence the water cycle, mainly through irrigation and the construction of reservoirs, which must be correctly quantified. The objective of the thesis is the improvement of the remote estimation of water resources, including the quantification of human factors, using several sensors recently launched, taking advantage of recent developments in remote sensing technology. With the arrival of the Sentinel constellations (Sentinel-1, 2, 3), we have better tools to estimate water resources, including human impacts, with greater precision and coverage. This thesis consists mainly of two parts where human interventions in the water cycle are considered: irrigation cartography (as an application of soil moisture), and the forcing of reservoirs in hydrological simulations (as an application of altimetry). Firstly, soil moisture is estimated from the statistical analysis of Sentinel-1 SAR data. Two methodologies are developed to obtain soil moisture with a spatial resolution of 100 m based on the interpretation of Sentinel-1 data collected with the VV polarization (vertical-vertical), which is combined with optical data of Sentinel-2 for the analysis of the effects of vegetation. Secondly, irrigation is mapped under various meteorological conditions, including high spatial and temporal resolution. A methodology for irrigation mapping is proposed using SAR data obtained in VV (vertical-vertical) and VH (vertical-horizontal) polarizations. With Sentinel-1 time series, different statistics and metrics are analysed, including the mean value, the variance of the signal, the correlation length and the fractal dimension, based on which the classification of irrigated trees, irrigated crops, and non-irrigated crops are derived. Finally, the level of the reservoirs is estimated from the Sentinel-3 altimetry data, with the SAR altimeter (SRAL), based on different algorithms to improve the accuracy. This study presents three specialised algorithms or retrackers designed to obtain the level of the surface of the studied inland bodies of water, minimising the contamination of the waveforms due to the surrounding soil. The performance of the selection method of the proposed wave portion is compared with three retrackers, that is, the centre of gravity retracker (OCOG) and the two-step physical-based retracker. Temporal series of the water level of reservoirs located in the basin of the Ebro River (Spain) are obtained. As an application, the level series of the reservoirs obtained are used to force the reservoirs in hydrological simulations.L'estimation et le suivi des ressources en eau des surfaces continentales aux niveaux régional et global est essentielle pour la gestion du bilan hydrique, particulièrement dans le contexte des changements climatiques et anthropiques. Ils couvrent un large éventail de thèmes et de domaines, incluant la caractérisation des ressources en eau à l'échelle du bassin, la modélisation hydrologique ainsi que la prévision et la cartographie des inondations. Dans ce contexte, la caractérisation des états de surface, en tant que paramètres d’entrée dans les modèles hydrologiques, est essentielle pour obtenir une meilleure précision de la simulation, qui est liée à la précision prévisionnelle des débits des cours d’eau et le suivi des sécheresses et des inondations. L'estimation de la teneur en eau des surfaces continentales, incluant l’état hydrique du sol et les niveaux des surfaces couvertes d’eau, est particulièrement nécessaire pour une description précise des processus hydrologiques et plus généralement du cycle de l'eau sur les surfaces continentales. Afin de mieux comprendre les processus hydrologiques, l'influence humaine (l’effet anthropique) sur le cycle de l'eau nécessite une évaluation fine. Elle est particulièrement liée à la gestion de l’irrigation et la construction de barrages. L'objectif de la thèse était d'améliorer l'estimation des ressources en eau et une meilleure caractérisation des interventions anthropiques à travers l'utilisation de nouveaux capteurs satellitaires multi-configurations du programme européen Copernicus. Avec le développement de la technologie de télédétection spatiale, et plus particulièrement avec l’arrivée des constellations Sentinel (Sentinel-1, 2, 3) à haute résolution spatiale et temporelle, il existe un meilleur outil pour estimer les états des surfaces continentales. Ce travail de thèse comprend principalement deux priorités liées à des interventions humaines dans le cycle hydrologique:la cartographie de l'irrigation en tant que action humaine liée directement à l'humidité du sol et le forçage des barrages dans un modèle de simulation de rivière en tant qu'application liée à l’estimation du niveau de l'eau libre. Un premier axe de recherche a été basé sur une analyse statistique des données SAR Sentinel-1 pour caractériser l’état hydrique du sol. Deux méthodes ont été développées pour estimer ce paramètre avec une résolution spatiale de 100 m. Elles sont basées sur des approches de détection de changement à partir des données Sentinel-1 acquises en polarisation VV (verticale-verticale), combinées aux données optiques Sentinel-2 pour corriger les effets de la végétation. L’application consistait à cartographier l'irrigation, avec des résolutions spatiale et temporelle élevées. Une méthodologie de cartographie de l'irrigation utilisant des données SAR Sentinel-1 a été proposée. Elle estbasée sur les acquisitions en polarisations VV (vertical-vertical) et VH (vertical-horizontal). A partir de la série temporelle des mesures Sentinel-1, des paramètres statistiques tel que la valeur moyenne, la variance du signal, la longueur de corrélation temporelle et la dimension fractale, sont analysées, en fonction du type de culture; cultures annuelles irriguées, arbres irrigués et cultures pluviales. Des classifications supervisées utilisant les approches Random Forest et SVM sont testées. En deuxième axe, l'estimation de la hauteur de la surface de l'eau à partir des données altimétriques de Sentinel-3 avec l’altimètre SAR (SRAL) a été réalisée à l'aide de différents algorithmes afin d'améliorer la précision sur des petites surfaces. Cette étude présente trois algorithmes spécialisés (ou retrackers) dédiées à la minimisation de la contamination des sols par les formes d’ondes permettant de récupérer les niveaux d’eau à partir de données altimétriques SAR sur des masses d’eaux intérieures. Les performances de la méthode de sélection de portion de forme d'onde proposée avec trois retrackers, à savoir, le retracker à seuil, le retracker à centre de gravité décalé (OCOG) et le retracker à base physique à 2 étapes, sont comparées. Des séries chronologiques de niveaux d'eau sont extraites pour les masses d'eau du bassin de l'Èbre (Espagne). Une application des produits altimétriques est proposée. Le produit de niveau d’eau a été utilisé comme paramètre d’entrée pour analyser l’effet tampon des barrages dans les simulations de débits fluviaux