جستجوی مقالات مرتبط با کلیدواژه "sentinel-5" در نشریات گروه "عمران"

Every country relies on soil as a vital natural resource that significantly contributes to environmental conservation and food production. Preparation of soil nutrient distribution map serves as a valuable tool for managers to make decisions. Due to the time-consuming and expensive nature of laboratory analysis for these variables on a large scale, efforts have been made to explore soil nitrogen through remote sensing. The current research deals with the application of remote sensing methods along with regression and random forest models to predict total soil nitrogen in Gilan province. This study aimed to answer two main questions: (1) Can SAR data be used to quantify total soil nitrogen (TSN) (2) How do SVM, BRT and RF algorithms perform in predicting soil nitrogen content?

This study focused on evaluating the data capabilities of Landsat-9 and Sentinel-1 satellites individually and in combination, using advanced algorithms such as Support Vector Machine (SVM), Boosted Regression Tree (BRT), and Random Forest (RF). The purpose of this evaluation was strategic, aiming to showcase the diverse conditions of the study area based on land cover/land use, climatic, and topographical parameters. Various variables, including climate parameters, topographic components, and remote sensing subscale indices, were investigated in conjunction with SAR data and optical images. Nonlinear machine learning algorithms, specifically SVM, RF, and BRT, were employed to predict total soil nitrogen status by modeling complex relationships between soil properties and environmental variables. R software, utilizing the CARET package for parameter input, was employed to implement the algorithm.

The results indicated the following: RF and BRT algorithms outperformed SVM and were effective in monitoring total soil nitrogen values. Multi-temporal SAR images showed higher accuracy in monitoring total soil nitrogen content compared to optical remote sensing data, facilitating more realistic predictions in paddy soils. The integration of environmental variables led to an increase in the accuracy of algorithms, where remote sensing variables played a crucial role, contributing to 61% and 51% effects in RF and BRT algorithms, respectively. The comparison of SVM and RF algorithms revealed that RF ranked second after the BRT algorithm, and the accuracy of total soil nitrogen estimation was not achieved with the SVM algorithm. However, both BRT and RF algorithms were able to monitor changes in total soil nitrogen. BRT performed better, accurately recording 58% of changes, as evidenced by a higher R2 value (0.58) and lower RMSE (0.25 mg/kg) and MAE (0.19 mg/kg) values.

In conclusion, the following key points were extracted from this research: 1) RF and BRT algorithms outperformed SVM in effectively monitoring total soil nitrogen levels; 2) multi-temporal SAR images demonstrated higher accuracy in tracking total soil nitrogen compared to optical remote sensing, enabling precise predictions in paddy soils; 3) the incorporation of environmental variables enhanced algorithmic accuracy; and 4) remote sensing variables contributed 61% and 51% to RF and BRT algorithms, respectively.

Natural phenomena in the world, such as earthquakes and heavy rains, which are sometimes combined with wind storms, can cause landslides. These landslides in one area can damage several parts and cause significant damage to natural and human infrastructure. Landslides occur in almost all countries of the world and play an important role in the changing of the earth's surface. There are different geodetic and non-geodetic methods to measure the changes caused by this phenomenon. Geodetic methods are not suitable for preparing a landslide damage map due to their limitations such as high cost and time consuming. Therefore, we have to use non-geodetic methods. Nowadays, the use of remote sensing techniques has received much attention. Radar images have been proposed as a suitable tool for monitoring landslides due to their high spatial resolution, wide view, the possibility of capturing in any kind of weather conditions and during the night, the high frequency of spatial and temporal observations, and acceptable accuracy. In addition, there are various methods for extracting information from this type of data, most of which require large initial data, and time-consuming processing. But in this research, we are trying to produce a landslide damage map by using the least input data and in the shortest possible time (no need to spend time for downloading all the required data). Therefore, the method used in this research is the use of Sentinel-1A RADAR images and processing these images in the Google Earth Engine (GEE) processing platform. In this article, we will prepare landslide damage map by examining the changes in the backscattering coefficient image (σ° ) between before and after landslide RADAR images. In this research, by having two sets of images related to before and after the occurrence of the landslide in ASC and DSC pass mode, we can produce the Iratio image between the image before and after the landslide for ASC and DSC mode. After that, we can average between IratioASC and IratioDSC to produce the IratioAverage. After producing this image and removing areas that cause errors and ambiguity, such as seas and lakes, agricultural areas, deforested areas, etc. finally, by determining a suitable threshold, it is possible to detect landslide areas. In order to evaluate the accuracy, since the lack of ground data in the study area, the generated landslide map was compared with Sentinel-2 optical sensor images and the results showed a high agreement between these two data sets, and this shows the high accuracy of the proposed method.

Earth has a complex ecosystem that is affected by natural processes and human activities, and understanding these processes is a real necessity. Environmental changes, from climate change to the reduction of natural resources, affect human life, economies, and the well-being of future generations. Therefore, the need for comprehensive Earth observation has never been more critical. To address this need, the Copernicus program was created by the European Space Agency in the early 2000s. The goal of this program was to create an operational system for earth monitoring with open access to high quality satellite data. The Sentinel satellites, as the core of the Copernicus program, is one of the symbols of humanity's progress in earth observation and environmental monitoring. This research examines the Sentinel satellites and shows the importance, basic principles and unique features of each of them. Sentinel satellites are unique operational platforms compared to the satellites of the previous generation.

In this paper, the main framework of the Copernicus program, such as open access to satellite data, global coverage, operational stability, diverse sensors, and the framework of global cooperation, which is of great help to researchers, policymakers, and other users of satellite data, has been evaluated. The Sentinel satellites includes several satellites that acquire images from the earth in different spectral ranges, with global coverage and different viewing times. These satellites have operational stability and new generations of satellites are developed and launched to replace older satellites. Each satellite is equipped with special sensors according to its mission goals and makes it possible to monitor the land, oceans and atmosphere with the best possible tools from space.

The Sentinel-1 satellite, which is equipped with a radar sensor, can image in the C frequency band and different spatial powers from 5 to 40 meters. Also, this satellite is able to take images in any weather conditions and at any hour of the day and night, which is valuable for many applications. The accuracy of Sentinel-1 data is very high in monitoring changes in the shape of the earth's surface, crisis management, polar ice observations and ocean monitoring. This vital role in detecting and monitoring land subsidence in urban areas makes it effective in urban planning and helping to prevent crises. On the other hand, the Sentinel-2 satellite with a multispectral sensor provides a powerful tool for Earth observation. By recording data in a wide spectral range in 13 spectral bands from visible to short-wave infrared, this satellite has been able to deepen our understanding of the features of the Earth's surface. The re-viewing time of this satellite has made it possible to monitor crops and evaluate the health of vegetation. Also, the spatial resolution of Sentinel-2 satellite images is an effective factor in urban planning, tree health monitoring, and natural disaster monitoring, such as fire and flood. In addition, the high bandwidth of this satellite helps to efficiently cover large areas and increases its efficiency in environmental monitoring.

The Copernicus program is known for several key principles that have underpinned their success. These principles include open access data, global coverage, operational interoperability, diverse sensors, and a global cooperation framework. The principle of open access has given the assurance to the general users, including researchers, policymakers and commercial companies, that a continuous flow of satellite images will support their activities. The Copernicus program with a system of satellites ensures global coverage with optimal revisit time. The operational continuity of the program has resulted in new generations of satellites being developed and launched to replace older generations, to ensure a continuous flow of data. Each Sentinel satellite is equipped with specific sensors designed for its mission objectives, enabling it to monitor the Earth, oceans, atmosphere, and more. According to the characteristics of the Copernicus program, Sentinel satellites have started a new era of Earth observation and have provided a powerful and versatile tool for monitoring and understanding the Earth's ecosystem.

In urban areas, deformation of transport and road infrastructure may lead to serious safety incidents. Therefore, management and monitoring are vital to ensure the quality of constructions and prevent transportation accidents, especially in areas with land subsidence such as Qom province. Low annual rainfall statistics, successive droughts and the type of soil in the region have caused this province, especially Qom city, to be among areas prone to land subsidence. The absence of a permanent geodynamic station in Qom's urban area, as well as the costly and time-consuming leveling operation, made radar interferometric technology to be chosen as one of the best methods for monitoring land deformation. In this study, the permanent scatterer interferometric synthetic aperture radar (PS-InSAR) technique has been used for infrastructure monitoring and inspection because it allows obtaining reliable results in the detection and prevention of infrastructure instabilities during time provides. For estimating land subsidence rate in the city of Qom, 29 descending radar images of the Sentinel-1 sensor were used during the period of January 2019 to November 2020. GMTSAR2StaMPS (G2S) software was used to process radar images and time series analysis. The results showed that southeastern area of Qom city has a subsidence rate of -54.5 mm/yr along the line of sight (LOS) of the satellite. The worrisome issue is the extension of land subsidence to the central area of the city and damage to important urban infrastructures, which should be taken into account in order to prevent this problem. The investigation of regional piezometers and Qom-Kahak hydrograph shows a drop of 1.8 meters in the water level between October 2017 and October 2021, which has caused subsidence of about -7.5 cm per year in the vertical direction in the southeast of Qom. Also, due to the good agreement of the results of radar interferometry and the drop of the underground level in the region, the excessive exploitation of underground water resources for agricultural purposes can be considered as the main reason for the subsidence in this area.

Studying the emission of methane gas as the second most important greenhouse gas is vital due to climate change. This study was conducted to check the correctness of the news published by NASA regarding the existence of a methane cloud in the south of Tehran. Due to the importance of the subject, so far many remote sensing satellites such as sentinel-5, MethanSAT, SCIAMACHY, and GOSAT have been provided to monitor pollutants, especially methane gas. Also, much research has been done in evaluating this sensor and monitoring polluted areas. For example, Cherepanova, et al analyzed the spatial-temporal analysis of methane concentration and its relationship with burned areas. Using daily data from the TROPOMI sensor, they concluded that methane concentrations were higher in newly burned areas than in the previous year's burns. Maurya, N.K., et al investigated the concentration of SO2, CO, SOX, and CH4 pollutants using Sentinel-5P TROPOMI data in the periods after wheat and rice harvesting and land burning. The results showed that the concentration of CH4, SO2, SOX, CO, and aerosol increased significantly during the burning period of agricultural land after harvesting rice and wheat every year.

In this research, the daily data series of the TROPOMI sensor on the Santi-5 satellite were used on the Google Earth Engine platform. First, the spatio-temporal analysis of methane dynamics in Tehran has been discussed. In this way, time changes are made from July to October 2022 and also for the spatial analysis of pollutants, a monthly map has been made. Then according to the obtained date, the correctness or incorrectness of the presented map has been checked. Finally, in order to evaluate the results, the length of the methane cloud observed by NASA was compared with the methane cloud extracted by TROPOMI.

The obtained results showed that on September 1st, 2022 methane concentration was high and around 1970 ppm, and other gases did not have a high value on this date, which indicates the lack of effect of other gases on the increase of methane concentration and lack of misdiagnosis and according to the average map in the south of Tehran This gas has been more. Also, the methane cloud observed by NASA on the old Tehran-Qom road near the Qasemabad-Shorabad neighborhood was confirmed by the use of Sentinel-5 satellite data on the Google Earth Engine platform in red, meaning high concentration, which caused the release and concentration of methane gas. It is located in the south of Tehran and its main source was the landfill in this area. In addition, the length reported by NASA was compared with the length of the methane cloud extracted from the TROPOMI instrument, which was 4.8 km and 4.41 km, respectively.

Nowadays, air pollution has become a major problem due to environmental problems and adverse effects on human health, especially in densely populated cities. Based on the obtained results and the image of methane gas on September 1st in the south of Tehran, it was found that the news published by NASA was true and the main factor was that this area is a garbage dump. In this way, the Google Earth Engine platform can be used as a suitable tool for monitoring daily, monthly and annual changes.

In recent decades, due to the various facilities in big cities that have caused the expansion of cities and urbanization and the increasing desire of citizens to live with a machine, this change in life pattern has also caused an increase in environmental pollution, difficult conditions for the livability of human life space, such as (The destruction of urban and suburban vegetation and the loss of ecological balance. The expansion of the population of cities and excessive population density in cities has caused the destruction of the natural environment and the construction of artificial structures that require physical and It has made the human spirit more visible to the environment. Considering that Iran is one of the countries located in the dry belt of the world, the cities to deal with noise and air pollution, as well as reduce environmental stress more than any other point to the space They need green. Considering the many problems that we see in changing the urban structure today, the heterogeneous quantification of the environment has been considered as a goal in landscape ecology, so that recently there have been efforts to develop methods In order to quantify the spatial heterogeneity (complexity and variability of the system characteristics, including the mosaic spatial layout of the land surface) (time and space) the features of the land have been done. Terrain metrics are a useful tool for expressing the mosaic pattern of urban green spaces and its changes in connection with urban development processes, which, in addition to interpreting the effect of the aforementioned processes on the ecological characteristics of the environment, can be used in decision making related to the growth of the city, distribution of uses and urban green space development planning. Therefore, according to the existing problems in defining and determining the urban green space, the application of remote sensing science, which is considered a powerful tool in the management of urban resources, and by providing the facilities of this science in order to provide up-to-date information, digital processing of images and the possibility of comparison Sometimes the data has helped experts to monitor the changes of the green space over time. Also, by using remote sensing in combination with terrain measurements, it can improve the understanding of urban spatial structure and change processes. Today, the high speed and extent of changes, both structural and process, in the face of the land, which are caused by destructive human activities, have made planners face many issues and problems. The present study was conducted in order to evaluate map production using Sentinel-1 and Landsat-8 radar images using local climate method and vector machine in Tehran metropolis. Using the proposed method, it was determined that the central and southern parts of Tehran have a very concentrated residential structure, so that urban green spaces are rarely seen in the urban context, while the green spaces in the north of Tehran are scattered among the contexts. It is residential, which indicates the existence of private green spaces in this part of the city. Also, in the south of the city, due to the presence of fine-grained texture in this area, due to the presence of residential centers in these areas, compared to the residential texture of the northern parts of the city, in the central and southern areas, private green spaces do not exist or rarely exist, and this warning increases. Heat islands and higher temperature are in the south of the city. Maps produced using remote sensing can be an alarm for municipal managers who can prevent future problems with planning and management. Also, considering the important role of green space in the morale of citizens and crime reduction, it is necessary for city managers to pay enough attention to the issue of reducing green space and the lack of proper distribution of use in Tehran, because neglecting this issue can lead to other problems. It created compensable for the citizens.

In recent years, Road detection and road extraction from satellite images with the advancement and development of deep learning algorithms in the field of semantic segmentation has received more and more attention of researchers. In this regard, most of the studies have been done in the field of Road detection and road extraction using optical images and in these studies, few studies have been performed using radar images worldwide. Therefore, the aim of this study was to use a deeply Refined Deep Residual Convolutional Neural Network (RDRCNN) to evaluate and compare the accuracy of road extraction from Sentinel 1 radar images in Tehran and Shiraz metropolitan areas in equal conditions in terms of number of educational samples, validation and architecture. It is the same. In this study, to extract the road using DNN, the VV-VH color combination of Sentinel 1 radar images from 8 different cities (Tehran, Mashhad, Isfahan, Shiraz, Tabriz, Urmia, Baghdad and Beijing) was used. Finally, the RDRCNN model with a residual connected unit (RCU) and a dilated perception unit (DPU) was used for road training and extraction. The research findings indicate that the RDRCNN model has performed almost the same in the process of identifying and extracting roads in the two cities of Tehran and Shiraz, and in general, the above model has performed slightly better in the city of Shiraz. In terms of accuracy evaluation metrics, for Tehran images, the criteria were Recall 57.66%, accuracy 51.29%, F1 score 54.43% and overall accuracy 92.78%, and for Shiraz images Recall criteria 60.77%, accuracy 54.71%, F1 score 57.40% and overall accuracy of 95.63% were obtained. The findings of this study show the low accuracy of road training and extraction from Sentinel 1 radar images for two metropolitan areas of Iran. In general, by comparing the results of this study with previous studies, it can be seen that one of the most important reasons for the low accuracy of the results is the low width of roads in Iranian cities; However, due to the lack of necessary studies in the field of road extraction with Sentinel 1 radar images, it is not possible to comment definitively on the results and it is suggested that more studies be done in this field.

Environmental pollution and disasters have gradually increased with population growth. The presence of oil resources in the seas and the incidents related to their discovery, extraction and transportation cause the formation of oil slicks on the sea surface, and the leakage of these petroleum products into the seas has irreparable environmental consequences. That is why monitoring the effects of these accidents is very important for public health. Satellite missions are a very effective tool for detecting pollutants such as oil spills. Artificial Aperture Radar Sensor (SAR) is an active microwave detection system that can be used to detect oil leaks with optical sensors installed on Landsat-8, Sentinel-2 and Ester satellite systems, taking into account cloud cover and satellite re-visit time at the same location. Be. In this study, the oil spill area due to pipe leakage in Khark Island was studied with Landsat-8 and Sentinel-1 satellite images. Various image processing techniques were applied to Landsat-8 bands to highlight oil spills in connection with the accident, such as morphology and convolution filters. We used Landsat-8 images to support the Sentinel-1 results. Oil spills were successfully detected by analyzing SAR data and Landsat-8 results, and by visually interpreting the results, the selected methods are consistent in terms of displaying oil spill areas.

Rapid urban growth, especially in developing countries, is causing a large number of urban planning problems. Although only three percent of the global land surface is covered by urban areas, approximately 54% of the world’s population lives in urban centers; according the latest estimates, by 2050 it will increase to nearly 65%. Accurate information on Urban Land Cover (ULC) types and their spatial distribution are of paramount importance for urban planning and management. To date, many studies have been conducted in the context of ULC mapping, and several methodologies and datasets have been used (e.g. land surveying and satellite data) in this regard. Under this background, generating ULC maps using land surveying method is considered as the most accurate technique, however, it is a costly and time-consuming task. Spending the least time and cost to produce these maps is one of the main challenges for city managers. To address this issue, the integration of satellite images and state-of-art classification methods has been received considerable attention in recent years. This study seeks to produce a 10 m resolution ULC map for Tabriz city, locating North East of Iran, using Sentinel-2 satellite data. The present study also aims to compare the potential of two advanced classifiers including Random Forest (RF) and Deep Neural Network (DNN) in ULC mapping. Five ULC classes including bare land, built-up areas, road, vegetation, and water were considered in this regard. As the number of trees (ntree) and the number of variables (mtry) are two main criteria applying the RF algorithm. In this study, ntree was set to 100 and the mtry was set to the square root of the total number of input features. In the case of DNN, a DNN model with six layers, including one input layer with 10 neurons (bands 2-8A and 11-13 of sentinel-2), four hidden layers with 200 neurons per layer, and one output layer (five ULC classes). In this study, the ReLU activation function was used for the hidden layers, softmax activation function was used for classifying information in the output layer. Our findings illustrated that the DNN algorithm by providing 95.2% overall accuracy outperformed RF (overall accuracy = 93.1%). Analyzing the performance of two algorithms regarding ULC classes showed that the DNN algorithm provided better results in bare land and built-up classes; the user’s accuracy and producer’s accuracy of bare land class were respectively 9.6% and 1% higher than those of RF. Regarding the built-up class, these metrics were also higher than RF (user’s accuracy = + 0.3% and producer’s accuracy = + 4.3%). In contrast, the RF algorithm performed better in extracting the road class; the user’s accuracy and producer’s accuracy of road class were 3.65% and 4.1% more than those of DNN, respectively. RF and DNN showed the same performances in classifying vegetation and water classes. In general, both algorithms provided good performances in ULC classification, however, the overall performance obtained by the DNN algorithm was substantially higher than RF. Because the performance of the DNN algorithm is better than the RF algorithm, we concluded that DNN is a valid alternative tool that should be considered for ULC mapping.

In recent years, the worldchr('39')s population has been growing, reaching about 7.7 billion in 2018. In the next five years, the worldchr('39')s population will increase to about ten billion, thus increasing the demand for food, water resources and fertile land for food production. Therefore, precision agriculture is trying to improve the quantity and quality of agricultural products by using new technologies. Precision farming is a new concept in modern agriculture and is based on the existence of heterogeneity at the farm level. One of the reasons for the growth of precision agriculture among scientists and farmers is the advancement of technology in various fields such as global coordinate system, imaging sensors and spatial information management tools. The use of remote sensing allows us to control a very large space in a very short time, which will reduce heavy costs. Along with rice and wheat, potatoes are the third most important food crop in the world, fed by more than one billion people, and the demand for this crop has increased as the population has grown; Therefore, better methods of product protection and management need to be used to improve production. In order to build the whole model, the images of the region were considered during the months of March to October in a period of two years, and pre-processing such as removing the cloud and other errors were done in order to have images with the least error. The NDVI index was then calculated using red and infrared bands (Figure 3). By calculating the NDVI index, useful information about the plant such as health, plant vigor, presence or absence of fall, and proper irrigation were obtained. On the other hand, land information, agricultural lands have been collected through questionnaires. For modeling, we deal with two sets of terrestrial and satellite data. Separate use of each of them will be associated with shortcomings and shortcomings; Therefore, for modeling, a combination of terrestrial and satellite information was used to build a model with minimal deficiencies and defects. It is common for modeling to work repetitively to obtain the best algorithm, so linear regression is the best choice for modeling. Univariate method was used for modeling and the date of 28 June was selected as the modeling interval. Because using multivariate methods, due to the fact that there was a high correlation in the information of different time periods, a lot of duplicate information was generated, and for this reason, we used the univariate method. Four machine learning algorithms were used for modeling, which Gaussian methods and support vector showed the best results. According to the production estimation model, it is possible to predict the amount of potatoes produced in each agricultural land and its health status with 50% accuracy three months before the harvest. The results of this study showed that estimation models will provide very valuable information for better management of crops to be used by agricultural planners to avoid wasting resources and prevent imports.

The subsidence caused by the increase in the exploitation of groundwater resources has a long history compared to its restoration potential in some internal regions of Iran. In recent years, land subsidence has caused serious environmental hazards in various plains and regions of Iran. Land subsidence is one of the environmental hazards that humans have faced in recent decades due to uncontrolled extraction of groundwater resources. This phenomenon, like other hazards such as earthquakes, droughts, floods, storms, landslides, etc., is one of the obstacles to socio-economic and development. The subsidence phenomenon is usually associated with many risks. This phenomenon can cause infrastructural, natural and ultimately damage to society and the economy. In recent years in Iran, with the increase in subsidence, buildings, airports, bridges, tunnels, streets, rail and road lines have been severely damaged. For this purpose, in this study, the amount of land subsidence in Kerman airport was analyzed using Sentinel-1 satellite radar images from 2014 to 2020 in Snap software using radar interference measurement technique. The subsidence rate of this range from 2014 to 2017 is at least 3.3 cm and maximum 6 cm, from 2017 to 2020 at least 3.6 cm and maximum 2.13 cm and from January 2020 to April was a minimum of 0.03 cm and a maximum of 13.2 cm, which shows a significant increase. Increased utilization of groundwater resources, compaction due to land drainage and compression due to the imposition of heavy loads (aircraft, structural loads, etc.) can be mentioned as main factors of subsidence in this area.

One of the economic challenges facing developing countries is the cost of tackling air pollution and improving its quality. On the other hand, in addition to cost, the health of people in the community and existing diseases are also directly related to air quality. Therefore, the study and analysis of changes in air pollutants, including nitrogen dioxide, carbon monoxide, and ozone can provide valuable information for experts to analyze air quality. The presence of high-resolution spatial sensors to study a variety of applications has enabled experts in this field to study most environmental phenomena. In the present study, temporal and spatial changes of air pollutants were determined using Sentinel-5 satellite data in April 2016, simultaneously with the release of Covid-19 virus for Iran, and compared with the values of the same period in 1998. The spread of Covid-19 virus during this period had a variety of consequences that led to a reduction in factory activity as well as a decrease in vehicle traffic. Therefore, the present study seeks to investigate the effect of these factors by analyzing the temporal changes and spatial distribution of pollutants in the interval between these two times. The results confirm the improvement of air quality in this period compared to the previous year, April 1998. According to the results, the concentration of nitrogen dioxide in the entire barley column and tropospheric nitrogen dioxide , which is directly related to transportation and human activities, has decreased.

Introduction Persistent Scatterer Interferometry (PSI) is a technique to detect and analysis of a network of coherent pixels referred as Permanent/Persistent scatterer (PS) which are stable throughout time-series of SAR images. This technique has been applied to monitor and measure phenomena such as earth subsidence fault movements and earthquake volcanic activity and other geological and environmental studies. In all PSI techniques, the processing is carried out only on the PS pixels. Therefore, high density and phase quality of these pixels are the most effective factors on the results of these techniques. The main challenge of this technique is to detect the coherent pixels over non-urban areas which suffer from the temporal decorrelation. Nowdays and with the development of polarimetric SAR sensors, polarimetric radar data are available. Polarimetric data consist of several conventional SAR acquisitions, usually addressed as channels. Each channel in a PolSAR acquisition is sensitive to different geometric characteristics of the scene. This additional redundancy over the scene may allow to increase both quality and density of the PS pixels. Therefore, the combination of polarimetry and interferometry enables to improve the effectiveness of PSI techniques, especially in non-urban areas. In this paper, we employ a method to improve the conventional PSInSAR algorithm for detecting PSC by using polarimetric optimization method on dual-pol SAR data. The improvement of this research is based on minimizing ADI criterion by means of an Exhaustive Search Polarimetric Optimization method to increase the number of PSCs. Materials & Methods 2.1 Dataset Description   The proposed method is tested using a dataset of 17 dual-pol SAR data (VV/VH) acquired by Sentinel1-A satellite March 2017 and October 2017.    2.2 Polarimetric SAR Interferometry   A general formulation for polarimetric SAR interferometry was proposed in (Cloude & Papathanassiou, 1997). The scattering matrix S represents the polarimetric information associated to each pixel of the scene. Considering a monostatic configuration, the scattering matrix S is defined as follows:   (1) where , are copolar terms,  is the cross polar term. This can be represented with the target scattering vector  using the Pauli basis as: (2) where  is the transpose operator. In Sentinel-1configuration (VV/VH), where there is no knowledge of  , scattering vector  can be written as: (3) In order to generate an interferogram, each target vector  can be projected onto a unitary complex vector  . Result of this step obtains the scattering coefficient μ defined as: (4) where i corresponds to two images, and * represents the conjugate operator. The scattering coefficient μ is a new channel or polarization state which is a linear combination of the Pauli vector elements. In this regard, all interferometry techniques can be extended from single-pol configuration to a desired polarization state by using (4) and (5). The projection vector for dual-pol data defined as:   (5) where  and  are two real parameters whose ranges are finite and known and are related to the geometrical and electromagnetic properties of the targets. The parameter  represents the type of scattering mechanism and  corresponds to orientation of scattering. In our research, the main purpose of polarimetric optimization is to search in a two-dimensional space,  and , to find an optimum projection vector, .   2.3 Amplitude Dispersion Index Optimization   In order to generate a polarimetric form of , it is sufficient to replace scattering coefficient, , in (6) by  as define in (4):       (6)       (7)   The main issue in the ADI optimization is finding a projection vector  for each pixel, which leads to minimize the  value. Results & Discussion Our results confirm that the algorithm substantially improves the PSInSAR performance, increasing the number of PS pixels with respect to standard PSI, and increasing the phase quality of selected pixels. The results reveal that using the optimum scattering mechanism increases the number of PSC about 2.6 times and PS density about 2 times than using single channel datasets. Also, the effectiveness of the method is evaluated in urban and non-urban regions. The experimental results showed that the method was successful to increase the final set of PS pixels in both regions significantly. Conclusion In summary, it can be inferred that the polarimetric optimization method is successful to increase the number of the final set of PS pixels in different regions, significantly.