Abstract
Exploring data aids in the comprehension of the dataset and the system's essence. Various approaches exist for managing numerous sensors. This study perceives operational states to clarify the physical dynamics within a soil environment. Utilizing Principal Component Analysis (PCA) enables dimensionality reduction, offering an alternative perspective on the spring soil dataset. The K-means algorithm clusters data densities, forming the groundwork for an operational state description. Soil data, integral to an ecosystem, entails evident attributes. Employing dynamic visualization, including animations, constitutes a vital exploration angle. Greenhouse gas variables have been added to PCA to achieve more understanding in the interconnection of gas exchange and soil properties. Pit data and flux data are analysed both separately and together using a data-driven approach. The results look promising, showing the potential to add new values and more detailed state structures to ecological models. All experiments are conducted within the Jupyter programming environment, utilizing Python 3. The relevant literature on data visualization is examined. Through combined techniques and tools, the potential features of the soil ecosystem are observed and identified.