Review of the CALIMAS Team Contributions to European Space Agency's Soil Moisture and Ocean Salinity Mission Calibration and Validation

Camps, Adriano ... et al.-- 38 pages, 22 figuresThis work summarizes the activities carried out by the SMOS (Soil Moisture and Ocean Salinity) Barcelona Expert Center (SMOS-BEC) team in conjunction with the CIALE/Universidad de Salamanca team, within the framework of the European Space Agency (ESA) CALIMAS project in preparation for the SMOS mission and during its first year of operation. Under these activities several studies were performed, ranging from Level 1 (calibration and image reconstruction) to Level 4 (land pixel disaggregation techniques, by means of data fusion with higher resolution data from optical/infrared sensors). Validation of SMOS salinity products by means of surface drifters developed ad-hoc, and soil moisture products over the REMEDHUS site (Zamora, Spain) are also presented. Results of other preparatory activities carried out to improve the performance of eventual SMOS follow-on missions are presented, including GNSS-R to infer the sea state correction needed for improved ocean salinity retrievals and land surface parameters. Results from CALIMAS show a satisfactory performance of the MIRAS instrument, the accuracy and efficiency of the algorithms implemented in the ground data processors, and explore the limits of spatial resolution of soil moisture products using data fusion, as well as the feasibility of GNSS-R techniques for sea state determination and soil moisture monitoringThis work has been performed under research grants TEC2005-06863-C02-01/TCM, ESP2005-06823-C05, ESP2007-65667-C04, AYA2008-05906-C02-01/ESP and AYA2010-22062-C05 from the Spanish Ministry of Science and Innovation, and a EURYI 2004 award from the European Science FoundationPeer Reviewe

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

Earth remote sensing with SMOS, Aquarius and SMAP missions

The first three of a series of new generation satellites operating at L-band microwave frequencies have been launch in the last decade. L-band is particularly sensitive to the presence of water content in the scene under observation, being considered the optimal bandwidth for measuring the Earth's global surface soil moisture (SM) over land and sea surface salinity (SSS) over oceans. Monitoring these two essential climate variables is needed to further improve our understanding of the Earth's water and energy cycles. Additionally, remote sensing at L-band has been proved useful for monitoring the stability in ice sheets and measuring sea ice thickness. The ESA's Soil Moisture and Ocean Salinity (SMOS, 2009-2017) is the first mission specifically launched to monitor SM and SSS. It carries on-board a novel synthetic aperture radiometer with multi-angular and full-polarization capabilities. NASA's Aquarius (2011-2015) was the second mission, devoted to SSS monitoring with a combined real aperture radiometer/scatterometer system that allows correcting for sea surface roughness. NASA's Soil Moisture Active Passive (SMAP, 2015-2018) is the second mission dedicated to measure SM. It carries on-board a real aperture full-polarimetric radiometer and a synthetic aperture radar (SAR) for enhanced spatial resolution and freeze/thaw detection. This Ph.D. Thesis is focused on analyzing the geophysical information that can be obtained from L-band SMOS, Aquarius and SMAP observations. The research activities are structured as follows: -Inter-comparison of radiometer brightness temperatures at selected targets. A novel methodology to measure the consistency between SMOS and Aquarius radiometric data over the entire dynamic range of observations (land, ice and ocean) is proposed. It allows detecting spatial/temporal differences or biases without latitudinal limitations neither cross-overs. This is a necessary step to combine observations from different instruments in a long term dataset for environmental, meteorological, hydrological or climatological studies. -Ice thickness effects on passive remote sensing of Antarctic continental ice. The relationship between Antarctic ice thickness spatial variations and changes detected by SMOS and Aquarius measurements is explored. The emissivity of Antarctica is analyzed to disentangle the role of the geophysical contributions (snow layers at different depths and subglacial lakes) to the observed signal. The stability of the L-band signal in the East Antarctic Plateau, calibration/validation site for microwave satellite missions, is assessed. -Microwave/optical synergy for multi-scale soil moisture sensing. The relationship of SM and land surface temperature (LST) dynamics is evaluated to better understand the fundamental SM-LST link through evapotranspiration and thermal inertia physical processes. A new approach to measure the critical soil moisture from time-series of spaceborne SM and LST is proposed. The synergistic use of SMOS SM and remotely sensed LST for refining SM disaggregation algorithms is also analyzed. -Comparison of passive and active microwave vegetation parameters. Recent research has shown that microwave vegetation opacity, sensitive to biomass and water content, and albedo, related to canopy structure, can be retrieved from passive L-band observations. The relationships between these two parameters and radar-derived vegetation descriptors have been explored using airborne observations from the SMAP Validation Experiment 2012 (SMAPVEX12). The obtained relations could allow for improved SM retrievals in active-passive systems, and also to estimate the vegetation properties at high resolution using SAR observations. The Ph.D. Thesis has been developed within the activities of the Barcelona Expert Centre (BEC). The presented results contribute to the use of L-band remote sensing in different scientific disciplines such as climate, cryosphere, hydrology and ecology.Els primers tres d'una sèrie de satèl·lits de nova generació funcionant a la banda L han sigut llançats a l'última dècada. La banda L es molt sensible a la presència d'aigua a l'escena observada, sent considerada òptima per mesurar la humitat del sòl (SM) i la salinitat del mar (SSS) de manera global a la superfície de la Terra. Monitoritzar aquestes dues variables climàtiques essencials es necessari per millorar el nostre coneixement dels cicles de l'aigua i l'energia. La teledetecció a banda L també ha sigut útil per monitoritzar l'estabilitat de les capes de gel i mesurar el gruix de gel marí. La missió Soil Moisture and Ocean Salinity (SMOS, 2009-2017) de l'ESA és la primera específicament llançada per monitoritzar SM i SSS. Porta un nou radiòmetre d'apertura sintètica amb capacitat multiangular i polarització completa. La missió Aquarius (2011-2015) de la NASA va ser la segona, dedicada a monitoritzar SSS amb un sistema de radiòmetre/escateròmetre d’apertura real que permet corregir la rugositat de la superfície del mar. La missió Soil Moisture Active Passive (SMAP, 2015-2018) de la NASA és la segona dedicada a mesurar SM. Porta un radiòmetre d'apertura real i polarització completa i un radar d'apertura sintètica (SAR) per una millor resolució espaial i detecció de congelació/descongelació. Aquesta tesi està enfocada en analitzar la informació geofísica que pot obtenir-se de les observacions a banda L d'SMOS, Aquarius i SMAP. La seva investigació està estructurada com: -Intercomparació de temperatures de brillantor en zones seleccionades. Es proposa un nou mètode per mesurar la consistència entre les dades radiomètriques d'SMOS i Aquarius sobre el rang dinàmic complet d'observacions (terra, gel, oceà). Això permet detectar diferències espaials/temporals o biaixos sense limitacions latitudinals ni creuaments. Aquest pas es necessari per combinar observacions de diferents instruments en un llarg conjunt de dades per estudis mediambientals, hidrològics o climatològics. -Efecte de gruix de gel en teledetecció de gel continental a l'Antàrtida. S'explora la relació entre les variacions espaials del gruix de gel antàrtic i els canvis detectats a les mesures d'SMOS i Aquarius. L'emissivitat de l'Antàrtida es analitzada per discernir el rol de les contribucions geofísiques (capes de gel a diferents profunditats i llacs subglacials) al senyal observat. S'avalua l'estabilitat del senyal a banda L sobre la zona est de l'altiplà antàrtic, lloc per calibratge/validació de satèl·lits de microones. -Sinèrgia de microones/òptic per teledetecció de SM multiescala. S'avalua la correlació entre la SM i la temperatura de la superfície del sòl (LST) per entendre millor la relació SM-LST a través de processos físics d'evapotranspiració i inèrcia tèrmica. Es proposa un nou mètode per mesurar la humitat crítica utilitzant sèries temporals de SM i LST de satèl·lit. S'analitza l'ús de la SM de SMOS amb la LST de teledetecció per refinar algorismes de desagregació de SM. -Comparació de paràmetres passius i actius de microones relatius a la vegetació. Recent investigació ha mostrat que l'opacitat, sensible a la biomassa i el contingut d'aigua, i l'albedo, relacionat amb l'estructura, poden ser recuperats d'observacions passives a banda L. S'exploren les relacions entre aquests dos paràmetres i estimadors de vegetació derivats de radar utilitzant les observacions d'avió de l'experiment de validació d'SMAP 2012 (SMAPVEX12). Les relacions obtingudes podrien permetre millors recuperacions de SM en sistemes actius/passius i estimar les propietats de la vegetació a alta resolució utilitzant mesures de SAR. La tesi s'ha desenvolupat dins les activitats del Barcelona Expert Centre (BEC). Els resultats presentats contribueixen a l'ús de la banda L a diferents disciplines científiques com la climatologia, la criosfera, la hidrologia i l'ecologia

Remote sensing satellite image processing techniques for image classification: a comprehensive survey

This paper is a brief survey of advance technological aspects of Digital Image Processing which are applied to remote sensing images obtained from various satellite sensors. In remote sensing, the image processing techniques can be categories in to four main processing stages: Image preprocessing, Enhancement, Transformation and Classification. Image pre-processing is the initial processing which deals with correcting radiometric distortions, atmospheric distortion and geometric distortions present in the raw image data. Enhancement techniques are applied to preprocessed data in order to effectively display the image for visual interpretation. It includes techniques to effectively distinguish surface features for visual interpretation. Transformation aims to identify particular feature of earth’s surface and classification is a process of grouping the pixels, that produces effective thematic map of particular land use and land cover

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Calibration of Cosmic-Ray Soil Neutron Sensors (CRNS) in Different Land Use-Land Covers in Lower Brazos River Basin: A Modeling Approach

The cosmic-ray neutron sensors (CRNS) are a proximal sensor that can be used to estimate spatially averaged soil moisture at hectometer scale. The sensor measures the number of thermalized neutrons created by the collision between cosmic rays and atmosphere that interact with hydrogen atoms present in the environment and can be used to estimate soil moisture. However, extensive in-situ soil moisture measurements are needed to separate the signal of soil moisture from all other hydrogen pools such as aboveground biomass and atmospheric water content to calibrate the sensor. The objective of this study is to introduce a new technique of calibrating the sensor by evaluating water budget closures using CRNS and a calibrated sub-surface model Hydrus with minimal ground measurements. We installed CRNS at three sites in the Brazos river basin representing different land covers and management practices: i) traditional agriculture, ii) native prairie, and iii) managed prairie. The model was parameterized by inverting profile soil moisture information from just three locations in each land cover using the Shuffled Complex Evolution Algorithm in Hydrus-1D. The hydraulic parameters for the entire field were estimated by interpolating between the three locations to populate a Hydrus 2D model domain which was used to simulate the soil moisture distribution in the field. The CRNS was calibrated against the area average of modeled soil moisture distribution in the field. The calibrated dataset was able to capture the soil water budget at all the three sites with a water budget closure error of 0.01 m^3m^-3 -0.07 m^3m^-3 . The first part of validation was done by evaluating the calibrated output against intensively measured gravimetric soil moisture. We achieved acceptable values of RMSE (0.03m^3m^-3 - 0.06 m^3m^-3 ).For second part of validation we compare the evapotranspiration (ET) derived from Landsat thermal sensors and calibrated CRNS output. The ET from Landsat 8 was derived using METRIC algorithm which solves energy balance equation to provide the estimates. The values are calibrated against the reference ET acquired using Penman-Monteith equation. ET from CRNS is calculated using piecewise linear regression model. CRNS performed better than the Landsat-ET and has higher temporal resolution. The method reduces the labor in the regions where conducting field campaigns is difficult. Additionally, CRNS presents itself as a viable alternative to in-situ electromagnetic sensors in the clayey soil where the performance of these sensors is poor due to signal distortion

Data-driven Regularization and Uncertainty Estimation to Improve Sea Ice Data Assimilation

Accurate estimates of sea ice conditions such as ice thickness and ice concentration in the ice-covered regions are critical for shipping activities, ice operations and weather forecasting. The need for this information has increased due to the recent record of decline in Arctic ice extent and thinning of the ice cover, which has resulted in more shipping activities and climate studies. Despite the extensive studies and progress to improve the quality of sea ice forecasts from prognostic models, there is still significant room for improvement. For example, ice-ocean models have difficulty estimating the ice thickness distribution accurately. To help improve model forecasts, data assimilation is used to combine observational data with model forecasts and produce more accurate estimates. The assimilation of ice thickness observations, compared to other ice parameters such as ice concentration, is still relatively unexplored since the satellite-based ice thickness observations have only recently become common. Also, preserving sharp features of ice cover, such as leads and ridges, can be difficult, due to the spatial correlations in the background error covariance matrices. At the same time, the current ice concentration assimilation systems do not directly assimilate high resolution sea ice information from synthetic aperture radar (SAR), even though they are the main source of information for operational production of ice chart products at the Canadian Ice Service. The key challenge in SAR data assimilation is automating the interpretation of SAR images. To address the problem of assimilating ice thickness observations while preserving sharp features, two different objective functions are studied. One with a conventional l2-norm and one imposing an additional l1-norm on the derivative of the ice thickness state estimate as a sparse regularization. The latter is motivated by analysis of high resolution ice thickness observations derived from an airborne electromagnetic sensor demonstrating the sparsity of the ice thickness in the derivative domain. The data fusion and data assimilation experiments are performed over a wide range of background and observation error correlation length scales. Results demonstrate the superiority of using a combined l1-l2 regularization framework especially when the background error correlation length scale was relatively short (approximately five times the analysis grid spacing). The problem of automated information retrieval from SAR images has been explored in a problem of ice/water classification. The selected classification approach takes advantage of neural networks to produce results comparable to a previous study using logistic regression. The employed dataset in both studies is a comprehensive dataset consisting of 15405 SAR images over a seven year period, covering all months and different locations. In addition, recent neural network uncertainty estimation approaches are employed to estimate the uncertainty associated with the classification of ice/water labels, which was not explored in this problem domain previously. These predicted uncertainties can improve the automated classification process by identifying regions in the predictions that should be checked manually by an analyst

Earth Observations for Addressing Global Challenges

"Earth Observations for Addressing Global Challenges" presents the results of cutting-edge research related to innovative techniques and approaches based on satellite remote sensing data, the acquisition of earth observations, and their applications in the contemporary practice of sustainable development. Addressing the urgent tasks of adaptation to climate change is one of the biggest global challenges for humanity. As His Excellency António Guterres, Secretary-General of the United Nations, said, "Climate change is the defining issue of our time—and we are at a defining moment. We face a direct existential threat." For many years, scientists from around the world have been conducting research on earth observations collecting vital data about the state of the earth environment. Evidence of the rapidly changing climate is alarming: according to the World Meteorological Organization, the past two decades included 18 of the warmest years since 1850, when records began. Thus, Group on Earth Observations (GEO) has launched initiatives across multiple societal benefit areas (agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather), such as the Global Forest Observations Initiative, the GEO Carbon and GHG Initiative, the GEO Biodiversity Observation Network, and the GEO Blue Planet, among others. The results of research that addressed strategic priorities of these important initiatives are presented in the monograph

Radio frequency interference detection and mitigation techniques for navigation and Earth observation

Radio-Frequency Interference (RFI) signals are undesired signals that degrade or disrupt the performance of a wireless receiver. RFI signals can be troublesome for any receiver, but they are especially threatening for applications that use very low power signals. This is the case of applications that rely on the Global Navigation Satellite Systems (GNSS), or passive microwave remote sensing applications such as Microwave Radiometry (MWR) and GNSS-Reflectometry (GNSS-R). In order to solve the problem of RFI, RFI-countermeasures are under development. This PhD thesis is devoted to the design, implementation and test of innovative RFI-countermeasures in the fields of MWR and GNSS. In the part devoted to RFI-countermeasures for MWR applications, first, this PhD thesis completes the development of the MERITXELL instrument. The MERITXELL is a multi-frequency total-power radiometer conceived to be an outstanding platform to perform detection, characterization, and localization of RFI signals at the most common MWR imaging bands up to 92 GHz. Moreover, a novel RFI mitigation technique is proposed for MWR: the Multiresolution Fourier Transform (MFT). An assessment of the performance of the MFT has been carried out by comparison with other time-frequency mitigation techniques. According to the results, the MFT technique is a good trade-off solution among all other techniques since it can mitigate efficiently all kinds of RFI signals under evaluation. In the part devoted to RFI-countermeasures for GNSS and GNSS-R applications, first, a system for RFI detection and localization at GNSS bands is proposed. This system is able to detect RFI signals at the L1 band with a sensitivity of -108 dBm at full-band, and of -135 dBm for continuous wave and chirp-like signals when using the averaged spectrum technique. Besides, the Generalized Spectral Separation Coefficient (GSSC) is proposed as a figure of merit to evaluate the Signal-to-Noise Ratio (SNR) degradation in the Delay-Doppler Maps (DDMs) due to the external RFI effect. Furthermore, the FENIX system has been conceived as an innovative system for RFI detection and mitigation and anti-jamming for GNSS and GNSS-R applications. FENIX uses the MFT blanking as a pre-correlation excision tool to perform the mitigation. In addition, FENIX has been designed to be cross-GNSS compatible and RFI-independent. The principles of operation of the MFT blanking algorithm are assessed and compared with other techniques for GNSS signals. Its performance as a mitigation tool is proven using GNSS-R data samples from a real airborne campaign. After that, the main building blocks of the patented architecture of FENIX have been described. The FENIX architecture has been implemented in three real-time prototypes. Moreover, a simulator named FENIX-Sim allows for testing its performance under different jamming scenarios. The real-time performance of FENIX prototype has been tested using different setups. First, a customized VNA has been built in order to measure the transfer function of FENIX in the presence of several representative RFI/jamming signals. The results show how the power transfer function adapts itself to mitigate the RFI/jamming signal. Moreover, several real-time tests with GNSS receivers have been performed using GPS L1 C/A, GPS L2C, and Galileo E1OS. The results show that FENIX provides an extra resilience against RFI and jamming signals up to 30 dB. Furthermore, FENIX is tested using a real GNSS timing setup. Under nominal conditions, when no RFI/jamming signal is present, a small additional jitter on the order of 2-4 ns is introduced in the system. Besides, a maximum bias of 45 ns has been measured under strong jamming conditions (-30 dBm), which is acceptable for current timing systems requiring accuracy levels of 100 ns. Finally, the design of a backup system for GNSS in tracking applications that require high reliability against RFI and jamming attacks is proposed.Les interferències de radiofreqüència (RFI) són senyals no desitjades que degraden o interrompen el funcionament dels receptors sense fils. Les RFI poden suposar un problema per qualsevol receptor, però són especialment amenaçadores per les a aplicacions que fan servir senyals de molt baixa potència. Aquest és el cas de les aplicacions que depenen dels sistemes mundials de navegació per satèl·lit (GNSS) o de les aplicacions de teledetecció passiva de microones, com la radiometria de microones (MWR) i la reflectometria GNSS (GNSS-R). Per combatre aquest problema, sistemes anti-RFI s'estan desenvolupament actualment. Aquesta tesi doctoral està dedicada al disseny, la implementació i el test de sistemes anti-RFI innovadors en els camps de MWR i GNSS. A la part dedicada als sistemes anti-RFI en MWR, aquesta tesi doctoral completa el desenvolupament de l'instrument MERITXELL. El MERITXELL és un radiòmetre multifreqüència concebut com una plataforma excepcional per la detecció, caracterització i localització de RFI a les bandes de MWR més utilitzades per sota dels 92 GHz. A més a més, es proposa una nova tècnica de mitigació de RFI per MWR: la Transformada de Fourier amb Multiresolució (MFT). El funcionament de la MFT s'ha comparat amb el d'altres tècniques de mitigació en els dominis del temps i la freqüència. D'acord amb els resultats obtinguts, la MFT és una bona solució de compromís entre les altres tècniques, ja que pot mitigar de manera eficient tots els tipus de senyals RFI considerats. A la part dedicada als sistemes anti-RFI en GNSS i GNSS-R, primer es proposa un sistema per a la detecció i localització de RFI a les bandes GNSS. Aquest sistema és capaç de detectar senyals RFI a la banda L1 amb una sensibilitat de -108 dBm a tota la banda, i de -135 dBm per a senyals d'ona contínua i chirp fen un mitjana de l'espectre. A més a més, el Coeficient de Separació Espectral Generalitzada (GSSC) es proposa com una mesura per avaluar la degradació de la relació senyal a soroll (SNR) en els Mapes de Delay-Doppler (DDM) a causa del impacte de les RFI. La major contribució d'aquesta tesi doctoral és el sistema FENIX. FENIX és un sistema innovador de detecció i mitigació de RFI i inhibidors de freqüència per aplicacions GNSS i GNSS-R. FENIX utilitza la MFT per eliminar la interferència abans del procés de correlació amb el codi GNSS independentment del tipus de RFI. L'algoritme de mitigació de FENIX s'ha avaluat i comparat amb altres tècniques i els principals components de la seva arquitectura patentada es descriuen. Finalment, un simulador anomenat FENIX-Sim permet avaluar el seu rendiment en diferents escenaris d'interferència. El funcionament en temps real del prototip FENIX ha estat provat utilitzant diferents mètodes. En primer lloc, s'ha creat un analitzador de xarxes per a mesurar la funció de transferència del FENIX en presència de diverses RFI representatives. Els resultats mostren com la funció de transferència s'adapta per mitigar el senyal interferent. A més a més, s'han realitzat diferents proves en temps real amb receptors GNSS compatibles amb els senyals GPS L1 C/A, GPS L2C i Galileo E1OS. Els resultats mostren que FENIX proporciona una resistència addicional contra les RFI i els senyals dels inhibidors de freqüència de fins a 30 dB. A més a més, FENIX s'ha provat amb un sistema comercial de temporització basat en GNSS. En condicions nominals, sense RFI, FENIX introdueix un petit error addicional de tan sols 2-4 ns. Per contra, el biaix màxim mesurat en condicions d'alta interferència (-30 dBm) és de 45 ns, el qual és acceptable per als sistemes de temporització actuals que requereixen nivells de precisió d'uns 100 ns. Finalment, es proposa el disseny d'un sistema robust de seguiment, complementari als GNSS, per a aplicacions que requereixen alta fiabilitat contra RFI.Postprint (published version