The Remote Sensing Ecological Index (RSEI) is widely used to assess environmental quality, yet the influence of weighting methods and land cover types on its outputs remains underexplored. Based on a Landsat 8 (Operational Land Imager/Thermal Infrared Sensor) images, this study investigates whether the differences between RSEI estimation techniques: objective (Principal Component Analysis, PCA), subjective (Analytical Hierarchy Process, AHP), and combined methods (Knowledge Granule Entropy and hybrid AHP-PCA) are statistically significant and how they interact with land use and land cover (LULC) types in Blida Province, Algeria. The findings reveal that, although all methods exhibit strong correlations (r > 87%), they also show significant differences in Euclidean distance (d > 21), highlighting notable methodological impacts on RSEI values. The hybrid AHP-PCA method showed the highest consistency with others, offering optimal performance. Across LULC types, the techniques successfully differentiated forests from other classes, as well as between farmland and bare land (p < 0.05). However, they produced relatively inconsistent results when the comparison involved urban areas. Within each LULC type, significant differences were observed between the standard RSEI and the advanced techniques, particularly in forested and urban areas. In contrast, for farmland and bare land, all techniques yielded statistically similar results, indicating no significant differences. These findings highlight the importance of selecting appropriate RSEI methods based on specific land cover characteristics and support the use of statistical analysis for robust comparison. This study contributes to the development of standardized, LULC-sensitive ecological assessment frameworks leveraging remote sensing data.
