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Research indicates that aerosol optical thickness (AOT) values and particulate matter (PM10) measurements can be used as indicators of atmospheric pollution. The problem of relating AOT with suspended particulate matter near the ground is still an open question. While satellite images can provide reliable and synoptic measurements from space, comparisons with monitoring surface level air pollution continues to be a challenge since satellite measurements are column integrated quantities. In this study, in-situ spectroradiometric measurements were taken during satellite overpass using field spectrometers to obtain the reflectance values of the calibration targets used. Sun photometer measurements were taken with the Microtops hand-held sun photometer to measure AOT. Meteorological data was collected from nearby meteorological stations and PM10 measurements were collected from local mobile air pollution stations. Following, the darkest pixel method of atmospheric correction was applied to a series of Landsat satellite images. The reflectance values of the atmospherically-corrected image were used in the radiative transfer equation to solve for AOT. Thematic maps were generated in order to develop air quality indices. The image-derived AOT values were examined for a positive correlation with PM10 measurements. It appears there exists a significant correlation between AOT and PM10 measurements. 2012 SPIE.

Typhoon motoring using Global Navigation Satellite System (GNSS) signals is a new application of the GNSS-R technique, with increasing interest in the last years. Examples of that can be the CYGNSS (Cyclone Global Navigation Satellite System) spaceborne mission proposed by NASA, or the TIGRIS (Typhoon Investigation using GNSS-R Interferometric Signals) experiment, conducted in the framework of ESA-China cooperation. This paper focuses on the TIGRIS experiment, presenting the preliminary results obtained using the interferometric GNSS-R (iGNSS-R) technique. Peer Reviewed

International audience; This paper sets out the methods and the technologies used to design a captive portal to redirect users to the URL (Uniform Resource Locator) of a course taking place in a given room. The captive portal is designed on a Raspberry Pi 2 carrying an Apache HTTP (HyperText Transfer Protocol) server and using iptables for redirections. It has a web configuration interface, developed with AngularJS, which communicates through HTTP request to the server side, developed in PHP, following the principle of a REST (REpresentational State Transfer) architecture. In addition to redirect users to the URL of a course, the interface is configurable in two modes: 1) fixed URL that sets an URL to which the user is redirected automatically, 2) hosting a local website which is used to load a web site in zip format on the device and then redirect users to this web site even when the device is not connected to any Ethernet network.

An important problem in robotics is to determine and maintain the position of a robot that moves through a previously known environment with reference points that are indistinguishable, which is made difficult due to the inherent noise in robot movement and identification of reference pints. Monte Carlo Localization (MCL) is a frequently used technique to solve this problem and its performance intuitively depends on reference points. In this paper we evaluate the performance of MCL as a function of the number of reference points and their positioning in the environment. In particular, we show that performance is not monotonic in the number of reference points and that a random positioning of the reference points is close to optimal.

The standard deviation of the Horizontal Wind Speed as a proxy of wind turbulence is used to compare the apparent wind turbulence measured by an off-shore floating Doppler lidar to the one measured by a fixed lidar on a metmast. We use statistical analysis based on clustering the horizontal wind speed measured by the floating lidar as well as buoy angular amplitude and period under the approximation of harmonic motion. Three scenarios with different wave and wind conditions are discussed from the IJmuiden's test campaign (North Sea.). This work was supported via Spanish Government–European Regional Development Funds project PGC2018-094132-B-I00 and EU H2020 ACTRIS-2 (GA 654109). The European Institute of Innovation and Technology (EIT), KIC InnoEnergy project NEPTUNE (Offshore Metocean Data Measuring Equipment and Wind, Wave and Current Analysis and Forecasting Software, call FP7) supported measurements campaigns. CommSensLab is a María-de-Maeztu Unit of Excellence funded by the Agencia Estatal de Investigacion (Spanish National Science Foundation). Peer Reviewed

The objective of this paper is to present an operational approach for mapping soil moisture at high spatial resolution over agricultural areas with vegetation cover. The developed approach uses the neural network (NN) technique based on coupling Sentinel-1 radar data and Sentinel-2 optical data. The neural networks were developed and validated using synthetic and real databases. To operationally map the soil moisture, the developed NN uses the C-band SAR signal in VV polarization, SAR incidence angle, and the Normalized Differential Vegetation Index “NDVI” as the inputs. To optimize the automatic production of soil moisture maps a pipeline using the Orfeo Toolbox is implemented.

As a result of climate change in agriculture, certain changes are taking place that affect the economic component of Ukraine in the domestic and foreign markets. The statistical analysis of the time series of the majority crops areas for 22 years (from 1998 to 2020) is carried out, and also the influence of agroclimatic zones on the area of cultivation of the main crops is analyzed.