Search results for "Sensing"
showing 10 items of 1698 documents
Feasibility of hyperspectral vegetation indices for the detection of chlorophyll concentration in three high Arctic plants: Salix polaris, Bistorta v…
2018
Remote sensing, which is based on a reflected electromagnetic spectrum, offers a wide range of research methods. It allows for the identification of plant properties, e.g., chlorophyll, but a registered signal not only comes from green parts but also from dry shoots, soil, and other objects located next to the plants. It is, thus, important to identify the most applicable remote-acquired indices for chlorophyll detection in polar regions, which play a primary role in global monitoring systems but consist of areas with high and low accessibility. This study focuses on an analysis of in situ-acquired hyperspectral properties, which was verified by simultaneously measuring the chlorophyll conc…
Automated detection and classification of synoptic scale fronts from atmospheric data grids
2021
<p>Automatic determination of fronts from atmospheric data is an important task for weather prediction as well as for research of synoptic scale phenomena. We developed a deep neural network to detect and classify fronts from multi-level ERA5 reanalysis data. Model training and prediction is evaluated using two different regions covering Europe and North America with data from two weather services. Due to a label deformation step performed during training we are able to directly generate frontal lines with no further thinning during post processing. Our network compares well against the weather service labels with a Critical Success Index higher than 66.9% and a Object Detecti…
A Review of Kernel Methods in Remote Sensing Data Analysis
2011
Kernel methods have proven effective in the analysis of images of the Earth acquired by airborne and satellite sensors. Kernel methods provide a consistent and well-founded theoretical framework for developing nonlinear techniques and have useful properties when dealing with low number of (potentially high dimensional) training samples, the presence of heterogenous multimodalities, and different noise sources in the data. These properties are particularly appropriate for remote sensing data analysis. In fact, kernel methods have improved results of parametric linear methods and neural networks in applications such as natural resource control, detection and monitoring of anthropic infrastruc…
Cloud screening with combined MERIS and AATSR images
2009
This paper presents a cloud screening algorithm based on ensemble methods that exploits the combined information from both MERIS and AATSR instruments on board ENVISAT in order to improve current cloud masking products for both sensors. The first step is to analyze the synergistic use of MERIS and AATSR images in order to extract some physically-based features increasing the separability of clouds and surface. Then, several artificial neural networks are trained using different sets of input features and different sets of training samples depending on acquisition and surface conditions. Finally, outputs of the trained neural networks are combined at the decision level to construct a more ac…
Polarization calibration techniques for the new-generation VLBI
2020
The calibration and analysis of polarization observations in Very Long Baseline Interferometry (VLBI) requires the use of specific algorithms that suffer from several limitations, closely related to assumptions in the data properties that may not hold in observations taken with new-generation VLBI equipment. Nowadays, the instantaneous bandwidth achievable with VLBI backends can be as high as several GHz, covering several radio bands simultaneously. In addition, the sensitivity of VLBI observations with state-of-the-art equipment may reach dynamic ranges of tens of thousands, both in total intensity and in polarization. In this paper, we discuss the impact of the limitations of common VLBI …
EAS selection in the EMMA underground array
2013
The first measurements of the Experiment with MultiMuon Array (EMMA) have been analyzed for the selection of the Extensive Air Showers (EAS). Test data were recorded with an underground muon tracking station and a satellite station separated laterally by 10 metres. Events with tracks distributed over all of the tracking detector area and even extending over to the satellite station are identified as EAS. The recorded multiplicity spectrum of the events is in general agreement with CORSIKA EAS simulation and demonstrates the array’s capability of EAS detection. peerReviewed
The FRAM robotic telescope for atmospheric monitoring at the Pierre Auger Observatory
2021
FRAM (F/Photometric Robotic Atmospheric Monitor) is a robotic telescope operated at the Pierre Auger Observatory in Argentina for the purposes of atmospheric monitoring using stellar photometry. As a passive system which does not produce any light that could interfere with the observations of the fluorescence telescopes of the observatory, it complements the active monitoring systems that use lasers. We discuss the applications of stellar photometry for atmospheric monitoring at optical observatories in general and the particular modes of operation employed by the Auger FRAM. We describe in detail the technical aspects of FRAM, the hardware and software requirements for a successful operati…
Canopy directional emissivity: Comparison between models
2005
Land surface temperature plays an important role in many environmental studies, as for example the estimation of heat fluxes and evapotranspiration. In order to obtain accurate values of land surface temperature, atmospheric, emissivity and angular effects should be corrected. This paper focuses on the analysis of the angular variation of canopy emissivity, which is an important variable that has to be known to correct surface radiances and obtain surface temperatures. Emissivity is also involved in the atmospheric corrections since it appears in the reflected downwelling atmospheric term. For this purpose, five different methods for simulating directional canopy emissivity have been analyz…
Recovering Surface Temperature and Emissivity from Thermal Infrared Multispectral Data
1998
Abstract In 1992 Thermal Infrared Multispectral Scanner (TIMS) data were acquired from the NASA C-130 aircraft over the Sahelian region of West Africa as part of the Hydrological and Atmospheric Pilot Experiment in the Sahel (HAPEX). TIMS measures the radiation from the surface modified by the atmosphere in six channels located between 8 mm and 12.5 μm in the thermal infrared. By using a variety of techniques it is possible to extract both the surface temperature and surface emissivity from the areas over which TIMS data were acquired. One such technique was tested with the data acquired during this experiment. Several TIMS images of both the east and west central sites on 2 and 4 September…
Long-term accuracy assessment of land surface temperatures derived from the Advanced Along-Track Scanning Radiometer
2012
Abstract The accuracy of land surface temperatures (LSTs) derived from the Advanced Along-Track Scanning Radiometer (AATSR) was assessed in a test site in Valencia, Spain from 2002 to 2008. AATSR LSTs were directly compared with concurrent ground measurements over homogeneous, full-vegetated rice fields in the conventional temperature-based (T-based) method. We also applied the new radiance-based (R-based) method over bare soil and water surfaces, where ground LST measurements were not available. In the R-based method, ground LSTs are simulated from AATSR brightness temperatures in the 11 μm band and radiative transfer simulations using surface emissivity data and atmospheric water vapor an…