In recent decades, global warming and sea level rise, population growth, and intensification of human activities, have directly affected the coasts and as such, their monitoring for the accretion and retreat are among the issues that are considered by the coastal countries. This study, compares two supervised classification algorithms for classifying Sentinel-2 satellite imagery for shoreline extraction. Median monthly images from 2020/01 to 2021/12 are taken and classified by Random Forest (RF) and Support Vector Machine (SVM) algorithms. By validating the maps, it is found that the RF algorithm has better accuracy and as such by averaging the accuracy of all maps, the overall accuracy (OA) values of 97.18% and the kappa coefficient (KC) of 0.97, and the mean overall accuracy and kappa coefficient of maps from SVM algorithm of 85.15% and 0.79, respectively, is obtained. After extracting the shorelines, the Digital Shoreline Analysis System (DSAS) is used to calculate the displacement rate. By calculating the Linear Regression Rate (LRR) factor, it is found that in 91% of transects (166 transects) we see the shoreline retreat to land. In 54% of them, the average rate of the retreat is 5.42 meters per year and in only 9% (16 transects) we see the accretion towards the sea.

Wetlands are valuable natural resources that provide many services to both the environment and humans. Over the past several decades, climatic change and human activities have had a considerable impact on the water level of wetlands. Zarivar Lake, located in the northwestern region of Iran, represents a significant ecological unit and aquatic ecosystem. In this study, from 2015 to 2022, the relationship between seasonal changes in Zarivar Lake’s waterbody (LWB) area and weather factors like precipitation, evapotranspiration, and the temperature of the lake’s surface water (LSWT) were examined. For this purpose, the Google Earth Engine (GEE) cloud platform, a powerful and fast tool for processing the time series of images, was used. The LWB was extracted by utilizing the average images of the dual-polarized SAR Sentinel-1 imagery for each season. Furthermore, meteorological parameters encompass the utilization of the Landsat-8 satellite’s thermal band to determine LSWT by using statistical mono-window (SMW), the CHIRPS rainfall model data for assessing precipitation levels, and the employment of MODIS evapotranspiration products in the form of 8-day data. The study revealed significant correlations between variations in Zarivar Lake’s waterbody area and meteorological factors. Correlation coefficients indicated a positive relationship between LWB area and precipitation during the winter (r = 0.67) and spring (r = 0.73), while weaker positive correlations were observed in the summer (r = 0.29) and fall (r = 0.30). Conversely, the LWB area showed a relative relationship with LSWT, with positive correlations in winter (r = 0.10) and spring (r = 0.26), and negative correlations in summer (r = −0.30) and fall (r = −0.07). Additionally, evapotranspiration parameters aligned with precipitation changes throughout the seasons, highlighting the significant influence of climate on Zarivar Lake.