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Rapid urbanization and population growth has resulted in increased traffic congestion and consequently air pollution in most major cities, in particular, in the developing countries. Knowledge on the amount of different air pollutants and their spatial and temporal concentrations is of great importance for decision makers on health, environment and air quality estimation in different scales. Mashhad, as a metropolitan, due to its specific religious, socio-cultural and geographical role in the region is declared as one of the most polluted cities of the country. Given that there is a direct relationship between traffic volume data and air pollutants (PM2.5, CO and ), this study attempts to estimate the amount of each pollutant based on traffic volume and some primary weather data. We used empirical models proposed in the literature, such as Baker model and AERMOD, as well as linear regression and nonlinear neural network methods to explore the correlation between traffic volume and air pollutants over a period of six months in the city of Mashhad. The results showed low correlation coefficients between traffic volume and air pollutants in all models, indicating that such models may not be suitable to further estimate air pollutants using only traffic volume and primary weather data. Correlation coefficients were lowest for the pollutant PM2.5 over the time period of the study. Sensitivity analysis demonstrated that vehicle average velocity is by far the most influential variable in the empirical models used.

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Satellite image simulation is of paramount importance in quantitative remote sensing studies. Synthetic signals/images are used in a range of applications including; pre-launch algorithm development and performance evaluation; designing sensors for given application; and parameter retrieval through inverse modelling. Top of atmosphere sensor reaching radiance is a complex function of the interactions between solar radiation and the Earth's atmosphere and surface. The at-sensor radiance is, therefore, a combination of the surface reflectance, atmospheric effects, target’s surroundings effects plus illumination and viewing geometry. Radiative transfer models are commonly used to simulate at-sensor radiance using physical and chemical properties of the surface and atmosphere. This paper presents a modeling system for the simulation of optical hyperspectral images through the extended four-stream approach. The system is modeled at three different levels: the surface, the atmosphere and the sensor. The simulation begins with four surface reflectance factors modeled by the Soil-Leaf-Canopy radiative transfer model SLC at the top of canopy and propagate them through the effects of the atmosphere which is explained by six atmospheric coefficients, derived from MODTRAN4 radiative transfer code. The top of atmosphere radiance is then convolved with the sensor spectral and spatial response functions. Validation of the model is considered over the Barrax area in Spain, using the dataset provided during SEN3EXP campaign (2009), to simulate hyperspectral CHRIS-Proba and multispectral LANDSAT-5 imageries. Overall, comparisons between simulated and actual images demonstrated model’s capability in simulating satellite signal/image with RMSE better that 0.02 for vegetative surface reflectances.

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Abstract
Satellite time series data play a key role in characterizing land surface change and monitoring of short and long-term land cover change processes over time. While coarse spatial resolution optical sensors (e.g. MODIS) can provide appropriate time series data, the temporal resolution of high to intermediate spatial resolutions sensors (1-100 m e.g. Landsat) does not allow for having temporally frequent measurements because of the orbital configuration of such sensors and cloud contamination. A promising approach for addressing this challenge and producing Landsat-like imageries is the blending of data from coarse spatial resolution sensors like MODIS. Among different approaches proposed in the literature, the ESTARFM model has been reported to outperform other models in generating Landsat-like imageries with reasonable accuracy over heterogeneous areas. Despite the large body of studies implementing ESTRAFM for downscaling MODIS data, quantitative evaluation of the model under different conditions has not yet been investigated. This study quantitatively evaluates model performance over different land cover types, resampling methods and time-difference analysis between input and synthetic images. The results demonstrated that employing bilinear resampling in the ESTARFM produces results slightly better than nearest neighbor and cubic resampling methods. Moreover, the ESTARFM model accurately predicts Landsat-like surface reflectance images with RMSE better than 0.02 and correlation more than 90% over different land cover types. However, the model performance significantly degrades as the time difference between the input and synthetic images increases.