With a length of 950 km, Karun River is the longest river in Iran. In this study, we aimed at application of Sentinel-3B satellite altimetry data as well as Sentinel-1 and Sentinel-2 satellite imagery for the estimation of Karun River discharge and validation with the in-situ data. Knowing that Level-2 altimetry data are not reliable for rivers and shallow waters, we opted to re-track the waveforms of Level-1B Sentinel-3B mission data and to test several re-tracking techniques for this purpose. The results showed that the threshold algorithm, with threshold of 90%, improves the accuracy of the time series of water level by 7. 05% and increases the correlation with the in-situ gauge data by 12. 7% as compared with those obtained via Level-2 data based on OCOG that was identified as the optimum re-tracker in this case. Next, from the estimated time series of the river’ s water level, the time series of Karun River discharge were evaluated in order to constitute our discharge estimation based on Sentinel-3B satellite altimetry data, which further to be compared with the discharge that we calculated using satellite imagery of Sentinel-1 and Sentinel-2, while taking the in-situ data as the benchmark. The river’ s discharge time series obtained from the altimetry data resulted in RMSE value of 852. 31 m 3 /s, NSE coefficient of 0. 19 m 3 /s, and correlation of 62. 40% with the in-situ river discharge time series. On the other hand, the river discharge time series obtained from satellite imagery of Sentinel-1 mission resulted in RMSE value of 165. 06 m 3 /s, NSE coefficient of 0. 94 m 3 /s, and correlation of 97. 12%, and Sentinel-2 mission the RMSE value 264. 23 m 3 /s, NSE coefficient of 0. 81 m 3 /s, and the correlation of 97. 32% with in-situ data. The overall results of this study indicates that various Copernicus satellites missions have good potentiality for Karun River discharge monitoring.

It is necessary to study the spatial-temporal trend of drought, which affects agriculture and food supply with its slow process and Multiple generating factors,Therefore, the present study was conducted to assess agricultural drought in Kermanshah using satellite indicators (Average images of Sentinel2 and Landsat8 satellites during the growing season) and terrestrial (using rainfall and temperature data from 1990 to 2020). The results of terrestrial indicators the absence of drought in 2002, 2007, 2018 and 2019. The 12-month and 24-month SPEI of 2001 and the 24-month SPI estimated 1999 in the category of very severe drought. Satellite indicators in 2015 and 2017 increased the Land Surface Temperature and in 2019 (increasing soil surface moisture and Temperature Vegetation Dryness) and 2020 estimated vegetation density. The decrease in sugar beet yield in the years 1999 to 2003 and the increase in the years 2006 to 2009, 2012/2011 and 2021/2020 confirm the high results. In the north, northeastern and eastern regions,High temperature, low rainfall and low humidity have dried the soil and negatively affected the growth, health and density of plants, but the southern region has always been humid. The presence of drought during the study period and Sentinel II images were more sensitive to vegetation indicators.

Land use maps describe the spatial distribution of natural resources, cultural landscapes, and human settlements that are essential for decision-makers. Therefore, the accuracy of maps obtained from the classification of satellite images is very effective in uncertainty for urban management. Due to the uniform quality of images in large areas at regular intervals, remote sensing images are essential for land use maps. The primary purpose of this study is to present a proposed method to create an accurate land cover map in urban areas using a combination of Sentinel-1 and Sentinel-2 data. For this purpose, the features of the backscattering coefficient VV and the two parameters obtained from the H-α decomposition method (entropy, alpha) of Sentinel-1 radar images and the features of the blue, green, red band, NDVI, NDWI, MNDWI, and SWI were extracted from Sentinel-2 Multispectral images and used as influential components to classify the urban area. To separate agricultural areas from other coatings, the SWI index was used. Elevation data have also been used to optimally distinguish complex classes with different topographies. We evaluated the extraction of effective indicators from these two datasets in an object-oriented approach based on support vector machine algorithms and random forest for land use classification. The results showed that using properties extracted from radar and Multispectral images simultaneously in the object-oriented classification method could altogether determinate the object's properties in the study area. When optical and radar data were used simultaneously for both classification algorithms, the overall accuracy classification increased. For the stochastic forest method, which provided the highest accuracy, the overall accuracy for the radar and optics data combination approach increased by 13% and 5%, respectively, compared to the radar feature approach and the optics feature approach alone. There was also a significant difference in classification accuracy at all levels between the support vector machine classification algorithm and the random forest. The results showed that the random forest classification method's overall accuracy and support vector machines were 83.3 and 79.8%, respectively, and the kappa coefficient was 0.72 and 0.68%, respectively.

District 22 of Tehran, as one of the rapidly developing urban areas, has undergone significant land use changes, making it a critical subject for environmental change studies. This research aimed to analyze and identify environmental and structural changes over the period from 2016 to 2021. Various remote sensing methods were employed, including Change Vector Analysis (CVA), regression techniques, and spectral indices.Satellite data from Sentinel-1 and Sentinel-2 were used in an integrated manner, and textural features were extracted from both optical and radar imagery. The results indicated that data fusion significantly improved classification accuracy: overall accuracy in 2016 reached 91% with a Kappa coefficient of 89%, while in 2021, overall accuracy was 86% with a Kappa coefficient of 85%. In addition to percentage changes in land use classes—construction (13%), vegetation (15%), water resources (4%), and soil (11%)—pixel-level analysis was also conducted. This enabled the identification of both the percentage and number of pixels that changed between different land use classes. For example, in the water class, the largest change was toward soil (18,300 pixels). In the road class, 21,000 pixels remained unchanged, while some transitioned to vegetation, construction, and water. Vegetation showed the highest stability with 158,000 unchanged pixels, although portions were converted to soil and construction. In the soil class, besides 42,000 stable pixels, over 156,000 pixels changed to roads and 12,000 to construction.This detailed analysis allows for more precise identification of change patterns and supports the development of targeted management strategies.

Background: Cervical cancer is the second most common type of cancer among women. Effective screening programs can help cancer detection in early phases and reduce death. Metastasis to lymph nodes is one of the most prognostic factors in patients who underwent surgery. Also, a positive result from pathology report alert oncologist as a cause of death. Sentinel lymph node biopsy has been widely studied and clinically used for many types of cancer. Methods: Two techniques exist for detecting sentinel node in cervical cancer, which are Blue dye and gamma probe with radioactive isotope (99mTc). Moreover, lymphoscintigraphy has many advantages over the stain method. Detecting the sentinel node is performed via laparoscopy or laparotomy; former method is better and more accurate. Results: Various researchers have focused on this method and its positive results; its superiority against full lymphadenectomy has been declared in previous studies. Moreover, the role of sentinel lymph nodes biopsy in cervical cancer is still being extensively studied. Sentinel lymph nodes (SLN) method has a higher accuracy level to detect metastasis. Conclusion: Hence, it can be considered as a more appropriate alternative for pelvic lymph node dissection (PLND), which is a standard technique. Altering the method to a standard clinical method needs in-depth researches and studies.

Eight new [NiL1] (1), [CuL1] (2), [CoL1] (3) and [CdL1] (4), [NiL2] (5), [CuL2] (6), [CoL2] (7) and [CdL2] (8) complexes were prepared by the reaction of two new Schiff base ligands and Ni(II), Cu(II), Co(II) and Cd(II) metal ions in equemolar ratios. The ligands, H2L1 and H2L2, were synthesized by reaction of N, N'(2-aminoethyl)(3-aminopropyl)piperazine and 2-hydroxybenzaldehyde or 2-hydroxy-3-methoxy-benzaldehyde while characterized with IR and 1H, 13C NMR spectroscopy. All complexes have been studied with IR and microanalysis. Theoretical calculations of relevant complexes are done by using one of the hybrid density functional theories which is B3LYP method with GEN keyword.

In this study, eucalyptus leaves ash (ELA) was used as a low cost adsorbent for simultaneous removal of Cd(II), Ni(II) and Co(II) ions from aqueous solutions. The adsorption parameters, such as initial pH of the solution, contact time, adsorbent dose and temperature, were studied and optimized. Evaluation of the obtained data with isotherm studies indicated that the adsorption process was matched well with the Langmuir model. The maximum capacity of adsorbent for Cd(II), Co(II) and Ni(II) ions were 166.7, 27.03 and 23.8 mg g-1, respectively. Kinetic studies were carried out on various kinetic models and the pseudo-second order kinetic model was fitted very well with experimental data. Moreover, base on thermodynamic studies the adsorption process was both endothermic and spontaneous.