MRSEILA: A modified remote sensing ecological index using local adaptability for enhancing ecological environment quality assessment

The Remote Sensing Ecological Index (RSEI) has been widely applied in ecological environment quality (EEQ) assessments by integrating multiple environmental factors. To enhance RSEI's ability to capture local ecological variations, a locally adapted version (RSEI_LA) was designed and widely adopted using moving windows. However, the randomness in eigenvector directions generated by principal component analysis (PCA) can introduce bias, affecting the accuracy of RSEI_LA's assessment. To enhance the effectiveness of RSEI_LA in EEQ, we propose a modified RSEI_LA model (MRSEI_LA) implemented on the Google Earth Engine (GEE) platform, consisting of three components: (1) optimization of moving window sizes tailored to each target region; (2) automatic recognition and correction of PCA-induced eigenvector direction inconsistencies; and (3) refinement of PCA computation within each circular window to improve the accuracy of EEQ evaluations. We validated MRSEI_LA using Landsat Collection 2 Level-2 surface reflectance data and compared its performance with RSEI_LA across four typical areas in China. The results showed that, compared to RSEI_LA, MRSEI_LA consistently produces aligned eigenvector directions and more accurate EEQ assessments that better reflect actual land surface conditions across all four testing areas, making it an effective tool for regional and large-scale ecological monitoring.