In past decades, the reckless exploitation of rare earth mines devastated the ecological environment. Under strict regulation and governance, the exploitation has gradually gotten back on track in recent years. In this regard, timely and accurate assessment of the ecological environment quality of rare earth management areas is indispensable for regional mine development planning, ecological protection, and sustainable development. Being relatively objective and providing instant results, the Remote Sensing Ecological Index (RSEI) is widely used in evaluating ecological environment quality. This paper combined Landsat 8 OLI multispectral imagery with meteorological, land type, and other data to set the Net Primary Productivity (NPP). The NPP reflects detailed regional vegetation destruction and climate variation, the greenness index of RSEI. We also used kernel principal component analysis (KPCA) to obtain the improved ecological index K-RSEI_NPP while evaluating the ecological environment quality of rare earth mining areas in southern Jiangxi and compared this with the traditional RSEI results. The results indicate that: (1) PC1 accounts for 88.51% of the results obtained based on K-RSEI_NPP, and the average correlation coefficient with each index reaches 0.757, which integrates the characteristics of the four indicators; (2) Compared with other indexes, the K-RSEI_NPP proposed in this paper can better display the detailed information of the ecological environment in the rare earth mining areas to differentiate mining areas under various statuses and cities with different vegetation coverage, and its results were consistent with the actual verification. Therefore, our K-RSEI_NPP can provide an effective basis for monitoring and evaluating the ecological environment of the mining area.

Timely and accurate monitoring of lakes’ water quality is crucial for assessing regional ecological health and implementing targeted conservation activities. Compared with traditional in situ water quality measurement methods, satellite remote sensing technology is more cost-effective and convenient, and also enables long-term time-series monitoring. This study utilizes Sentinel-2 multispectral imagery, selects East Juyan Lake as the study area, and employs measured water quality data from 30 in situ sampling points as training and testing samples. Using the correlation coefficient, root mean square error, and mean absolute error as evaluation metrics, a Grid Search-based XGBoost machine-learning method is applied to invert the concentration of total phosphorus (TP), a key parameter for water quality assessment. The experiments demonstrate that: (1) The XGBoost model, after parameter tuning via Grid Search, achieved the highest inversion accuracy, with R², RMSE, and MRE values of 0.856, 0.017, and 7.20%, respectively; The average TP concentration retrieved for the lake was 0.231 mg/L. This method requires minimal manual setting of numerous training parameters, reducing human intervention. (2) The spatial distribution shows that TP is primarily enriched in the deeper central and eastern parts of the lake, while concentrations are relatively lower in the near-shore vegetation zones and the western shallow water areas. The findings provide a significant reference for remote sensing monitoring of lake water quality and can be used to predict and regulate salinity, eutrophication, and similar conditions in comparable lakes.

Scientific and rational monitoring of eco-environmental effects induced by mining activities is a prerequisite for optimizing mining planning and contributes to the advancement of ecological civilization. Remote sensing and multi-source data provide advanced methods for long-term dynamic evaluation of mining-induced eco-environmental effects. This study systematically constructs eco-environmental effect indicators tailored to mining characteristics and establishes quantitative extraction methods based on Landsat data and spectral indices. The Mine Eco-environmental Effect Index (MEEI) was developed using kernel principal component analysis (KPCA). The Heidaigou Open-pit Coal Mine in Jungar Banner was selected as the study area to validate the MEEI’s performance and analyze ecological dynamics across five key temporal phases. Results indicate the following: (1) the KPCA-based MEEI effectively integrates multi-indicator features, offering an objective representation of comprehensive eco-environmental impacts; (2) from 1990 to 2020, the ecological trajectory of the coal mine followed a pattern of “sharp deterioration → gradual slowdown → relative stabilization”, with post-mining restoration and management measures significantly mitigating negative impacts and improving regional ecological quality. This study provides a methodological framework for dynamic evaluation of mining-related eco-environmental effects, supporting sustainable mining practices and ecological governance.