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.

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.

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.

Background and Objectives: Given the significance of investigating and monitoring riparian ecosystems, this project was designed to identify and map the land cover, including tree and shrub species classes, around the Zarineh Rood River in West Azerbaijan province, Iran. Recognizing that the separation of lands with high spectral similarity using single-time images is not precise, this study utilized a time series of satellite images, capitalizing on the phenological differences of plant species.Methodology: The research separated the land cover classes into two stages. In the first stage, the time series data from Sentinel 1 and 2 were used to map different classes of tree cover (natural, wood farming, orchard), shrub cover (natural, orchard), grass or pasture, agriculture, residential lands, soil, and water bodies. Given that seasonal changes in the images can provide valuable information about land cover classes, a one-year (2021) time series of Sentinel 2 optical images and Sentinel 1 radar polarizations for 2021, in the form of median in each season, were processed on the Google Earth Engine platform. The data were classified using four composites of input features and four classifiers. In the second stage, to separate the vegetation classes into Tamarix, willows, orchard, and poplar plantation, the trend of one-year changes of normalized difference vegetation index (NDVI), normalized green red difference index (NGRD), normalized difference red edge index (NDREI), and green normalized difference vegetation index (GNDVI) combined with HV polarization of Sentinel 1 radar in the form of median in seasons, was used as an input feature. The land cover map produced contained Tamarix, willows, orchard, poplar plantation, grass or pasture, agriculture, residential lands, soil, and water bodies.Results: In the first stage of classification, the input feature of NDVI (Monthly)_ Radar (Seasonal)_ Sentinel 2 (Seasonal) and the random forest classifier were the best feature and the most accurate classification algorithm, separating the classes from each other with an overall accuracy and Kappa coefficient of 88% and 0.85, respectively. In the second stage of classification, the NDVI index between the months of April and November enabled the separation of all four tree and shrub covers. GNDVI between December and April was the best indicator for separating willows. Also, between May to November, it effectively separated Tamarix. NGRDI was suitable between May and November for separating Tamarix and also separated the poplar plantations between April and November. The GNDVI index between April and September effectively separated the two categories of orchards and poplar plantations from Tamarix and willows. The map was generated using the mentioned input feature and random forest algorithm. The overall accuracy and Kappa coefficient obtained from the validation relying on ground samples and Google Earth images were 80% and 0.77, respectively. The main diagonal of the error matrix shows the highest separation between water, soil, and urban land classes. Among the vegetation classes, willows and agricultural lands exhibited the best distinction.Conclusion: The variation in a plant’s phenology, encompassing leafing, blossoming, fruiting, fall, and sleep cycle, leads to changes in the values of vegetation indicators during the seasons, which can be utilized in mapping vegetation to enhance separability. Consequently, if tree and shrub stands are pure and exhibit a different phenological behavior from their neighbors, they can be distinguished with higher accuracy using time series of satellite images.

Background: The tendency to spread to sentinel lymph node (SLN) may differ depending on the biological, clinical, and histopathological features of tumors. If the factors that affect SLN metastasis (SLNM) are known, there may be no need to perform SLN biopsy (SLNB) in some groups. Objectives: This study aimed to investigate the factors affecting SLNM in patients who underwent surgery and SLNB before (surgery group) or after (neoadjuvant chemotherapy group) systemic therapy in the light of current biological characteristics of tumors and patients. Methods: The study included patients who were operated on for breast cancer and underwent SLNB in our institute between 2017 and 2019. The study included a total of 1, 050 patients, who were divided into the surgery (n=900) and neoadjuvant chemotherapy (NAC) groups (n=150). The patients' tumor localization, tumor size, histological subtype, grade, receptor status, lymphovascular invasion (LVI) status, the number of sentinel lymph nodes removed, metastatic lymph nodes in SLNB, and axillary dissection status were analyzed in this study. Results: The study included a total of 1, 050 patients, who were assigned to the surgery (n=900) and NAC groups (n=150). Of the patients, 311 (34. 5%) cases had SLNM. In the surgery group, multivariate analyses showed that grade III, LVI, Her2 (+) increased the risk of metastasis. In the NAC group, the analyses showed Pre-NAC clinical findings of LN metastasis and luminal A subtypes as effective factors. The factors affecting SLNM were analyzed, and the univariate analyses showed that grades II and III, a tumor size of>2 cm, LVI, Her2 (+), and triple negative increased the risk of metastasis. The analyses also revealed LVI as the most important risk factor for SLN metastasis. Conclusion: Knowing the factors affecting SLNM can provide clues for the type of intervention, reconstruction, and radiotherapy planning of patients to be operated on directly or after NAC. In our study, it was found that patient age, tumor size, tumor biology, tumor grade, and especially LVI status were very important in predicting SLN positivity. It is believed that these features should be taken into account when determining the treatment strategy.