949 resultados para Radar meteorology


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In this thesis, the first-order radar cross section (RCS) of an iceberg is derived and simulated. This analysis takes place in the context of a monostatic high frequency surface wave radar with a vertical dipole source that is driven by a pulsed waveform. The starting point of this work is a general electric field equation derived previ- ously for an arbitrarily shaped iceberg region surrounded by an ocean surface. The condition of monostatic backscatter is applied to this general field equation and the resulting expression is inverse Fourier transformed. In the time domain the excitation current of the transmit antenna is specified to be a pulsed sinusoid signal. The result- ing electric field equation is simplified and its physical significance is assessed. The field equation is then further simplified by restricting the iceberg's size to fit within a single radar patch width. The power received by the radar is calculated using this electric field equation. Comparing the received power with the radar range equation gives a general expression for the iceberg RCS. The iceberg RCS equation is found to depend on several parameters including the geometry of the iceberg, the radar frequency, and the electrical parameters of both the iceberg and the ocean surface. The RCS is rewritten in a form suitable for simulations and simulations are carried out for rectangularly shaped icebergs. Simulation results are discussed and are found to be consistent with existing research.

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Inscriptions: Verso: [stamped] Photograph by Freda Leinwand. [463 West Street, Studio 229G, New York, NY 10014].

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The amount and quality of available biomass is a key factor for the sustainable livestock industry and agricultural management related decision making. Globally 31.5% of land cover is grassland while 80% of Ireland’s agricultural land is grassland. In Ireland, grasslands are intensively managed and provide the cheapest feed source for animals. This dissertation presents a detailed state of the art review of satellite remote sensing of grasslands, and the potential application of optical (Moderate–resolution Imaging Spectroradiometer (MODIS)) and radar (TerraSAR-X) time series imagery to estimate the grassland biomass at two study sites (Moorepark and Grange) in the Republic of Ireland using both statistical and state of the art machine learning algorithms. High quality weather data available from the on-site weather station was also used to calculate the Growing Degree Days (GDD) for Grange to determine the impact of ancillary data on biomass estimation. In situ and satellite data covering 12 years for the Moorepark and 6 years for the Grange study sites were used to predict grassland biomass using multiple linear regression, Neuro Fuzzy Inference Systems (ANFIS) models. The results demonstrate that a dense (8-day composite) MODIS image time series, along with high quality in situ data, can be used to retrieve grassland biomass with high performance (R2 = 0:86; p < 0:05, RMSE = 11.07 for Moorepark). The model for Grange was modified to evaluate the synergistic use of vegetation indices derived from remote sensing time series and accumulated GDD information. As GDD is strongly linked to the plant development, or phonological stage, an improvement in biomass estimation would be expected. It was observed that using the ANFIS model the biomass estimation accuracy increased from R2 = 0:76 (p < 0:05) to R2 = 0:81 (p < 0:05) and the root mean square error was reduced by 2.72%. The work on the application of optical remote sensing was further developed using a TerraSAR-X Staring Spotlight mode time series over the Moorepark study site to explore the extent to which very high resolution Synthetic Aperture Radar (SAR) data of interferometrically coherent paddocks can be exploited to retrieve grassland biophysical parameters. After filtering out the non-coherent plots it is demonstrated that interferometric coherence can be used to retrieve grassland biophysical parameters (i. e., height, biomass), and that it is possible to detect changes due to the grass growth, and grazing and mowing events, when the temporal baseline is short (11 days). However, it not possible to automatically uniquely identify the cause of these changes based only on the SAR backscatter and coherence, due to the ambiguity caused by tall grass laid down due to the wind. Overall, the work presented in this dissertation has demonstrated the potential of dense remote sensing and weather data time series to predict grassland biomass using machine-learning algorithms, where high quality ground data were used for training. At present a major limitation for national scale biomass retrieval is the lack of spatial and temporal ground samples, which can be partially resolved by minor modifications in the existing PastureBaseIreland database by adding the location and extent ofeach grassland paddock in the database. As far as remote sensing data requirements are concerned, MODIS is useful for large scale evaluation but due to its coarse resolution it is not possible to detect the variations within the fields and between the fields at the farm scale. However, this issue will be resolved in terms of spatial resolution by the Sentinel-2 mission, and when both satellites (Sentinel-2A and Sentinel-2B) are operational the revisit time will reduce to 5 days, which together with Landsat-8, should enable sufficient cloud-free data for operational biomass estimation at a national scale. The Synthetic Aperture Radar Interferometry (InSAR) approach is feasible if there are enough coherent interferometric pairs available, however this is difficult to achieve due to the temporal decorrelation of the signal. For repeat-pass InSAR over a vegetated area even an 11 days temporal baseline is too large. In order to achieve better coherence a very high resolution is required at the cost of spatial coverage, which limits its scope for use in an operational context at a national scale. Future InSAR missions with pair acquisition in Tandem mode will minimize the temporal decorrelation over vegetation areas for more focused studies. The proposed approach complements the current paradigm of Big Data in Earth Observation, and illustrates the feasibility of integrating data from multiple sources. In future, this framework can be used to build an operational decision support system for retrieval of grassland biophysical parameters based on data from long term planned optical missions (e. g., Landsat, Sentinel) that will ensure the continuity of data acquisition. Similarly, Spanish X-band PAZ and TerraSAR-X2 missions will ensure the continuity of TerraSAR-X and COSMO-SkyMed.

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Multi-channel ground-penetrating radar is used to investigate the late-summer evolution of the thaw depth and the average soil water content of the thawed active layer at a high-arctic continuous permafrost site on Svalbard, Norway. Between mid of August and mid of September 2008, five surveys have been conducted over transect lengths of 130 and 175 m each. The maximum thaw depths range from 1.6 m to 2.0 m, so that they are among the deepest thaw depths recorded for Svalbard so far. The thaw depths increase by approximately 0.2 m between mid of August and beginning of September and subsequently remain constant until mid of September. The thaw rates are approximately constant over the entire length of the transects within the measurement accuracy of about 5 to 10 cm. The average volumetric soil water content of the thawed soil varies between 0.18 and 0.27 along the investigated transects. While the measurements do not show significant changes in soil water content over the first four weeks of the study, strong precipitation causes an increase in average soil water content of up to 0.04 during the last week. These values are in good agreement with evapotranspiration and precipitation rates measured in the vicinity of the the study site. While we cannot provide conclusive reasons for the detected spatial variability of the thaw depth at the study site, our measurements show that thaw depth and average soil water content are not directly correlated. The study demonstrates the potential of multi-channel ground-penetrating radar for mapping thaw depth in permafrost areas. The novel non-invasive technique is particularly useful when the thaw depth exceeds 1.5 m, so that it is hardly accessible by manual probing. In addition, multi-channel ground-penetrating radar holds potential for mapping the latent heat content of the active layer and for estimating weekly to monthly averages of the ground heat flux during the thaw period.

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We present surface elevations and ice thicknesses along an airborne radar survey made in Eastern Dronning Maud Land. The survey was carried out above 4 major outlet glaciers which flows around Sør Rondane Mountains with AWI's radar mounted on Polar 5 plane. The data were collected between the 21st and the 23th of January 2011. A full description of the data can be found in Callens et al. (see further detatils).

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The Padul-Nigüelas Fault Zone (PNFZ) is situated at the south-western mountain front of the Sierra Nevada (Spain) in an extensive regime and belongs to the internal zone of the Betic Cordilleras. The aim of this study is a collection of new evidence for neotectonic activity of the fault zone with classical geological field work and modern geophysical methods, such as ground penetrating radar (GPR). Among an apparently existing bed rock fault scarp with triangular facets, other evidences, such as deeply incised valleys and faults in the colluvial wedges, are present in the PNFZ. The preliminary results of our recent field work have shown that the synsedimentary faults within the colluvial sediments seem to propagate basinwards and the bed rock fault is only exhumed due to erosion for the studied segment (west of Marchena). We will use further GPR data and geomorphologic indices to gather further evidences of neotectonic activity of the PNFZ.