1,492 research outputs found

    In-depth verification of Sentinel-1 and TerraSAR-X geolocation accuracy using the Australian Corner Reflector Array

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    This article shows how the array of corner reflectors (CRs) in Queensland, Australia, together with highly accurate geodetic synthetic aperture radar (SAR) techniques—also called imaging geodesy—can be used to measure the absolute and relative geometric fidelity of SAR missions. We describe, in detail, the end-to-end methodology and apply it to TerraSAR-X Stripmap (SM) and ScanSAR (SC) data and to Sentinel-1interferometric wide swath (IW) data. Geometric distortions within images that are caused by commonly used SAR processor approximations are explained, and we show how to correct them during postprocessing. Our results, supported by the analysis of 140 images across the different SAR modes and using the 40 reflectors of the array, confirm our methodology and achieve the limits predicted by theory for both Sentinel-1 and TerraSAR-X. After our corrections, the Sentinel-1 residual errors are 6 cm in range and 26 cm in azimuth, including all error sources. The findings are confirmed by the mutual independent processing carried out at University of Zurich (UZH) and German Aerospace Center (DLR). This represents an improveïżœment of the geolocation accuracy by approximately a factor of four in range and a factor of two in azimuth compared with the standard Sentinel-1 products. The TerraSAR-X results are even better. The achieved geolocation accuracy now approaches that of the global navigation satellite system (GNSS)-based survey of the CRs positions, which highlights the potential of the end-to-end SAR methodology for imaging geodesy

    Pre-Flight SAOCOM-1A SAR Performance Assessment by Outdoor Campaign

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    In the present paper, we describe the design, execution, and the results of an outdoor experimental campaign involving the Engineering Model of the first of the two Argentinean L-band Synthetic Aperture Radars (SARs) of the Satelite Argentino de Observacion con Microondas (SAOCOM) mission, SAOCOM-1A. The experiment's main objectives were to test the end-to-end SAR operation and to assess the instrument amplitude and phase stability as well as the far-field antenna pattern, through the illumination of a moving target placed several kilometers away from the SAR. The campaign was carried out in Bariloche, Argentina, during June 2016. The experiment was successful, demonstrating an end-to-end readiness of the SAOCOM-SAR functionality in realistic conditions. The results showed an excellent SAR signal quality in terms of amplitude and phase stability

    Investigating rapid deforestation and carbon dioxide release in Bangladesh using geospatial information from remote sensing data

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    Rapid deforestation over the last few years due to the massive influx of refugees from neighboring Myanmar has been reported and is seen as a precursor to environmental disaster, raising the need for more effective monitoring of forest areas. The availability of data from several space-borne synthetic aperture radar (SAR) missions allow enhanced monitoring of forest areas. The objective of this study was to map deforestation in two selected areas located in northeast and southeast Bangladesh using Sentinel-1 imageries and determine the applicability of SAR in forest monitoring in Bangladesh. Towards these purpose satellite imageries from 2017 and 2018 collected by Sentinel-1A and Sentinel-1 Band SAR data in dual-polarization mode were used. In the northeastern area of interest, temporary deforestation was detected, which had occurred in low lying areas due to prolonged flooding. The second area of interest, in the southeast, revealed man-made deforestation in high land areas on an immense scale due to the influx and settlement of seven hundred thousand refugees. The results of the two sub-studies demonstrate the applicability and need of SAR data to effectively monitor deforestation in Bangladesh especially as it allows isolating natural and anthropogenic deforestation

    Désagrégation de l'humidité du sol issue des produits satellitaires micro-ondes passives et exploration de son utilisation pour l'amélioration de la modélisation et la prévision hydrologique

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    De plus en plus de produits satellitaires en micro-ondes passives sont disponibles. Cependant, leur large rĂ©solution spatiale (25-50 km) n’en font pas un outil adĂ©quat pour des applications hydrologiques Ă  une Ă©chelle locale telles que la modĂ©lisation et la prĂ©vision hydrologiques. Dans de nombreuses Ă©tudes, une dĂ©sagrĂ©gation d’échelle de l’humiditĂ© du sol des produits satellites micro-ondes est faite puis validĂ©e avec des mesures in-situ. Toutefois, l’utilisation de ces donnĂ©es issues d’une dĂ©sagrĂ©gation d’échelle n’a pas encore Ă©tĂ© pleinement Ă©tudiĂ©e pour des applications en hydrologie. Ainsi, l’objectif de cette thĂšse est de proposer une mĂ©thode de dĂ©sagrĂ©gation d’échelle de l’humiditĂ© du sol issue de donnĂ©es satellitaires en micro-ondes passives (Satellite Passive Microwave Active and Passive - SMAP) Ă  diffĂ©rentes rĂ©solutions spatiales afin d’évaluer leur apport sur l’amĂ©lioration potentielle des modĂ©lisations et prĂ©visions hydrologiques. À partir d’un modĂšle de forĂȘt alĂ©atoire, une dĂ©sagrĂ©gation d’échelle de l’humiditĂ© du sol de SMAP l’amĂšne de 36-km de rĂ©solution initialement Ă  des produits finaux Ă  9-, 3- et 1-km de rĂ©solution. Les prĂ©dicteurs utilisĂ©s sont Ă  haute rĂ©solution spatiale et de sources diffĂ©rentes telles que Sentinel-1A, MODIS et SRTM. L'humiditĂ© du sol issue de cette dĂ©sagrĂ©gation d’échelle est ensuite assimilĂ©e dans un modĂšle hydrologique distribuĂ© Ă  base physique pour tenter d’amĂ©liorer les sorties de dĂ©bit. Ces expĂ©riences sont menĂ©es sur les bassins versants des riviĂšres Susquehanna (de grande taille) et Upper-Susquehanna (en comparaison de petite taille), tous deux situĂ©s aux États-Unis. De plus, le modĂšle assimile aussi des donnĂ©es d’humiditĂ© du sol en profondeur issue d’une extrapolation verticale des donnĂ©es SMAP. Par ailleurs, les donnĂ©es d’humiditĂ© du sol SMAP et les mesures in-situ sont combinĂ©es par la technique de fusion conditionnelle. Ce produit de fusion SMAP/in-situ est assimilĂ© dans le modĂšle hydrologique pour tenter d’amĂ©liorer la prĂ©vision hydrologique sur le bassin versant Au Saumon situĂ© au QuĂ©bec. Les rĂ©sultats montrent que l'utilisation de l’humiditĂ© du sol Ă  fine rĂ©solution spatiale issue de la dĂ©sagrĂ©gation d’échelle amĂ©liore la reprĂ©sentation de la variabilitĂ© spatiale de l’humiditĂ© du sol. En effet, le produit Ă  1- km de rĂ©solution fournit plus de dĂ©tails que les produits Ă  3- et 9-km ou que le produit SMAP de base Ă  36-km de rĂ©solution. De mĂȘme, l’utilisation du produit de fusion SMAP/ in-situ amĂ©liore la qualitĂ© et la reprĂ©sentation spatiale de l’humiditĂ© du sol. Sur le bassin versant Susquehanna, la modĂ©lisation hydrologique s’amĂ©liore avec l’assimilation du produit de dĂ©sagrĂ©gation d’échelle Ă  9-km, sans avoir recours Ă  des rĂ©solutions plus fines. En revanche, sur le bassin versant Upper-Susquehanna, c’est le produit avec la rĂ©solution spatiale la plus fine Ă  1- km qui offre les meilleurs rĂ©sultats de modĂ©lisation hydrologique. L’assimilation de l’humiditĂ© du sol en profondeur issue de l’extrapolation verticale des donnĂ©es SMAP n’amĂ©liore que peu la qualitĂ© du modĂšle hydrologique. Par contre, l’assimilation du produit de fusion SMAP/in-situ sur le bassin versant Au Saumon amĂ©liore la qualitĂ© de la prĂ©vision du dĂ©bit, mĂȘme si celle-ci n’est pas trĂšs significative.Abstract: The availability of satellite passive microwave soil moisture is increasing, yet its spatial resolution (i.e., 25-50 km) is too coarse to use for local scale hydrological applications such as streamflow simulation and forecasting. Many studies have attempted to downscale satellite passive microwave soil moisture products for their validation with in-situ soil moisture measurements. However, their use for hydrological applications has not yet been fully explored. Thus, the objective of this thesis is to downscale the satellite passive microwave soil moisture (i.e., Satellite Microwave Active and Passive - SMAP) to a range of spatial resolutions and explore its value in improving streamflow simulation and forecasting. The random forest machine learning technique was used to downscale the SMAP soil moisture from 36-km to 9-, 3- and 1-km spatial resolutions. A combination of host of high-resolution predictors derived from different sources including Sentinel-1A, MODIS and SRTM were used for downscaling. The downscaled SMAP soil moisture was then assimilated into a physically-based distributed hydrological model for improving streamflow simulation for Susquehanna (larger in size) and Upper Susquehanna (relatively smaller in size) watersheds, located in the United States. In addition, the vertically extrapolated SMAP soil moisture was assimilated into the model. On the other hand, the SMAP and in-situ soil moisture were merged using the conditional merging technique and the merged SMAP/in-situ soil moisture was then assimilated into the model to improve streamflow forecast over the au Saumon watershed. The results show that the downscaling improved the spatial variability of soil moisture. Indeed, the 1-km downscaled SMAP soil moisture presented a higher spatial detail of soil moisture than the 3-, 9- or original resolution (36-km) SMAP product. Similarly, the merging of SMAP and in-situ soil moisture improved the accuracy as well as spatial representation soil moisture. Interestingly, the assimilation of the 9-km downscaled SMAP soil moisture significantly improved the accuracy of streamflow simulation for the Susquehanna watershed without the need of going to higher spatial resolution, whereas for the Upper Susquehanna watershed the 1-km downscaled SMAP showed better results than the coarser resolutions. The assimilation of vertically extrapolated SMAP soil moisture only slightly further improved the accuracy of the streamflow simulation. On the other hand, the assimilation of merged SMAP/in-situ soil moisture for the au Saumon watershed improved the accuracy of streamflow forecast, yet the improvement was not that significant. Overall, this study demonstrated the potential of satellite passive microwave soil moisture for streamflow simulation and forecasting

    Global evaluation of SMAP/Sentinel-1 soil moisture products

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    MAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types.Peer ReviewedPostprint (published version

    Comparison of Above-Water Seabird and TriOS Radiometers along an Atlantic Meridional Transect

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    The Fiducial Reference Measurements for Satellite Ocean Color (FRM4SOC) project has carried out a range of activities to evaluate and improve the state-of-the-art in ocean color radiometry. This paper described the results from a ship-based intercomparison conducted on the Atlantic Meridional Transect 27 from 23rd September to 5th November 2017. Two different radiometric systems, TriOS-Radiation Measurement Sensor with Enhanced Spectral resolution (RAMSES) and Seabird-Hyperspectral Surface Acquisition System (HyperSAS), were compared and operated side-by-side over a wide range of Atlantic provinces and environmental conditions. Both systems were calibrated for traceability to SI (SystĂšme international) units at the same optical laboratory under uniform conditions before and after the field campaign. The in situ results and their accompanying uncertainties were evaluated using the same data handling protocols. The field data revealed variability in the responsivity between TRiOS and Seabird sensors, which is dependent on the ambient environmental and illumination conditions. The straylight effects for individual sensors were mostly within ±3%. A near infra-red (NIR) similarity correction changed the water-leaving reflectance (ρw) and water-leaving radiance (Lw) spectra significantly, bringing also a convergence in outliers. For improving the estimates of in situ uncertainty, it is recommended that additional characterization of radiometers and environmental ancillary measurements are undertaken. In general, the comparison of radiometric systems showed agreement within the evaluated uncertainty limits. Consistency of in situ results with the available Sentinel-3A Ocean and Land Color Instrument (OLCI) data in the range from (400
560) nm was also satisfactory (-8% < Mean Percentage Difference (MPD) < 15%) and showed good agreement in terms of the shape of the spectra and absolute values

    Modelling Aboveground Biomass of Miombo Woodlands in Niassa Special Reserve, Northern Mozambique

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    Aboveground biomass (AGB) estimation plays a crucial role in forest management and carbon emission reporting, especially for developing countries wishing to address REDD+ projects. Both passive and active remote-sensing technologies can provide spatially explicit information of AGB by using a limited number of field samples, thus reducing the substantial budgetary cost of field inventories. The aim of the current study was to estimate AGB in the Niassa Special Reserve (NSR) using fusion of optical (Landsat 8/OLI and Sentinel 2A/MSI) and radar (Sentinel 1B and ALOS/PALSAR-2) data. The performance of multiple linear regression models to relate ground biomass with different combinations of sensor data was assessed using root-mean-square error (RMSE), and the Akaike and Bayesian information criteria (AIC and BIC). The mean AGB and carbon stock (CS) estimated from field data were estimated at 56 Mg ha−1 (ranging from 11 to 95 Mg ha−1) and 28 MgC ha−1, respectively. The best model estimated AGB at 63 ± 20.3 Mg ha−1 for NSR, ranging from 0.6 to 200 Mg ha−1 (r2 = 87.5%, AIC = 123, and BIC = 51.93). We obtained an RMSE % of 20.46 of the mean field estimate of 56 Mg ha−1. The estimation of AGB in this study was within the range that was reported in the existing literature for the miombo woodlands. The fusion of vegetation indices derived from Landsat/OLI and Sentinel 2A/MSI, and backscatter from ALOS/PALSAR-2 is a good predictor of AGB.info:eu-repo/semantics/publishedVersio

    Chronology of KSC and KSC Related Events for 1996

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    The document is intended to serve as a record of KSC events and is a reference source for historians and other researchers. Arrangement is by day and month and individual articles are attributed to published sources. Materials were researched and described by the KSC Library Archivist for KSC Library Services Contractor Sherikon Space Systems, Inc

    Regulation of prefrontal glutamate by the endogenous neuromodulator kynurenic acid as measured by rapid electrochemistry: relevance to schizophrenia

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    Schizophrenia is one of the most debilitating neuropsychiatric disorders, with a prevalence of approximately 1% worldwide. Symptoms include impairing deficits in cognitive processes (e.g., working memory, attention, and cognitive flexibility) which are currently elusive to treatment. Alpha-7 nicotinic acetylcholine receptors (α7 nAChRs) may be a critical link between the dysregulated cortical glutamatergic transmission and cognitive deficits in schizophrenia. Elevated levels of the endogenous, astrocyte-derived neuromodulator kynurenic acid (KYNA), a potent, non-competitive α7 nAChR antagonist, in the brains and cerebrospinal fluid (CSF) of schizophrenic patients suggests that KYNA binding may cause or exacerbate cortical dysregulation in schizophrenia. Thus, we used a state-of-the-art microelectrode array (MEA) to amperometrically examine the regulation of glutamate release by KYNA in the prefrontal cortex (PFC) of intact, freely moving rats. The MEA allows for the selective quantification of glutamate levels with second-by-second resolution (500-800 ms), a limit of detection of ≀0.5 ÎŒM, and outstanding spatial resolution (<100 ÎŒm). Intraperitoneal (i.p.) injections of L-kynurenine (25 and 50 mg/kg), KYNA’s bioprecursor, dose-dependently decreased basal glutamate levels in PFC (nadir after 50 mg/kg kynurenine: 31% decrease from baseline values). However, co-administration of galantamine (3 mg/kg), a drug that competes with KYNA at an allosteric potentiating site of the α7 nAChR, attenuated kynurenine-mediated reductions in cortical glutamate. These data in intact animals demonstrate KYNA’s α7 nAChR-mediated regulation of glutamate release in the PFC. Drugs that normalize cortical glutamatergic function by attenuating KYNA production or by countering its effects on α7 nAChRs may be effective cognition-enhancing therapeutics in schizophrenic patients.Undergraduate Research Office Fellowship; Sigma Xi Research Fund; Arts & Sciences Undergraduate Research Award; Dean's Biological Research FundNo embarg
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