2000 days of SMOS at the Barcelona Expert Centre: a tribute to the work of Jordi Font

Soil Moisture and Ocean Salinity (SMOS) is the first satellite mission capable of measuring sea surface salinity and soil moisture from space. Its novel instrument (the L-band radiometer MIRAS) has required the development of new algorithms to process SMOS data, a challenging task due to many processing issues and the difficulties inherent in a new technology. In the wake of SMOS, a new community of users has grown, requesting new products and applications, and extending the interest in this novel brand of satellite services. This paper reviews the role played by the Barcelona Expert Centre under the direction of Jordi Font, SMOS co-principal investigator. The main scientific activities and achievements and the future directions are discussed, highlighting the importance of the oceanographic applications of the mission.Peer ReviewedPostprint (published version

Potential synergetic use of GNSS-R signals to improve the sea-state correction in the sea surface salinity estimation: Application to the SMOS mission

It is accepted that the best way to monitor sea surface salinity (SSS) on a global basis is by means of L-band radiometry. However, the measured sea surface brightness temperature (TB) depends not only on the SSS but also on the sea surface temperature (SST) and, more importantly, on the sea state, which is usually parameterized in terms of the 10-m-height wind speed (U10) or the significant wave height. It has been recently proposed that the mean-square slope (mss) derived from global navigation satellite system (GNSS) signals reflected by the sea surface could be a potentially appropriate sea-state descriptor and could be used to make the necessary sea state TB corrections to improve the SSS estimates. This paper presents a preliminary error analysis of the use of reflected GNSS signals for the sea roughness correction and was performed to support the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) mission; the orbit and parameters for the SMOS instrument were assumed. The accuracy requirement for the retrieved SSS is 0.1 practical salinity units after monthly averaging over 2◦ × 2◦ boxes. In this paper, potential improvements in salinity estimation are hampered mainly by the coarse sampling and by the requirements of the retrieval algorithm, particularly the need for a semiempirical model that relates TB and mss.Postprint (published version

GNSS transpolar earth reflectometry exploriNg system (G-TERN): mission concept

The global navigation satellite system (GNSS) Transpolar Earth Reflectometry exploriNg system (G-TERN) was proposed in response to ESA's Earth Explorer 9 revised call by a team of 33 multi-disciplinary scientists. The primary objective of the mission is to quantify at high spatio-temporal resolution crucial characteristics, processes and interactions between sea ice, and other Earth system components in order to advance the understanding and prediction of climate change and its impacts on the environment and society. The objective is articulated through three key questions. 1) In a rapidly changing Arctic regime and under the resilient Antarctic sea ice trend, how will highly dynamic forcings and couplings between the various components of the ocean, atmosphere, and cryosphere modify or influence the processes governing the characteristics of the sea ice cover (ice production, growth, deformation, and melt)? 2) What are the impacts of extreme events and feedback mechanisms on sea ice evolution? 3) What are the effects of the cryosphere behaviors, either rapidly changing or resiliently stable, on the global oceanic and atmospheric circulation and mid-latitude extreme events? To contribute answering these questions, G-TERN will measure key parameters of the sea ice, the oceans, and the atmosphere with frequent and dense coverage over polar areas, becoming a “dynamic mapper”of the ice conditions, the ice production, and the loss in multiple time and space scales, and surrounding environment. Over polar areas, the G-TERN will measure sea ice surface elevation (<;10 cm precision), roughness, and polarimetry aspects at 30-km resolution and 3-days full coverage. G-TERN will implement the interferometric GNSS reflectometry concept, from a single satellite in near-polar orbit with capability for 12 simultaneous observations. Unlike currently orbiting GNSS reflectometry missions, the G-TERN uses the full GNSS available bandwidth to improve its ranging measurements. The lifetime would be 2025-2030 or optimally 2025-2035, covering key stages of the transition toward a nearly ice-free Arctic Ocean in summer. This paper describes the mission objectives, it reviews its measurement techniques, summarizes the suggested implementation, and finally, it estimates the expected performance.Peer ReviewedPostprint (published version

SMOS instrument performance and calibration after six years in orbit

ESA's Soil Moisture and Ocean Salinity (SMOS) mission, launched 2-Nov-2009, has been in orbit for over 6 years, and its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) in two dimensions keeps working well. The calibration strategy remains overall as established after the commissioning phase, with a few improvements. The data for this whole period has been reprocessed with a new fully polarimetric version of the Level-1 processor which includes a refined calibration schema for the antenna losses. This reprocessing has allowed the assessment of an improved performance benchmark. An overview of the results and the progress achieved in both calibration and image reconstruction is presented in this contribution.Peer ReviewedPostprint (author's final draft

Multiscale soil moisture retrievals from microwave remote sensing observations

Memoria de tesis doctoral presentada por María Piles Guillem para optar al grado de Doctora por la Universitat Politècnica de Catalunya (UPC), realizada bajo la dirección del Dr. Adriano Camps y de la Dra. Mercè Vall-llossera.-- 159 pages[EN] Soil moisture is a key state variable of the Earth’s system; it is the main variable that links the Earth’s water, energy and carbon cycles. Soil moisture variations affect the evolution of weather and climate over continental regions, and accurate observations of the Earth’s changing soil moisture are needed to achieve sustainable land and water management, and to enhance weather and climate forecasting skill, flood prediction and drought monitoring. This Ph.D. Thesis focuses on measuring the Earth’s surface soil moisture from space at a global and regional scale. [...][ES] La humedad del suelo es la variable que regula los intercambios de agua, energía, y carbono entre la tierra y la atmósfera. Mediciones precisas de humedad son necesarias para una gestión sostenible de los recursos de agua del planeta, para mejorar las predicciones meteorológicas y climáticas, y para la detección y monitorización de sequías e inundaciones. Esta tesis se centra en la medición de la humedad superficial de la Tierra desde el espacio, a escalas global y regional. [...]This work has been funded by the Spanish Ministry of Science and Education under the FPU grant AP2005-4912 and projects ESP2007-65667-C04-02 and AYA2008-05906-C02-01/ESPPeer Reviewe

The Determination of Surface Salinity with the European SMOS Space Mission

The European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission aims at obtaining global maps of soil moisture and sea surface salinity from space for large-scale and climatic studies. It uses an L-band (1400–1427 MHz) Microwave Interferometric Radiometer by Aperture Synthesis to measure brightness temperature of the earth’s surface at horizontal and vertical polarizations ( h and v). These two parameters will be used together to retrieve the geophysical parameters. The retrieval of salinity is a complex process that requires the knowledge of other environmental information and an accurate processing of the radiometer measurements. Here, we present recent results obtained from several studies and field experiments that were part of the SMOS mission, and highlight the issues still to be solved

Advanced methods for earth observation data synergy for geophysical parameter retrieval

The first part of the thesis focuses on the analysis of relevant factors to estimate the response time between satellite-based and in-situ soil moisture (SM) using a Dynamic Time Warping (DTW). DTW was applied to the SMOS L4 SM, and was compared to in-situ root-zone SM in the REMEDHUS network in Western Spain. The method was customized to control the evolution of time lag during wetting and drying conditions. Climate factors in combination with crop growing seasons were studied to reveal SM-related processes. The heterogeneity of land use was analyzed using high-resolution images of NDVI from Sentinel-2 to provide information about the level of spatial representativity of SMOS data to each in-situ station. The comparison of long-term precipitation records and potential evapotranspiration allowed estimation of SM seasons describing different SM conditions depending on climate and soil properties. The second part of the thesis focuses on data-driven methods for sea ice segmentation and parameter retrieval. A Bayesian framework is employed to segment sets of multi-source satellite data. The Bayesian unsupervised learning algorithm allows to investigate the ‘hidden link’ between multiple data. The statistical properties are accounted for by a Gaussian Mixture Model, and the spatial interactions are reflected using Hidden Markov Random Fields. The algorithm segments spatial data into a number of classes, which are represented as a latent field in physical space and as clusters in feature space. In a first application, a two-step probabilistic approach based on Expectation-Maximization and the Bayesian segmentation algorithm was used to segment SAR images to discriminate surface water from sea ice types. Information on surface roughness is contained in the radar backscattering images which can be - in principle - used to detect melt ponds and to estimate high-resolution sea ice concentration (SIC). In a second study, the algorithm was applied to multi-incidence angle TB data from the SMOS L1C product to harness the its sensitivity to thin ice. The spatial patterns clearly discriminate well-determined areas of open water, old sea ice and a transition zone, which is sensitive to thin sea ice thickness (SIT) and SIC. In a third application, SMOS and the AMSR2 data are used to examine the joint effect of CIMR-like observations. The information contained in the low-frequency channels allows to reveal ranges of thin sea ice, and thicker ice can be determined from the relationship between the high-frequency channels and changing conditions as the sea ice ages. The proposed approach is suitable for merging large data sets and provides metrics for class analysis, and to make informed choices about integrating data from future missions into sea ice products. A regression neural network approach was investigated with the goal to infer SIT using TB data from the Flexible Microwave Payload 2 (FMPL-2) of the FSSCat mission. Two models - covering thin ice up to 0.6m and the full-range of SIT - were trained on Arctic data using ground truth data derived from the SMOS and Cryosat-2. This work demonstrates that moderate-cost CubeSat missions can provide valuable data for applications in Earth observation.La primera parte de la tesis se centra en el análisis de los factores relevantes para estimar el tiempo de respuesta entre la humedad del suelo (SM) basada en el satélite y la in-situ, utilizando una deformación temporal dinámica (DTW). El DTW se aplicó al SMOS L4 SM, y se comparó con la SM in-situ en la red REMEDHUS en el oeste de España. El método se adaptó para controlar la evolución del desfase temporal durante diferentes condiciones de humedad y secado. Se estudiaron los factores climáticos en combinación con los períodos de crecimiento de los cultivos para revelar los procesos relacionados con la SM. La heterogeneidad del uso del suelo se analizó utilizando imágenes de alta resolución de NDVI de Sentinel-2 para proporcionar información sobre el nivel de representatividad espacial de los datos de SMOS a cada estación in situ. La comparación de los patrones de precipitación a largo plazo y la evapotranspiración potencial permitió estimar las estaciones de SM que describen diferentes condiciones de SM en función del clima y las propiedades del suelo. La segunda parte de esta tesis se centra en métodos dirigidos por datos para la segmentación del hielo marino y la obtención de parámetros. Se emplea un método de inferencia bayesiano para segmentar conjuntos de datos satelitales de múltiples fuentes. El algoritmo de aprendizaje bayesiano no supervisado permite investigar el “vínculo oculto” entre múltiples datos. Las propiedades estadísticas se contabilizan mediante un modelo de mezcla gaussiana, y las interacciones espaciales se reflejan mediante campos aleatorios ocultos de Markov. El algoritmo segmenta los datos espaciales en una serie de clases, que se representan como un campo latente en el espacio físico y como clústeres en el espacio de las variables. En una primera aplicación, se utilizó un enfoque probabilístico de dos pasos basado en la maximización de expectativas y el algoritmo de segmentación bayesiano para segmentar imágenes SAR con el objetivo de discriminar el agua superficial de los tipos de hielo marino. La información sobre la rugosidad de la superficie está contenida en las imágenes de backscattering del radar, que puede utilizarse -en principio- para detectar estanques de deshielo y estimar la concentración de hielo marino (SIC) de alta resolución. En un segundo estudio, el algoritmo se aplicó a los datos TB de múltiples ángulos de incidencia del producto SMOS L1C para aprovechar su sensibilidad al hielo fino. Los patrones espaciales discriminan claramente áreas bien determinadas de aguas abiertas, hielo marino viejo y una zona de transición, que es sensible al espesor del hielo marino fino (SIT) y al SIC. En una tercera aplicación, se utilizan los datos de SMOS y de AMSR2 para examinar el efecto conjunto de las observaciones tipo CIMR. La información contenida en los canales de baja frecuencia permite revelar rangos de hielo marino delgado, y el hielo más grueso puede determinarse a partir de la relación entre los canales de alta frecuencia y las condiciones cambiantes a medida que el hielo marino envejece. El enfoque propuesto es adecuado para fusionar grandes conjuntos de datos y proporciona métricas para el análisis de clases, y para tomar decisiones informadas sobre la integración de datos de futuras misiones en los productos de hielo marino. Se investigó un enfoque de red neuronal de regresión con el objetivo de inferir el SIT utilizando datos de TB de la carga útil de microondas flexible 2 (FMPL-2) de la misión FSSCat. Se entrenaron dos modelos - que cubren el hielo fino hasta 0.6 m y el rango completo del SIT - con datos del Ártico utilizando datos de “ground truth” derivados del SMOS y del Cryosat-2. Este trabajo demuestra que las misiones CubeSat de coste moderado pueden proporcionar datos valiosos para aplicaciones de observación de la Tierra.Postprint (published version

Coupled land surface and radiative transfer models for the analysis of passive microwave satellite observations

Soil moisture is one of the key variables controlling the water and energy exchanges between Earth’s surface and the atmosphere. Therefore, remote sensing based soil moisture information has potential applications in many disciplines. Besides numerical weather forecasting and climate research these include agriculture and hydrologic applications like flood and drought forecasting. The first satellite specifically designed to deliver operational soil moisture products, SMOS (Soil Moisture and Ocean Salinity), was launched 2009 by the European Space Agency (ESA). SMOS is a passive microwave radiometer working in the L-band of the microwave domain, corresponding to a frequency of roughly 1.4 GHz and relies on a new concept. The microwave radiation emitted by the Earth’s surface is measured as brightness temperatures in several look angles. A radiative transfer model is used in an inversion algorithm to retrieve soil moisture and vegetation optical depth, a measure for the vegetation attenuation of the soil’s microwave emission. For the application of passive microwave remote sensing products a proper validation and uncertainty assessment is essential. As these sensors have typical spatial resolutions in the order of 40 – 50 km, a validation that relies solely on ground measurements is costly and labour intensive. Here, environmental modelling can make a valuable contribution. Therefore the present thesis concentrates on the question which contribution coupled land surface and radiative transfer models can make to the validation and analysis of passive microwave remote sensing products. The objective is to study whether it is possible to explain known problems in the SMOS soil moisture products and to identify potential approaches to improve the data quality. The land surface model PROMET (PRocesses Of Mass and Energy Transfer) and the radiative transfer model L-MEB (L-band microwave emission of the Biosphere) are coupled to simulate land surface states, e.g. temperatures and soil moisture, and the resulting microwave emission. L-MEB is also used in the SMOS soil moisture processor to retrieve soil moisture and vegetation optical depth simultaneously from the measured microwave emission. The study area of this work is the Upper Danube Catchment, located mostly in Southern Germany. Since model validation is essential if model data are to be used as reference, both models are validated on different spatial scales with measurements. The uncertainties of the models are quantified. The root mean squared error between modelled and measured soil moisture at several measuring stations on the point scale is 0.065 m3/m3. On the SMOS scale it is 0.039 m3/m3. The correlation coefficient on the point scale is 0.84. As it is essential for the soil moisture retrieval from passive microwave data that the radiative transfer modelling works under local conditions, the coupled models are used to assess the radiative transfer modelling with L-MEB on the local and SMOS scales in the Upper Danube Catchment. In doing so, the emission characteristics of rape are described for the first time and the soil moisture retrieval abilities of L-MEB are assessed with a newly developed LMEB parameterization. The results show that the radiative transfer modelling works well under most conditions in the study area. The root mean squared error between modelled and airborne measured brightness temperatures on the SMOS scale is less than 6 – 9 K for the different look angles. The coupled models are used to analyse SMOS brightness temperatures and vegetation optical depth data in the Upper Danube Catchment in Southern Germany. Since the SMOS soil moisture products are degraded in Southern Germany and in different other parts of the world these analyses are used to narrow down possible reasons for this. The thorough analysis of SMOS brightness temperatures for the year 2011 reveals that the quality of the measurements is degraded like in the SMOS soil moisture product. This points towards radio frequency interference problems (RFI), that are known, but have not yet been studied thoroughly. This is consistent with the characteristics of the problems observed in the SMOS soil moisture products. In addition to that it is observed that the brightness temperatures in the lower look angles are less reliable. This finding could be used to improve the brightness temperature filtering before the soil moisture retrieval. An analysis of SMOS optical depth data in 2011 reveals that this parameter does not contain valuable information about vegetation. Instead, an unexpected correlation with SMOS soil moisture is found. This points towards problems with the SMOS soil moisture retrieval, possibly under the influence of RFI. The present thesis demonstrates that coupled land surface and radiative transfer models can make a valuable contribution to the validation and analysis of passive microwave remote sensing products. The unique approach of this work incorporates modelling with a high spatial and temporal resolution on different scales. This makes detailed process studies on the local scale as well as analyses of satellite data on the SMOS scale possible. This could be exploited for the validation of future satellite missions, e.g. SMAP (Soil Moisture Active and Passive) which is currently being prepared by NASA (National Aeronautics and Space Administration). Since RFI seems to have a considerable influence on the SMOS data due to the gained insights and the quality of the SMOS products is very good in other parts of the world, the RFI containment and mitigation efforts carried out since the launch of SMOS should be continued