Search results for "computer"
showing 10 items of 30657 documents
On numerical broadening of particle size spectra: a condensational growth study using PyMPDATA 1.0
2021
Abstract. The work discusses the diffusional growth in particulate systems such as atmospheric clouds. It focuses on the Eulerian modeling approach in which the evolution of the probability density function describing the particle size spectrum is carried out using a fixed-bin discretization. The numerical diffusion problem inherent to the employment of the fixed-bin discretization is scrutinized. The work focuses on the applications of MPDATA family of numerical schemes. Several MPDATA variants are explored including: infinite-gauge, non-oscillatory, third-order-terms and recursive antidiffusive correction (double pass donor cell, DPDC) options. Methodology for handling coordinate transfor…
Linking photosynthesis and sun-induced fluorescence at sub-daily to seasonal scales
2018
Abstract Due to its close link to the photosynthetic process, sun-induced chlorophyll fluorescence (F) opens new possibilities to study dynamics of photosynthetic light reactions and to quantify CO2 assimilation rates. Although recent studies show that F is linearly related to gross primary production (GPP) on coarse spatial and temporal scales, it is argued that this relationship may be mainly driven by seasonal changes in absorbed photochemical active radiation (APAR) and less by the plant light use efficiency (LUE). In this work a high-resolution spectrometer was used to continuously measure red and far-red fluorescence and different reflectance indices within a sugar beet field during t…
Understanding deep learning in land use classification based on Sentinel-2 time series
2020
AbstractThe use of deep learning (DL) approaches for the analysis of remote sensing (RS) data is rapidly increasing. DL techniques have provided excellent results in applications ranging from parameter estimation to image classification and anomaly detection. Although the vast majority of studies report precision indicators, there is a lack of studies dealing with the interpretability of the predictions. This shortcoming hampers a wider adoption of DL approaches by a wider users community, as model’s decisions are not accountable. In applications that involve the management of public budgets or policy compliance, a better interpretability of predictions is strictly required. This work aims …
Transferring deep learning models for cloud detection between Landsat-8 and Proba-V
2020
Abstract Accurate cloud detection algorithms are mandatory to analyze the large streams of data coming from the different optical Earth observation satellites. Deep learning (DL) based cloud detection schemes provide very accurate cloud detection models. However, training these models for a given sensor requires large datasets of manually labeled samples, which are very costly or even impossible to create when the satellite has not been launched yet. In this work, we present an approach that exploits manually labeled datasets from one satellite to train deep learning models for cloud detection that can be applied (or transferred) to other satellites. We take into account the physical proper…
Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress
2019
Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF – especially from space – is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using high-resolution spectral sensors in …
Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis
2015
In this paper we present an approach to perform relative spectral alignment between optical cross-sensor acquisitions. The proposed method aims at projecting the images from two different and possibly disjoint input spaces into a common latent space, in which standard change detection algorithms can be applied. The system relies on the regularized kernel canonical correlation analysis transformation (kCCA), which can accommodate nonlinear dependencies between pixels by means of kernel functions. To learn the projections, the method employs a subset of samples belonging to the unchanged areas or to uninteresting radiometric differences. Since the availability of ground truth information to p…
Validation of HF radar sea surface currents in the Malta-Sicily Channel
2019
Abstract A network of High-Frequency radar (HFR) stations runs operationally in the Malta-Sicily Channel (MSC), Central Mediterranean Sea, providing sea surface current maps with high temporal (1 h) and spatial (3 × 3 km) resolutions since August 2012. Comparisons with surface drifter data and near-surface Acoustic Doppler Current Profiler (ADCP) observations, as well as radar site-to-site baseline analyses, provide quantitative assessments of HFR velocities accuracy. Twenty-two drifters were deployed within the HFR domain of coverage between December 2012 and October 2013. Additionally, six ADCP vertical current profiles were collected at selected positions during a dedicated field survey.…
Geophysical prospection of the Roman city of Pollentia, Alcúdia (Mallorca, Balearic Islands, Spain)
2016
Abstract We present the results of the geophysical investigation carried out in the Roman city of Pollentia, in the island of Mallorca. The ancient city was identified in the 19th century. Old and new archaeological excavations have helped to uncover a residential area, a theatre, the forum, several necropolises and other remains of the city, but a large unexplored area has still to be investigated. For instance, the limits of the ancient town and the presence of harbour structures are still unknown. The geophysical survey has covered an area of more than 20.000 m2 by integrating magnetic, electromagnetic, electrical and ground penetrating radar (GPR) methods. Many unseen archaeological fea…
The river harbour of Ostia Antica - stratigraphy, extent and harbour infrastructure from combined geophysical measurements and drillings
2018
Abstract We performed a combined geophysical and geoarchaeological survey of the harbour of ancient Ostia, Italy, to investigate the extent of the former harbour basin, the sedimentary infill and possible building remains around the harbour. Besides geoarchaeological results the paper highlights the advantage of combining vibracore drilling with different geophysical prospection methods, which are sensitive to different physical soil parameters. Geophysical methods applied were electrical resistivity tomography (ERT), ground penetrating radar (GPR) and seismics with shear and compressional waves. The extent and shape of the harbour basin were determined by ERT profiling. The ERT profiles we…
High spatio- temporal resolution land surface temperature mission - a copernicus candidate mission in support of agricultural monitoring
2018
International audience; Evolution in the Copernicus Space Component (CSC) is foreseen in the mid-2020s to meet priority Copernicus user needs not addressed by the existing infrastructure, and/or to reinforce services by monitoring capability in the thematic domains of CO 2 , polar, and agriculture/forestry. This evolution will be synergetic with the enhanced continuity of services for the next generation of CSC. The “High Spatio-Temporal Resolution Land Surface Temperature Monitoring (LSTM) Mission”, identified as one of the CSC Expansion High Priority Candidate Missions (HPCM), currently undergoes an ESA preparatory phase (phase A/B1) study to establish mission feasibility. The LSTM missio…