Monitoring land use and land cover is critically important for the development of environmental, social, and economic policies. These analyses not only provide information about ongoing changes but also allow us to relate them to anthropogenic factors or climate change. In recent years, access to large volumes of freely available satellite imagery, along with platforms designed for processing this type of data, has enabled a better understanding of land cover changes and the factors that drive them. In this study, we propose the use of a machine learning model to classify land cover in preservation areas during the period from 2019 to 2025. We then analyze the results of accumulated precipitation data and in situ water quality measurements. The results show that it is possible to achieve land cover classification with an accuracy of 97%. In addition, we analyzed the relationship between precipitation levels and the extent of surface water within the reservoir. The results indicate that fluctuations in surface water are consistent with the classifications derived from the machine learning model and correspond to the accumulated rainfall during the classified periods. Furthermore, we examined the relationship between areas identified as urban and non-urban and the in-situ water quality measurements. The integration of satellite imagery, meteorological data, and in situ measurements provides a robust framework for the interdisciplinary analysis of environmental dynamics and interactions.