This study aims to analyze the differences of each satellite product and their responses between the dry and rainy seasons by investigating seasonal floods and spectral indices based on Hierarchical Split-Based Approach (HBSA) from Sentinel-1and Sentienl-2 imagery, respectively, together with Climate Hazards group Infrared Precipitation with Stations (CHIRPS) precipitation data. The spectral indices calculated from Sentinel-2 images at different dates allow to monitor environmental changes, such as the dynamics of floods and land use and land cover. Sentinel-1 data is suitable to map flood extents by applying threshold algorithms to help differentiate water bodies from other land cover types, while CHIRPS rainfall provides essential information for seasonal variations in flooding. Using these three satellite products, this study is to understand the relationships of each satellite responds and which veriable are best suited to describing flooding on a local scale. The precipitation exhibited significant variability throughout both seasons. The rainy season saw the highest precipitation in September, while the dry season also showed in November, May, and April. The lowest precipitation occurred in June (i.e., rainy season) and January to March and December (dry season). The water bodies varied significantly, with the highest in October and November (rainy season), and the lowest in June, July, and February (dry season). The Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Water Index proposed by GAO (NDWI-GAO) values remained consistent during the rainy season compared to the dry season with significant difference, possibly due to their detection of flooded rice fields with irrigation. These results show the relationships between satellite observation data and further study will be needed to validate the possibility of using them to predict flooding for early warnings.