Post-disaster analysis is an important challenge in disaster risk management during the recovery phase. This study focuses on the benefits of the use of multispectral images by Copernicus Sentinel-2 to assess long-term urban change and to monitor the progress of reconstruction after earthquakes. The innovative and straightforward methodology aims at monitoring changes in urban areas, and more specifically related to buildings, after a seismic event. It is based on the analysis of extended time series of the spectral reflectance values in the Red-Green-Blue (RGB) bands and the resulting Perceived Lightness (PL) values, as well as the Normalized Difference Vegetation Index (NDVI). This procedure is effective and reproducible, with the advantage that Sentinel-2 observations, unlike commercial Very High Resolution (VHR) images, are free and regularly available on a wide scale. Based on a rich set of multispectral data, this research provides valuable insights into urban dynamics, and aims to optimize post-disaster risk management.

Global cities are increasingly experiencing severe heat-related issues due to rising global temperatures and urban heating. Vulnerable groups are particularly at risk of experiencing heat-related diseases. To effectively plan mitigation strategies, it is essential to have information on the areas with the highest urban heating and the presence of vulnerable populations. The current study aims to determine the persistent UHI and quantify its impact on the children and elderly population in the NCR, India. The study used MODIS LST data from 2000 to 2021 and the age structure population data for the analysis. The study's findings showed that more than 75% of the susceptible groups reside in the 10% of the study region that is part of a high-impact zone, which is particularly dangerous due to heat-related hazards. These findings emphasize the critical need for targeted strategies and actions to reduce the negative effects of urban heating on disadvantaged communities.

In this work a standard approach for the collection of multi-angular reflectance measurements by means of UAS (Unmanned Aerial System) is presented, as well as the modelling of the reflectance anisotropy through the Ross-Li-Maignan BRDF (Bidirectional Reflectance Distribution Function) model. The analysis is performed over three different types of surfaces, in particular a wheat field, an asphalted area and a corn field, and the measurements are acquired by means of MAIA multispectral camera on board UAS, which is characterized by the same spectral bands of Copernicus Sentinel-2 MSI (MultiSpectral Instrument). The results are promising being the relative RMSE between modelled and measured reflectances below 10% for all the test sites.

This work addresses the Bidirectional Reflectance Distribution Function (BRDF) characterization by means of MAIA multispectral camera onboard an Unmanned Aerial System (UAS). The proposed procedure relies on the design and execution of UAS flight plan for multi-angular acquisitions, which can be automatically repeated over different land cover types. Then, the inversion of the RossThick-LiSparse (Ross-Li) BRDF model is pursued in order to retrieve the fundamental parameters, allowing the complete characterization of the considered surface in terms of reflectance distribution for each band. A key point of this work is the challenge we face related to the development of an optimum strategy for the collection of ground-based dataset for BRDF model inversion.

There exists a scaling error in currently downscaling land surface temperature (DLST) processes and it is necessary to develop substantial methods specially for reducing it. In this letter, a multiple regional correction (MRC) approach with multiple correction terms (mCTs) was proposed to correct scaling errors in DLST processes. The test results indicate that (1) the proposed approach can effectively correct the scaling effects in DLST processes at high resolutions by reducing root mean square error by ~ 55%; (2) all initial resolutions considered for optical data (0.5 m – 4 m) were effective for developing the MRC approach with mCTs in correcting the scaling errors. Overall, this novel approach can significantly improve the accuracy of DLST maps at very high resolutions.

Surface albedo in the rural and urban environment is one of the most influencing parameters in the evaluation of the radiative forcing of the land surface. Satellite remote sensing is an efficient tool for surface broadband albedo estimation with various spatial resolutions. Different retrieval algorithms were proposed and tested in the literature. This letter focuses on the narrow-to-broadband conversion algorithm applied to Sentinel-2 reflective data, using its best spatial resolution (10 m). New narrow-to-broadband coefficients for albedo retrieval were computed. Their accuracy was tested with ground-based measurements in two different environments and compared with another algorithm introduced in the literature. The application of these coefficients for the albedo estimation requires the assumption of Lambertian surface and clear sky conditions. The first test was performed on six field stations of the Surface Radiation Budget Network (SURFRAD), and the second test was carried out within an urban area (Perugia, Central Italy), to better evaluate the impact of the spatial resolution on the albedo estimation from spaceborne sensors. The obtained root mean square error (RMSE) for the two tests presented very good values for both algorithms (around 0.02), with a slightly better performance of the proposed coefficients, especially for the higher albedo values. In the urban test, the retrieved albedo from Sentinel-2 with a 10-m pixel size provided considerably better results than the Landsat 8 estimation (30-m), highlighting the benefit of using a finer resolution in heterogeneous environments.

River discharge is an important variable to measure in order to predict droughts and flood occurrences. Once the cross-sectional geometry of the river is known, discharge can be inferred from water level and surface flow velocity measurements. Since river discharges are of particular interest during extreme weather events, when river sites cannot be safely accessed, noncontact sensing technologies are particularly appealing. To this purpose, this work proposes a prototype of a low-cost continuous wave (CW) Doppler radar sensor, which is able to monitor the surface flow velocity of rivers. The prototype is tested at two gauged sites in central Italy, along the Tiber River. The surface flow velocity distribution across the river is monitored by means of the analysis of received Doppler signal. The surface velocity statistics are then extracted using a novel algorithm that is optimized to run on a microprocessor platform with minimal computing power (ArduinoUNO). In particular, the radar measurements are used to initialize a 2-D entropy-based velocity model (EVM) that is able to estimate river discharges in any flow condition. Finally, the results concerning the observed discharge provided by the EVM prove to be comparable with those obtained with more expensive commercial solutions. The results are important since the described methodology can be extended to small-size Doppler radar sensors onboard unmanned aerial vehicles (UAVs), the latter providing a method for mapping surface velocity of rivers.

Calibration of microwave radiometers is a crucial task for reliable and accurate antenna temperature measurements in remote sensing applications. This paper describes a processing procedure for data calibration of noise-adding (NA) total power (TP) radiometers, without thermal stabilization, able to improve the radiometric resolution performance and keep good accuracy. This method, easily implementable, is based on the strong dependence of the radiometric gain on the internal physical temperature of the system. It provides a calibration of the output voltage measured in the TP mode exploiting the noise source power injection only every 30 min. The quality of the proposed procedure was assessed by means of three experiments carried out in different years and environmental conditions, using a low-cost NA TP radiometer operating at 12.65 GHz. The measurements show an uncertainty better than 0.7 K and, above all, a clear improvement in radiometric resolution (below 0.1 K). The radiometric resolution benefit is particularly appreciable in applications where the aim is the detection of small radiation power increments. Two experimental tests show how this method for data calibration effectively resolves the warm target counting inside the antenna footprint, while the same data measured with the standard NA procedure do not allow the same detection capability.

A regression-based downscaling of land surface temperature was developed over the heterogeneous urban area of Aprilia, Central Italy, using high resolution (HR) airborne data. Airborne sensors provided thermal and visible-near infrared (VNIR) measurements at 2-m pixel size. Coarse resolution images at 40, 30, and 20 m, upscaled by aggregation from the native airborne data, were sharpened to the finer resolution of 2 m. The main core of the downscaling method is the use of the spectral mixture analysis (SMA) to derive fractional pixel composition as predictors of the regression scheme. The HR VNIR data allow choosing detailed land cover types in the application of SMA, such as bright/dark roofs, and the benefit of this detailed selection is proved. The estimation error of the custom technique improves of about 10%-15% with respect to a classical regression downscaling.

In this work, the land surface temperature (LST) retrieval using Landsat Thematic Mapper (TM) data over the city of Florence, Italy, characterized also by the presence of rural and vegetated zones, was compared with a high-resolution (1 m ground pixel size) thermal image provided by an airborne survey made on July 18, 2010. Two Landsat scenes were processed before and after the flight, with the aim to evaluate the impact of the Landsat TM resolution of the thermal channel (120 m) on the LST estimation over an urban texture. The thermal data were downscaled at 30 m using a statistical approach employing spectral indices: such a method highlights the potentials and limits of the LST downscaling performed over an heterogeneous urban area.

The vertical structure of the atmospheric water vapor induces phase errors in interferometric synthetic aperture radar (SAR) data. This paper presents a simulation study to investigate whether spaceborne submillimeter radiometric observations, which can be realized with fairly high spatial resolution, are able to derive the vertical structure of the atmospheric wet delay. The accuracy of the retrieved zenith wet delay (ZWD) trend as a function of surface height is assessed in order to correct the associated height dependence of the interferometric phase error in a SAR interferogram. Using a simulated benchmark, we evaluate the errors associated with the use of both a linear and an exponential model of the behavior of ZWD as a function of the surface height. This paper shows a fairly accurate reconstruction of the trend parameters estimated from radiometer brightness temperature images, with respect to realistic atmospheric profiles provided by radiosounding observations (RAOBs). The trend parameters that we consider in this paper are the slope K for the linear model and scale height H for the exponential one. An overall better accuracy is found for the exponential model, which is more representative of the actual behavior of ZWD with height, resulting in a residual uncertainty in the path delay due to the atmospheric stratification of approximately 0.2-0.3 cm and nearly zero bias, as compared to RAOBs.

Spaceborne Interferometric Synthetic Aperture Radar (InSAR) is a well established technique useful in many land applications, such as tectonic movements, landslide monitoring and digital elevation model extraction. One of its major limitation is the atmospheric effect, and in particular the high water vapour spatial and temporal variability which introduces an unknown delay in the signal propagation. This paper describes the general approach and some results achieved in the framework of an ESA funded project devoted to the mapping of the water vapour with the aim to mitigate its effect in InSAR applications. Ground based (microwave radiometers, radiosoundings, GPS) and spaceborne observations (AMSR-E, MERIS, MODIS) of columnar water vapour were compared with Numerical Weather Prediction model runs in Central Italy during a 15-day experiment. A dense network of GPS receivers was deployed close to Como, in Northern Italy, to complement the operational network in order to derive Zenith Total Delay as well as Slant Delay which can support InSAR processing. A comparison with Atmospheric Phase Screens (APS) derived from a sequence of Envisat multi pass interferometric acquisitions processed using the Permanent Scatters technique on the two test sites has been also performed. The acquired experimental data and their comparison give a valuable idea of what can be done to gather information on water vapour, which, besides InSAR applications, plays a fundamental role in weather prediction and radio propagation studies. The work has been carried out in the framework of an ESA funded project, named "Mitigation of Electromagnetic Transmission errors induced by Atmospheric Water Vapour Effects" (METAWAVE). This paper presents the general approach an the various methodologies exploited in the project, together with the overall intercomparison of the results. In deep details on the comparison with the InSAR APS maps derived by the PS technique, as well as on GPS receiver processing and water vapour tomography are reported in two companion papers.

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.

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 (Ndand 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 Ndand Nwby 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.

A multilayer neural network has been developed to retrieve IPWV over land from brightness temperatures measured by the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) on board the Aqua satellite. 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 for clear-sky. In the presence of clouds, an rms of the order of 0.38 cm was found for both areas.

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 model's 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.

Spaceborne Interferometric Synthetic Aperture Radar (InSAR) is a well established technique useful in many land applications, such as monitoring tectonic movements and landslides or extracting digital elevation models. One of its major limitations is the atmospheric variability, and in particular the high water vapor spatial and temporal variability, which introduces an unknown delay in the signal propagation. On the other hand, these effects might be exploited, so as InSAR could become a tool for highresolution water vapor mapping. This paper describes the approach and some preliminary results achieved in the framework of an ESA funded project devoted to the mitigation of the water vapor effects in InSAR applications. Although very preliminary, the acquired experimental data and their comparison give a first idea of what can be done to gather valuable information on water vapor, which play a fundamental role in weather prediction and radio propagation studies.

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.

In this work a method based on neural networks is proposed to retrieve profiles of refractivity, temperature, pressure and humidity in the troposphere from GPS-LEO radio occultation. To overcome the constraint of temperature profile availability at each GPS occultation, we have trained a neural network with refractivity profiles as input computed from the geometrical occultation parameters of the CHAMP LEO satellite, while the outputs are the dry and wet refractivity profiles and the dry pressure profiles obtained from the contemporary ECMWF data.

Two cloud water models that can be included in the radiative transfer to simulate brightness temperatures from radiosonde data are analyzed. We first evaluate and optimize the capability of the models to detect correctly the presence of clouds, by comparing the cloud base heights identified from the radiosoundings with those provided by a ceilometer. Then, the performances of the different cloud models are analyzed by comparing simulated brightness temperatures in the absence of scattering with those measured by a dual-channel microwave radiometer.

A large dataset of concurrent integrated precipitable water vapor (IPWV) estimates from ground-based microwave radiometers (MWRs), global positioning system (GPS) ground-receivers, and radiosonde observations (RAOBs) has been collected in five different sites in Central Italy. Both MWRs and GPS have shown a capability of accurate and continuous water vapor monitoring. These data are used to study the seasonal and spatial variability of IPWV. A comparison of these data with the IPWV field produced operationally by the nonhydrostatic Mesoscale Model (MM5), running at the University of L'Aquila/Center of Excellence (CETEMPS) is performed in order to find either model shortcomings and to corroborate the IPWV behavior highlighted by the measurements. Both measurements and model outputs span over a period of about one year allowing for a systematic statistical analysis for all the examined stations. The statistical analysis shows a good agreement between GPS and MWR data, whereas discrepancies are found between RAOBs and the other techniques. The IPWV shows the largest diurnal variability, approximately 3%, during the summer season. An overall good agreement is found between the forecasted and observed IPWV. The related statistical parameters show a very low bias (0.001 cm) with a good correlation coefficient (0.939). On the other hands, the seasonal analyses highlight a few discrepancies, mostly due to the MM5 difficulties in correctly forecasting the diurnal cycle.

This work concerns the surface 'change detection' by means of differential digital elevation model (D/spl I.bar/DEM) techniques, based on laser scanning 3D data. The laser scanning 3D technology allows to generate an high precision DEM, that can be used to calculate the morphological ground surface variations at different acquisition time intervals. The basic operation has been to acquire several DEMs in the test area: through the subtraction of different DEMs we can obtain the temporal decorrelation of two geometrical parameters: volume and elevation vector. The validation is performed by the comparison with the GPS data. The integration with differential interferometric SAR systems will allow to investigate wide areas. So, when the alarm event has been individuated, the laser scanning 3D is used to generate a precision DEM reconstruction. The results show that with such technique it is possible to calculate the volume and elevation variations with a standard deviation less than a centimetre.

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.

This work considers the use of a dual-frequency, ground-based microwave radiometer with scanning capabilities to investigate the behaviour of the atmosphere in terms of precipitable water vapour and integrated cloud liquid before precipitation events.

This paper concerns the validation of the atmospheric integrated precipitable water vapour (IPVW) product of the MERIS instrument on board of ENVISAT satellite. The validation is performed both at specific locations and over an extended area. The first comparison is performed with respect to the measurements of ground based instruments (microwave radiometers, GPS receivers, radiosoundings). The second assessment is based on IPWV maps of the Tyrrhenian area that are produced by geostatistical interpolation of the measurements of a network of GPS receivers (over land) and measurements of Special Sensor Microwave Imager Radiometer (over sea). The preliminary results show that the standard ESA algorithm for MERIS underestimates IPWV values both over land and sea backgrounds.

This paper considers the monitoring of integrated precipitable water vapour (IPWV) exploiting measurements of Global Positioning System (GPS) ground-based receivers with the purpose of analysing the behaviour of such parameter in occasion of precipitation events. A statistical characterisation of atmospheric water vapour content retrieved by GPS at specific receiver locations will be proposed to improve the knowledge of water vapour field of precipitation systems.

This paper concerns the remote sensing of atmospheric integrated precipitable water vapour (IPVW) using a Global Positioning System (GPS) network and the Special Sensor Microwave Imager Radiometer (SSM/I) in the Mediterranean area. A comparison of IPWV maps from the two different techniques is presented. Some preliminary attempts to develop a data assimilation method, are also proposed.

The passive calibration of the radar altimeter consists in characterising the receiver by observing natural surfaces with known emission in the so-called noise-sensing mode. The paper focuses on the general approach undertaken to simulate the brightness temperature at the top of the atmosphere observed by the Envisat Radar Altimeter (RA-2). It is based on emissivity models for land and sea as well as atmospheric radiation models supported by a continuous flow of on-line data used as model inputs.

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.

Considers the remote sensing of atmospheric integrated precipitable water vapor (IPWV) using a Global Positioning System (GPS) network in the Mediterranean area during the whole year 1999. The assessment of IPWV retrieval has been carried out by means of an experimental campaign conducted in the proximity of the city of Cagliari, Italy, located on the southern coast of Sardinia in the Mediterranean Sea. The authors have compared GPS estimates with co-located measurements from a microwave dual-channel water vapor radiometer (WVR) collected in nonprecipitating conditions and with observations from radiosondes released four times a day from the Cagliari-Elmas Airport, approximately 15 km away from the GPS and WVR site. The long-term comparison has shown a fairly good agreement among the two remote sensors and the radiosonde observations, with a higher correlation between GPS and WVR.