Stefania Bonafoni

Satellite and Ground-Based Sensors for the Urban Heat Island Analysis in the City of Rome

In this work, the trend of the Urban Heat Island (UHI) of Rome is analyzed by both ground-based weather stations and a satellite-based infrared sensor. First, we have developed a suitable algorithm employing satellite brightness temperatures for the estimation of the air temperature belonging to the layer of air closest to the surface. UHI spatial characteristics have been assessed using air temperatures measured by both weather stations and brightness temperature maps from the Advanced Along Track Scanning Radiometer (AATSR) on board ENVISAT polar-orbiting satellite. In total, 634 daytime and nighttime scenes taken between 2003 and 2006 have been processed. Analysis of the Canopy Layer Heat Island (CLHI) during summer months reveals a mean growth in magnitude of 3–4 K during nighttime and a negative or almost zero CLHI intensity during daytime, confirmed by the weather stations.

Mapping the atmospheric water vapor by integrating microwave radiometer and GPS measurements

This paper deals with a procedure to generate maps of the integrated precipitable water vapor (IPWV) over the Mediterranean area by using estimates from a global positioning system (GPS) network over land and from the Special Sensor Microwave/Imager (SSM/I) over sea. In particular, we investigate the application of the kriging geostatistical technique to obtain regularly spaced IPWV values. The horizontal spatial structure of water vapor retrieved by SSM/I is explored by computing variograms that provide a measure of dissimilarity between pairs of IPWV values for the region of interest. Because the water vapor density decreases with height, the GPS station elevation is accounted for in the interpolation procedure. In this respect, the potential of the kriging with external drift relative to the ordinary kriging is evaluated by applying a test based on the cross-validation approach. Case studies are presented and qualitatively compared to the corresponding Meteosat infrared images. A quantitative comparison with an independent source of information, such as IPWV computed from radiosonde observations and from European Centre for Medium-Range Weather Forecasts analysis, is also performed.

A Low-Cost Microwave Radiometer for the Detection of Fire in Forest Environments

This paper deals with the development of a microwave noise-adding radiometer, which is purposely designed for the fire detection in forest environments. The sensor operates at 12.65 GHz and exploits commercial Satellite Television (SAT-TV) components such as a parabolic dish and a low-noise block. First, a simple system model is presented to estimate the radiometric contrast due to the presence of fire (increase in the antenna noise temperature with respect to the background) at a certain distance from the receiving antenna. Then, the design of the sensor is addressed, underlining the key technologies that allow the required performance to be attained at low industrial costs. An experimental characterization of the developed radiometer is reported focusing on both accuracy and sensitivity issues. Due to a continuous gain calibration based on the noise-adding procedure, the antenna noise temperature can be retrieved with an absolute error of 4 K without any thermal stabilization of the instrument electronics. Last, real fire detection experiments have been carried out both in laboratory and open-space environments. A radiometric contrast of 8.8 K has been recorded for a wooden fire of 0.2 placed at a distance of about 30 m from the antenna.

Fire Detection by Microwave Radiometric Sensors: Modeling a Scenario in the Presence of Obstacles

This paper deals with the problem of fire detection in the presence of obstacles that are nontransparent to visible or infrared wavelengths. Exploiting the obstacle penetration capability of microwaves, a solution based on passive microwave radiometry has been proposed. To investigate such a solution, a theoretical model of the scene sensed by a microwave radiometer is developed, accounting for the presence of both fire spot and wall-like obstacles. By reversing the models equations, it is possible to directly relate the obstacle emissivity, reflectivity, and transmissivity to the antenna noise temperatures measured in several conditions. These temperatures have been sensed with a portable low-cost instrument. The selected 12.65-GHz operation frequency features good wall penetration capability to be balanced with a reasonable antenna size. In order to verify the aforementioned model, several fire experiments have been carried out, resulting in an overall good agreement between measurements and developed theory. In particular, a 2-cm-thick plasterboard wall, typically used for indoor building construction, shows a transmissivity equal to 0.86 and can easily be penetrated by a microwave radiometer in the X-band.

Downscaling of Landsat and MODIS Land Surface Temperature Over the Heterogeneous Urban Area of Milan

Remotely sensed images of land surface temperature (LST) with high spatial resolution are required for various environmental applications. For instance, finer resolutions (FRs) are essential to capture thermal details in urban textures. To meet the requirements of sharper and sharper images, this study carries out a downscaling from coarser spatial resolution LST images to FRs using relationships between LST and spectral indexes (SIs) representative of different land cover types over the heterogeneous area of Milan. Different regressive schemes were applied to downscale LST of Landsat Thematic Mapper (TM) and Terra MODIS images during four summer passages. The regressions were first evaluated on Landsat images aggregated at 960 m resolution and downscaled to 480, 240, and 120 m. For the four Landsat scenes, the best regression models include both vegetation and built-up/soil SIs: the root mean square (rms) error, around 1 K for 480 m and 2 K for 120 m, is clearly below the LST standard deviation of each reference image, assumed as LST spatial variability. Then, contemporary MODIS data were downscaled from 960 m to the above FRs, and the best models include again both vegetation and built-up/soil SIs. The rms error is higher than the correspondent Landsat one (in some cases exceeds 3 K), but always below the LST spatial variability. A compression of the range of LST values for the MODIS-downscaled images was found with respect to the Landsat disaggregated images: this shortcoming in the LST retrieval affects the MODIS downscaling accuracy.

Satellite-Based Retrieval of Precipitable Water Vapor Over Land by Using a Neural Network Approach

A method based on neural networks is proposed to retrieve integrated precipitable water vapor (IPWV) over land from brightness temperatures measured by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). Water vapor values provided by European Centre for Medium-Range Weather Forecasts (ECMWF) were used to train the network. The performance of the network was demonstrated by using a separate data set of AMSR-E observations and the corresponding IPWV values from ECMWF. Our study was optimized over two areas in Northern and Central Italy. Good agreements on the order of 0.24 cm and 0.33 cm rms, respectively, were found between neural network retrievals and ECMWF IPWV data during clear-sky conditions. In the presence of clouds, an rms of the order of 0.38 cm was found for both areas. In addition, results were compared with the IPWV values obtained from in situ instruments, a ground-based radiometer, and a global positioning system (GPS) receiver located in Rome, and a local network of GPS receivers in Como. An rms agreement of 0.34 cm was found between the ground-based radiometer and the neural network retrievals, and of 0.35 cm and 0.40 cm with the GPS located in Rome and Como, respectively.

Retrieving atmospheric temperature profiles by microwave radiometry using a priori information on atmospheric spatial-temporal evolution

A new approach is presented to determine atmospheric temperature profiles by combining measurements coming from different sources and taking into account evolution models derived by conventional meteorological observations. Using a historical database of atmospheric parameters and related microwave brightness temperatures, the authors have developed a data assimilation procedure based on the geostatistical Kriging method and the Kalman filtering suitable for processing satellite radiometric measurements available at each satellite pass, data of a ground-based radiometer, and temperature profiles from radiosondes released at specific times and locations. The Kalman filter technique and the geostatistical Kriging method as well as the principal component analysis have proved very powerful in exploiting climatological a priori information to build spatial and temporal evolution models of the atmospheric temperature field. The use of both historical radiosoundings (RAOBs) and a radiative transfer code allowed the estimation of the statistical parameters that appears in the models themselves (covariance and cross-covariance matrices, observation matrix, etc.). The authors have developed an algorithm, based on a Kalman filter supplemented with a Kriging geostatistical interpolator, that shows a significant improvement of accuracy in vertical profile estimations with respect to the results of a standard Kalman filter when applied to real satellite radiometric data.

Development of a Low-Cost Microwave Radiometer for the Early Detection of Fire in Forest Environments

This paper deals with the development of a microwave, noise-adding radiometer, purposely designed for the fire detection in forest environments. The sensor operates at 12.65GHz and exploits commercial TV-SAT components such as a parabolic dish and a low-noise down-converter. First, an electromagnetic model is presented in order to estimate the radiometric contrast (increase of the brightness temperature with respect to the background) due to a fire covered by vegetation. Then, the design of the sensor is addressed, underlining the key technologies that allow the required performances to be attained at low industrial costs

Microwave Radiometry Imaging for Forest Fire Detection: a Simulation Study

This paper deals with passive microwave imaging for flre detection by means of a single-channel ground-based radiometer. The simulation of images sensed in the presence of flre spots under difierent environmental and operative conditions will be presented. We will refer to a low-cost ground-based radiometer operating at 12.65GHz. Scenarios where flres are un-visible and IR sensors are not useful with respect to a microwave imager will be investigated in deep, such as in the presence of vegetation canopy optically masking flre sources and smoke plumes in the early stage. These simulations will assess limits and capabilities of microwave imaging for the identiflcation of little flres masked by forest areas.

Neural Networks for Arctic Atmosphere Sounding From Radio Occultation Data

This paper illustrates a procedure for the retrieval of tropospheric profiles (temperature, pressure, and humidity) using only refractivity profiles coming from Global Positioning System (GPS)-low-Earth-orbit radio occultation, without the constraint of independent knowledge of atmospheric parameters at each GPS occultation. In order to achieve this goal, we have used an approach based on neural networks (NNs), exploiting a data set of 1106 occultations collected over the Arctic region during the winter season of 2007 and 2008. Total refractivity (N) profiles from Formosa Satellite 3 (FORMOSAT-3)/Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) satellites have been used as input for training the NNs, whereas the target profiles of dry and wet components (Nd and Nw) were derived using prior information on dry and wet fractions of the total refractivity provided by the analysis of the European Centre for Medium-Range Weather Forecast (ECMWF). Once we have retrieved Nd and Nw by the trained networks, the other atmospheric parameters (pressure, temperature, and vapor) can be computed, and we have done so relative to colocated ECMWF data, which we have assumed as atmospheric truth. Finally, some comparisons with radiosonde observations (RAOBs) are shown, and performances and potential of the proposed approach are discussed. Profiles computed using 1-D variational retrieval by the COSMIC Data Analysis and Archive Center have also been considered as a benchmark in the RAOB comparison.