Acquisition and processing software for an airborne soil moisture mapper LBand radiometer

Treball confidencial fins el 19/07/2015The improvement in weather forecast and climate motorization, to prevent for example natural disasters, requires global scale knowledge of the soil moisture (SM) and the surface salinity (SSS), which are non-existent at present due to the difficulty to carry out in-situ measurements. These parameters influence the heat exchange among land, sea and air. On one hand, thanks to the SM, the amount of water on Earth and the exchange of energy between the land’s surface and the atmosphere can be known. On the other hand, the knowledge of the distribution of the SSS will inform about sea currents and differences between evaporation and precipitation. The quantification of these parameters will contribute to improve the weather forecasts, hydrological studies, vegetation motorization, and risk of forest fires. As part of the program “The living planet programme: Earth Explorer Opportunity Missions”, the European Space Agency (ESA) selected in 1999 the Soil Moisture and Ocean Salinity (SMOS) mission, designed to observe soil moisture over land and salinity over the oceans. It was launched in 2009 and has put in orbit the first microwave imaging radiometer using aperture synthesis. Over the sea, sea surface salinity will be remotely measured by means of L-band (1400-1427 MHz) microwave radiometry. As the brightness temperature also depends on the sea surface temperature and on the sea state, post-processing corrections are needed to get the surface salinity. Over land, soil moisture is retrieved from the changes in the L-band brightness temperature, although post processing correction for surface roughness and vegetation are also required

Concurs obert Centre Operatiu de gestió d'autobusos del Barcelonès Nord

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Concurs restringit per a la construcció de 48 habitatges amb protecció al Sector Can Coll

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Using Unsupervised and Supervised Learning and Digital Twin for Deep Convective Ice Storm Classification

Smart Ice Cloud Sensing (SMICES) is a small-sat concept in which a primary radar intelligently targets ice storms based on information collected by a lookahead radiometer. Critical to the intelligent targeting is accurate identification of storm/cloud types from eight bands of radiance collected by the radiometer. The cloud types of interest are: clear sky, thin cirrus, cirrus, rainy anvil, and convection core. We describe multi-step use of Machine Learning and Digital Twin of the Earth's atmosphere to derive such a classifier. First, a digital twin of Earth's atmosphere called a Weather Research Forecast (WRF) is used generate simulated lookahead radiometer data as well as deeper "science" hidden variables. The datasets simulate a tropical region over the Caribbean and a non-tropical region over the Atlantic coast of the United States. A K-means clustering over the scientific hidden variables was utilized by human experts to generate an automatic labelling of the data - mapping each physical data point to cloud types by scientists informed by mean/centroids of hidden variables of the clusters. Next, classifiers were trained with the inputs of the simulated radiometer data and its corresponding label. The classifiers of a random decision forest (RDF), support vector machine (SVM), Gaussian na\"ive bayes, feed forward artificial neural network (ANN), and a convolutional neural network (CNN) were trained. Over the tropical dataset, the best performing classifier was able to identify non-storm and storm clouds with over 80% accuracy in each class for a held-out test set. Over the non-tropical dataset, the best performing classifier was able to classify non-storm clouds with over 90% accuracy and storm clouds with over 40% accuracy. Additionally both sets of classifiers were shown to be resilient to instrument noise

New Passive Instruments Developed for Ocean Monitoring at the Remote Sensing Lab—Universitat Politècnica de Catalunya

Lack of frequent and global observations from space is currently a limiting factor in many Earth Observation (EO) missions. Two potential techniques that have been proposed nowadays are: (1) the use of satellite constellations, and (2) the use of Global Navigation Satellite Signals (GNSS) as signals of opportunity (no transmitter required). Reflectometry using GNSS opportunity signals (GNSS-R) was originally proposed in 1993 by Martin-Neira (ESA-ESTEC) for altimetry applications, but later its use for wind speed determination has been proposed, and more recently to perform the sea state correction required in sea surface salinity retrievals by means of L-band microwave radiometry (TB). At present, two EO space-borne missions are currently planned to be launched in the near future: (1) ESA's SMOS mission, using a Y-shaped synthetic aperture radiometer, launch date November 2nd, 2009, and (2) NASA-CONAE AQUARIUS/SAC-D mission, using a three beam push-broom radiometer. In the SMOS mission, the multi-angle observation capabilities allow to simultaneously retrieve not only the surface salinity, but also the surface temperature and an “effective” wind speed that minimizes the differences between observations and models. In AQUARIUS, an L-band scatterometer measuring the radar backscatter (σ0) will be used to perform the necessary sea state corrections. However, none of these approaches are fully satisfactory, since the effective wind speed captures some sea surface roughness effects, at the expense of introducing another variable to be retrieved, and on the other hand the plots (TB-σ0) present a large scattering. In 2003, the Passive Advance Unit for ocean monitoring (PAU) project was proposed to the European Science Foundation in the frame of the EUropean Young Investigator Awards (EURYI) to test the feasibility of GNSS-R over the sea surface to make sea state measurements and perform the correction of the L-band brightness temperature. This paper: (1) provides an overview of the Physics of the L-band radiometric and GNSS reflectometric observations over the ocean, (2) describes the instrumentation that has been (is being) developed in the frame of the EURYI-funded PAU project, (3) the ground-based measurements carried out so far, and their interpretation in view of placing a GNSS-reflectometer as secondary payload in future SMOS follow-on missions

Sensitivity of cervical cytology in endometrial cancer detection in a tertiary hospital in Spain

Introduction: Cervical cytology is a well-stablished cervical cancer screening method. However, due to the anatomical continuity of the genital tract, it can also detect signs of endometrial disease. Our aim was to estimate the sensitivity of cervical cytology in endometrial cancer detection and prognosis in a large population over a 30-year period in a large academic tertiary hospital in Spain. Methodology: We performed a search for women diagnosed with endometrial cancer from 1990 to 2020, who were surgically treated and had a previous cervical cytology result. Information Technologies Department databases from Bellvitge University Hospital and the Screenwide case-control study's database were used. Cervical cytology results were classified as abnormal when squamous lesions, glandular atypia or malignant cells were identified. Results: Overall, we evaluated 371 women with endometrial cancer and a documented cervical cytology performed within 3 years previous to surgical treatment. Overall, the sensitivity of cervical cytology for endometrial cancer detection was 25.6%. Several clinico-pathological characteristics, such as non-endometrioid histology and a higher stage, were correlated with higher sensitivity

A General Analysis of the Impact of Digitization in Microwave Correlation Radiometers

This study provides a general framework to analyze the effects on correlation radiometers of a generic quantization scheme and sampling process. It reviews, unifies and expands several previous works that focused on these effects separately. In addition, it provides a general theoretical background that allows analyzing any digitization scheme including any number of quantization levels, irregular quantization steps, gain compression, clipping, jitter and skew effects of the sampling period

Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions

International audienceAgricultural landscape homogenization has detrimental effects on biodiversity and key ecosystem services. Increasing agricultural landscape heterogeneity by increasing seminatural cover can help to mitigate biodiversity loss. However, the amount of seminatural cover is generally low and difficult to increase in many intensively managed agricultural landscapes. We hypothesized that increasing the heterogeneity of the crop mosaic itself (hereafter “crop heterogeneity”) can also have positive effects on biodiversity. In 8 contrasting regions of Europe and North America, we selected 435 landscapes along independent gradients of crop diversity and mean field size. Within each landscape, we selected 3 sampling sites in 1, 2, or 3 crop types. We sampled 7 taxa (plants, bees, butterflies, hoverflies, carabids, spiders, and birds) and calculated a synthetic index of multitrophic diversity at the landscape level. Increasing crop heterogeneity was more beneficial for multitrophic diversity than increasing seminatural cover. For instance, the effect of decreasing mean field size from 5 to 2.8 ha was as strong as the effect of increasing seminatural cover from 0.5 to 11%. Decreasing mean field size benefited multitrophic diversity even in the absence of seminatural vegetation between fields. Increasing the number of crop types sampled had a positive effect on landscape-level multitrophic diversity. However, the effect of increasing crop diversity in the landscape surrounding fields sampled depended on the amount of seminatural cover. Our study provides large-scale, multitrophic, cross-regional evidence that increasing crop heterogeneity can be an effective way to increase biodiversity in agricultural landscapes without taking land out of agricultural production

Incidence, Clinical Characteristics and Management of Inflammatory Bowel Disease in Spain : Large-Scale Epidemiological Study

(1) Aims: To assess the incidence of inflammatory bowel disease (IBD) in Spain, to describe the main epidemiological and clinical characteristics at diagnosis and the evolution of the disease, and to explore the use of drug treatments. (2) Methods: Prospective, population-based nationwide registry. Adult patients diagnosed with IBD-Crohn's disease (CD), ulcerative colitis (UC) or IBD unclassified (IBD-U)-during 2017 in Spain were included and were followed-up for 1 year. (3) Results: We identified 3611 incident cases of IBD diagnosed during 2017 in 108 hospitals covering over 22 million inhabitants. The overall incidence (cases/100,000 person-years) was 16 for IBD, 7.5 for CD, 8 for UC, and 0.5 for IBD-U; 53% of patients were male and median age was 43 years (interquartile range = 31-56 years). During a median 12-month follow-up, 34% of patients were treated with systemic steroids, 25% with immunomodulators, 15% with biologics and 5.6% underwent surgery. The percentage of patients under these treatments was significantly higher in CD than UC and IBD-U. Use of systemic steroids and biologics was significantly higher in hospitals with high resources. In total, 28% of patients were hospitalized (35% CD and 22% UC patients, p < 0.01). (4) Conclusion: The incidence of IBD in Spain is rather high and similar to that reported in Northern Europe. IBD patients require substantial therapeutic resources, which are greater in CD and in hospitals with high resources, and much higher than previously reported. One third of patients are hospitalized in the first year after diagnosis and a relevant proportion undergo surgery