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A good ecological environment is conducive to the pursuit of high quality of life and sustainable socio-economic development of human beings, and the rapid development of remote sensing technology provides great convenience for ecological environment monitoring. In this study, we construct a remote sensing ecological index (RSEI) model through principal component (PC) analysis method using four indicators of Normalized Difference Vegetation Index (NDVI), Soil humidity (WET), Land Surface Temperature (LST)and Normalized Difference Build and Soil Index (NDBSI) based on the Google Earth Engine (GEE) platform to evaluate the ecological environment quality of the nine cities of Shandong Province in the Lower Yellow River Region. The results show that (1) the overall trend of remote sensing ecological index in nine cities of the Lower Yellow River Region from 2001 to 2021 expresses an increasing trend, indicating that the ecological environment quality is improving. (2) The areas with higher ecological quality grades are distributed in the woodland area near Weishan Lake, and the area with higher vegetation cover near Mountain Tai, and the area at the mouth of the Yellow River, while the areas with lower grades are mainly distributed in areas such as urban construction land and unused land in the mountains. (3) The contribution rate of PC1 eigenvalues of RSEI in nine cities is around 60%, and NDVI and WET indexes are positively related to the ecological environment quality in nine cities in the Lower Yellow River Region, while LST and NDBSI are negatively related. (4) The overlay analysis of remote sensing ecological indices and land use types shows that forest land and grassland contribute to the improvement of ecological environment, and building land as well as unused land play a suppressive role in the improvement of ecological environment quality.
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