Search results for "Remote Sensing"

2021

Abstract We propose a signal deconvolution procedure for imaging spectrometer data, where a measured point spread function (PSF) is deconvolved itself before being used for deconvolution of the signal. We evaluate the effectiveness of our procedure for improvement of the spatio-spectral signal, as well as our target application, i.e. estimation of sun-induced fluorescence (SIF). Imaging spectrometers are well established instruments for remote sensing. When used for scientific purposes these instruments are usually calibrated on a regular basis. In our case the point spread function of the optics is measured in an elaborate procedure with a tunable monochromator point light source. PSFs are…

Study of the correlation between columnar aerosol burden, suspended matter at ground and chemical components in a background European environment

2012

Although routinely monitored by ground based air quality networks, the particulate matter distribution could be eventually better described with remote sensing techniques. However, valid relationships between ground level and columnar ground based quantities should be known beforehand. In this study we have performed a comparison between particulate matter measurements at ground level at different cut sizes (10, 2.5 and 1.0 mm), and the aerosol optical depth obtained by means of a ground based sunphotometer during a multiinstrumental field campaign held in El Arenosillo (Huelva, Spain) from 28 June to 4 July 2006. All the PM fractions were very well correlated with AOD with correlation coef…

A more cost-effective geomatic approach to modelling PM10 dispersion across Europe

2014

International audience; PM10 concentrations in most major European cities exceed the limits set by the European Directive and are expected to continue to do so in the coming years. Moreover, PM10s can be transported over long distances impacting air quality, public health and ecosystem functionality far from their sources of emission. Modelling remains one of the only options for tracking PM10 deposition in remote areas with no monitoring stations. Even so, air pollution models based on atmospheric physics usually imply substantial economic, logistical and computational investment. In this work, we present a new geomatic approach to modelling mean annual PM10 concentrations across Europe. O…

Monitoring Mediterranean marine pollution using remote sensing and hydrodynamic modelling

2011

Human activities contaminate both coastal areas and open seas, even though impacts are different in terms of pollutants, ecosystems and recovery time. In particular, Mediterranean offshore pollution is mainly related to maritime transport of oil, accounting for 25% of the global maritime traffic and, during the last 25 years, for nearly 7% of the world oil accidents, thus causing serious biological impacts on both open sea and coastal zone habitats. This paper provides a general review of maritime pollution monitoring using integrated approaches of remote sensing and hydrodynamic modeling; focusing on the main results of the MAPRES (Marine pollution monitoring and detection by aerial survei…

Cover Picture: Macromol. Rapid Commun. 20/2006

2006

Cover Picture: Macromol. Rapid Commun. 11/2005

2005

Cover Picture: Macromol. Rapid Commun. 21/2005

2005

Back Cover: Macromol. Rapid Commun. 6/2007

2007

Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data

2018

The colored dissolved organic matter (CDOM) variable is the standard measure of humic substance in waters optics. CDOM is optically characterized by its spectral absorption coefficient, a C D O M at at reference wavelength (e.g., ≈ 440 nm). Retrieval of CDOM is traditionally done using bio-optical models. As an alternative, this paper presents a comparison of five machine learning methods applied to Sentinel-2 and Sentinel-3 simulated reflectance ( R r s ) data for the retrieval of CDOM: regularized linear regression (RLR), random forest regression (RFR), kernel ridge regression (KRR), Gaussian process regression (GPR) and support vector machines (SVR). Two different datasets of radiative t…

Correcting AVHRR Long Term Data Record V3 estimated LST from orbital drift effects

2012

Abstract NOAA (National Oceanic and Atmospheric Administration) satellite series is known to suffer from what is known as the orbital drift effect. The Long Term Data Record (LTDR [Pedelty et al., 2007]), which provides AVHRR (Advanced Very High Resolution Radiometer) data from these satellites for the 80s and the 90s, is also affected by this orbital drift. To correct this effect on Land Surface Temperature (LST) time series, a novel method is presented here, which consists in adjusting retrieved LST time series on the basis of statistical information extracted from the time series themselves. This method is as simple and straightforward as possible, in order to be implemented easily for s…