With the rapid population growth, both urbanization and transportation affect the air pollution, population health, and global warming. A number of air pollutants are released from industrial facilities and other activities and may cause adverse effects on human health and the environment. One of the biggest air pollutant, nitrogen dioxide (NO₂), is mainly caused as combustion of fossil fuels, and especially from traffic exhaust gases. Over the years, air pollution has been monitored using satellite remote sensing data. In this study, we investigate the relation of the tropospheric NO₂ retrieved from the recently launched Sentinel-5 Precursor, a low earth orbit atmosphere mission, dedicated for monitoring air pollution equipped with a spectrometer Tropomoi (TROPOspheric Monitoring Instrument), and the population density over Turkey. For this purpose, we use the mean value of NO₂ collected from July 2018 to January 2019, and the statistic population data from 2017. The results showed significant correlation higher than 0.72 between the population density and the maximum NO₂ values. For future studies, we recommend investigating the correlation of different air pollutants with population, and other factors contributing to air and environmental pollution.

Proximal remote sensing devices are becoming widely applied in field plant research to estimate biochemical (e.g. pigments or nitrogen) or agronomical (e.g. leaf area, biomass or yield) parameters as indicators of stress. Non-invasive characterization of plant responses allows screening larger populations faster than the conventional procedures that, besides being time-consuming, also implies the destruction of material or is subjective (e.g. visual ranking). This study deals with the comparison of a set of remote sensing devices at single-leaf and whole-canopy levels based on their performance in assessing how the eggplant and its yield respond to crafting as a way to tolerate root-knot nematodes. The results showed that whole-canopy measurements, as the Green Area (GA) derived from the RGB images (r=0.706 and p-value=0.001**) or the canopy temperature (r=-0.619 and p-value=0.005**), outperformed the single-leaf measurements, as the leaf chlorophyll content measured by the Dualex (r=0.422 and p-value=0.059) assessing yield. Moreover, other parameters as the time required to measure each sample or the cost of the sensors were taken into account in the discussion. Everything considered, indexes derived from the RGB images have demonstrated their robust potential for the assessment of crop status, being a low-cost alternative to other more expensive and time-consuming devices.

Change detection (CD), which is a process of identifying changes occurred in a geographical area over the time, plays a key role in many applications including assessing natural disasters, monitoring crops, and managing water resources. In the past decades many CD (both binary and multiple) techniques have been proposed. Hence, evaluating and analyzing of probability of changes and interpreting them, is essential task which leads to better management of natural resources and preventing disasters. For this purpose, we adopted an approach to visualize probability of occurring detected changes. Based on this approach, change pixels will be categorized and labeled as probabilities (in percentage). In this paper, the proposed framework consists of the following three steps. Firstly, this research produces binary change maps from methods have been proposed in the literature. Then spectral similarity of pixels is calculated in abundances map (of endmembers) domain. A measurement of spectral similarity identifies the finer spectral differences between the two hyperspectral images (HSIs). Finally, combining binary map and spectral similarity values resulting change multiple probability map. The experimental results show that the method has a good result, and can be widely used in hyperspectral CD applications.

Due to the high spatial heterogeneity and temporal variability, wetlands are one of the most difficult ecosystems to observe using remote sensing data. With the additional Sentinel-2 vegetation red-edge bands, an improvement of the vegetated classes classification is expected. In order to investigate the influence of the Sentinel-2 red-edge bands, in this paper, we use one Sentinel-2 satellite image acquired in the summer period, in August, and we evaluate two classification scenarios over wetland classes. As a study area, the Central Anatolian region in Turkey has been selected. The first scenario excludes the red-edge bands, while in the second scenario are included all red-edge bands in the classification dataset where two different wetland classes, intensive vegetated wetland classes such as swamps and partially decayed vegetated wetland areas such as bogs, have been classified using Support Vector Machines (SVMs) learning classifier. The classes were defined using high-resolution images from Unmanned Aerial Vehicle (UAV) obtained on the same date with the overpass of the Sentinel-2 satellite over the study area. As expected, the results showed significant improvement of the intensive vegetated wetlands, with more than 30% in both user and producer accuracy, while no significant changes have been noticed in the partially decayed vegetated wetlands. For future studies, we recommend evaluating the influence of the Sentinel radar data over wetland areas.

Polarimetric SAR data based scattering retrieval has been widely used to characterize manmade and natural features. It has been found that PolSAR data has the capability to retrieve scattering information contributed by different features within a small area or single resolution cell. Generally, it has been found that the urban structures are contributing the high double-bounce scattering, but due to closely spaced urban structure, multiple reflections of the SAR waves from the walls of the buildings give the appearance of the volume scattering. The overestimation of volume scattering from urban structure could be reduced by the adoption of interferometric coherence in decomposition modeling. The PolInSAR coherence constitutes the full collection of polarimetric and interferometric information. The urban buildings are considered as permanent scatterers which is usually not affected by the temporal and volume decorrelation. Therefore, they show high coherence magnitudes. The prime focus of this research was the implementation of PolInSAR coherence in the decomposition modeling to minimize the overestimation of volume scattering from the urban structure. This study has used the CoSSC product of the TanDEM-X mission. The PolInSAR data over Dehradun, India were acquired in bistatic mode. All the possible combinations of PolInSAR coherence were generated from TerraSAR-X and TanDEM-X. The Pauli basis based PolInSAR coherence has shown the capability to distinguish different features according to their nature. To find the appropriate coherence for decomposition modeling the optimization was performed on PolInSAR data to select the optimal coherence. The results obtained from PolInSAR coherence based decomposition modeling had shown the dominance of double bounce scattering in the urban area for closely spaced structures also. The study strongly recommends the use of PolInSAR coherence in the decomposition modelling to minimize the ambiguity in the scattering retrieval from an urban area due to close spaced buildings.