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However, both the RSEI and its improved models have limitations in explaining the EEQ with only two-dimensional (2D) factors, resulting in inaccurate evaluation results. Incorporating more comprehensive, three-dimensional (3D) ecological information poses challenges for maintaining stability in large-scale monitoring, using traditional weighting methods like the Principal Component Analysis (PCA). This study introduces an Improved Remote Sensing Ecological Index (IRSEI) model that integrates 2D (normalized difference vegetation factor, normalized difference built-up and soil factor, heat factor, wetness, difference factor for air quality) and 3D (comprehensive vegetation factor) ecological factors for enhanced EEQ monitoring. The model employs a combined subjective\u2013objective weighting approach, utilizing principal components and hierarchical analysis under minimum entropy theory. A comparative analysis of IRSEI and RSEI in Miyun, a representative study area, reveals a strong correlation and consistent monitoring trends. By incorporating air quality and 3D ecological factors, IRSEI provides a more accurate and detailed EEQ assessment, better aligning with ground truth observations from Google Earth satellite imagery.<\/jats:p>","DOI":"10.3390\/rs16183485","type":"journal-article","created":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T04:31:23Z","timestamp":1726806683000},"page":"3485","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Evaluation of Ecological Environment Quality Using an Improved Remote Sensing Ecological Index Model"],"prefix":"10.3390","volume":"16","author":[{"given":"Yanan","family":"Liu","sequence":"first","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanlin","family":"Xiang","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1677-7837","authenticated-orcid":false,"given":"Pingbo","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ai","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2357","DOI":"10.1109\/JSTARS.2022.3157648","article-title":"A Siamese Network Based U-Net for Change Detection in High Resolution Remote Sensing Images","volume":"15","author":"Chen","year":"2022","journal-title":"IEEE J. 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