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Therefore, it is necessary to develop tools to monitor the environmental quality in the LP quickly and accurately. To reveal the spatio-temporal changes in environmental quality in the LP from 2000 to 2020, we used the Moderate-Resolution Imaging Spectroradiometer (MODIS) products on the Google Earth Engine platform and constructed the remote sensing ecological index (RSEI) through principal component analysis (PCA). Then, Sen\u2013Mann\u2013Kendall methods were applied to determine the changing trend of the environmental quality of the LP. Finally, natural and anthropogenic factors affecting the environmental quality were probed using a geographical detector model. The results showed that: (1) the average RSEI values in 2000, 2010 and 2020 were 0.396, 0.468 and 0.511, respectively, displaying an upward trend from 2000 to 2020, with a growth rate of 0.005 year\u22121. The overall environment quality was moderate (0.4\u20130.6). (2) In terms of spatial distribution, the environmental quality was excellent in the southeast and poor in the northwest of the LP. The areas with improved environmental quality (84.51%) were located in all the counties, whereas the areas with degraded environmental quality (8.11%) occurred in the north and southeast of the study area. (3) Greenness, heat, wetness, dryness and land use types were prominent factors affecting RSEI throughout the study period; additionally, the total industrial gross domestic product showed a growing influence. The contribution of multi-factor interaction was stronger than that of single factors. The results will provide a reference and a new research perspective for local environmental protection and regional planning.<\/jats:p>","DOI":"10.3390\/rs14205094","type":"journal-article","created":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T22:45:29Z","timestamp":1665614729000},"page":"5094","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Dynamic Monitoring of Environmental Quality in the Loess Plateau from 2000 to 2020 Using the Google Earth Engine Platform and the Remote Sensing Ecological Index"],"prefix":"10.3390","volume":"14","author":[{"given":"Jing","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"}]},{"given":"Guijun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]},{"given":"Liping","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"}]},{"given":"Zhenhong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"}]},{"given":"Meiling","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"}]},{"given":"Chen","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"}]},{"given":"Enjun","family":"Gong","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450045, China"}]},{"given":"Huiling","family":"Long","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]},{"given":"Haitang","family":"Hu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"125534","DOI":"10.1109\/ACCESS.2020.3007786","article-title":"Spatiotemporal Change and Landscape Pattern Variation of Eco-Environmental Quality in Jing-Jin-Ji Urban Agglomeration From 2001 to 2015","volume":"8","author":"Ji","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ji, J., Tang, Z., Zhang, W., Liu, W., Jin, B., Xi, X., Wang, F., Zhang, R., Guo, B., and Xu, Z. 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