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.

Urban areas are rapidly changing all over the world and therefore, the continuous mapping of the changes are essential for the urban planner and decision makers. Urban changes can be mapped and measured by using remote sensing data and techniques along with several statistical measures. The urban scene is characterized by very high complexity, containing objects formed from different types of man-made materials as well as natural objects. The aim of this study is to detect urban growth, which can be further utilized for urban planning. Although high-resolution optical data can be used to determine classes more precisely, it is still difficult to distinguish classes such as residential regions with different building type due to spectral similarities. SAR data provide valuable information about the type of scattering backscatter from an object in the scene as well as its geometry and its dielectric properties. Therefore, the information obtained using the SAR processing is complementary to that obtained using optical data. This proposed algorithm has been applied to multi-sensor dataset consisting of the optical QuickBird (RGB) image and full polarimetric L-band UAVSAR image data. After preprocessing data, the coherency matrix (T), and Pauli decomposition are extracted from multi-temporal UAVSAR images. Next, SVM (Support vector machines) classification method is applied to the multi-temporal features in order to generate two classified maps. In the next step, post classification based algorithm was used for generating the change map. Finally, the results of the change maps are fused by the majority voting algorithm to improve the detecting of the urban changes. In order to clarify the importance of using both optical and polarimetric images, the majority voting algorithm was also applied to change maps of optical and polarimetric images separately. In order to analyze the accuracy of the change maps, the ground truth change and no-change area that gathered by visual interpretation of Google earth images were used. After correcting the noise generated by the post-classification method, the final change map was obtained with an overall accuracy of 89.81% and Kappa of 0.80.

Flood events represent some of the most catastrophic natural disasters, especially in localities where appropriate measurement instruments and early warning system are not available. Remotely sensed data can often help to obtain near real-time rainfall information with a global spatial coverage without the limitations that characterize other instruments. In order to achieve this goal, a freely accessible Extreme Rainfall Detection System (ERDS – erds.ithacaweb.org) was developed and implemented by ITHACA with the aim of monitoring and forecasting exceptional rainfall events and providing information in an understandable way also for non-specialized users. The near real-time rainfall monitoring is performed taking advantages of NASA GPM IMERG half-hourly data (one of the most advanced rainfall measurements provided by satellite).
This study aims to evaluate ERDS performance in the detection of the extreme rainfall that led to a massive flood event in Queensland (Australia) between January and February 2019. Due to the impressive amount of rainfall that affected the area, Flinders River (one of the longest Australian river) overflowed, expanding to a width of tens of kilometres. Several cities were also partially affected and Copernicus Emergency Management Service was activated with the aim of providing an assessment of the impact of the event.
In this research, ERDS output was validated using both in-situ and open source remotely sensed data. Specifically, taking advantage of both NASA MODIS (Moderate-resolution Imaging Spectroradiometer) and Copernicus Sentinel datasets it was possible to have a clear look of the full extent of the flood event. GPM data proved to be a reliable source of rainfall information for the evaluation of areas affected by heavy rainfall. By merging these data, it was possible to recreate the dynamics of the event.

Earth’s topography and deformation mapping has become a lot more easier by the use of a geodetic technique popularly known as repeat-pass Synthetic Aperture Radio Detection and Ranging (SAR/RADAR) Interferometry (InSAR). However, the measurements obtained through InSAR are liable to atmospheric errors. Due to refraction, Radar waves which traverse through the atmosphere twice, experience a delay. Troposphere and Ionosphere are the two major layers of the atmosphere that are mainly responsible for this delay in the propagating Radar wave. According to previous studies, water vapor and clouds present in the troposphere and the Total Electron Content (TEC) of the ionosphere are responsible for the additional path delay in the wave. An increase is induced in the observed range due to tropospheric refractivity and path shortenings are observed due to ionospheric electron density. The quality of phase measurement is affected by these atmospheric induced propagation delays and hence errors are introduced in the topography and deformation fields. A three-pass differential interferogram (DInSAR) is generated from two interferograms and the effect of this atmospheric delay is studied on the same. The interferograms are generated from three Advanced Land Observation Satellite (ALOS) carrying Phased Array L-band Synthetic Aperture RADAR (PALSAR) Single Look Complex (SLC) images acquired on the same study area. Atmospheric phase correction is done on the generated DInSAR . The phase error due to the atmosphere may be confused with the displacement component. Unless these errors in phase are not corrected, it is difficult to obtain accurate measurements. Thus, for all practical applications of DInSAR, atmospheric error correction is essential in order to avoid inaccurate erratic height and deformation measurements.

ISRO’S RISAT-1 was the first SAR satellite equipped with the compact polarimetric (CP) mode for data acquisition. The CP mode has several advantages compared to the Quad-pol mode in terms of reduced hardware complexity, pulse repetition frequency (PRF), data transmission rate and double swath coverage. To exploit the advantages offered by the CP mode, the datasets need to be Polarimetric calibrated. The Polarimetric calibration procedure estimates the polarimetric distortions in the datasets caused due to channel imbalance, crosstalk and Faraday rotation. These polarimetric distortions cause the misinterpretation of the ground targets in the polarimetric decomposition techniques. The Freeman compact-pol polarimetric calibration algorithm is the most commonly used algorithm which requires a Trihedral corner reflector, a 0^o oriented Dihedral corner reflector and a 45^o oriented Dihedral corner reflector. In this study one trihedral corner reflector and one 0^o oriented Dihedral corner reflector were deployed at the Desalpar calibration site, Gujarat, India. The RISAT-1 Circular Transmit Linear Receive (CTLR) dataset of this area was used to estimate the polarimetric distortion parameters and these distortion parameters were used to polarimetrically calibrate the RISAT-1 CTLR dataset of the Haridwar region, Uttarakhand, India. The Freeman compact-pol polarimetric calibration algorithm was used for this study and the 45^o oriented Dihedral corner reflector response required for the calibration algorithm was derived from the oriented Dihedral corner reflector response. The Cloude compact-pol decomposition algorithm was used to evaluate the ground target characterization accuracy before and after polarimetric calibration using the proposed algorithm. Before polarimetric calibration, urban targets were showing surface scattering behavior and river channels were showing increased double bounce scattering behavior. After polarimetric calibration, the urban tragets were showing dominance in double bounce scattering and river channels were showing dominance in surface scattering. The calibration methodology and the results obtained from Cloude compact-pol decomposition are discussed in detail in the manuscript.

The Azar area is located in the east of Mehran in Ilam, Iran, is an arid region where oil extraction started form 2014. The subsidence is mainly observed in the vicinity of the oilfield. In this study we has measured the land subsidence in Azar oil field through a time series analysis. time-series interferometric synthetic aperture radar (T-InSAR) has emerged as a powerful technique to measure various surface deformation phenomena of the earth. The main step in all T-InSAR algorithms is the phase unwrapping step to resolve the inherent cycle ambiguities of interferometric phases and to estimate absolute deformation between pixels/points.T-InSAR was done to investigate what the corresponding subsidence source is. The Stanford Method for Persistent Scatterers (StaMPS) package was employed to process Envisat ASAR images collected between 2003 and 2009, as well as Sentinel-1A images collected between 2014 and 2018. The subsidence to have occurred with a mean rate up to 6 mm/year between 2014 and 2018, but no subsidence took place between 2003 and 2009 in the radar line of sight direction. Due to the high depth of oil wells (4,300 kilometres) we expect that the induced cover the large area and has a small magnitude.

The results of the study confirm pattern of subsidence induced by oil extraction. The results of this study also reveal that the factors leading to land subsidence include oil extraction, heavy rainfall, and poor soil condition. The analysis also show that oil extraction plays a key role in land subsidence in Azar oil field. Also the result of the InSAR cumulative map shows seasonal displacements on territory oilfield.