kNDVI is an advanced vegetation index designed to address the saturation issue of traditional NDVI in high-vegetation-cover areas and more accurately reflect the biophysical and physiological state of vegetation. By integrating kernel methods and machine learning principles, kNDVI enhances NDVI's performance across different biomes, climatic zones, and complex phenological cycles. Compared with NDVI, kNDVI better captures seasonal dynamics, gross primary productivity (GPP), and sun-induced chlorophyll fluorescence (SIF), particularly in tropical and arid regions [24]. In vegetation research, kNDVI has been widely applied to monitor vegetation coverage, growth status, and responses to climate change and land use changes. For instance, in the Shendong mining area, kNDVI was used to analyze spatiotemporal changes in vegetation cover, revealing the relationship between vegetation restoration and mining activities [25]. Furthermore, kNDVI elucidated the spatiotemporal dynamics and future trends of vegetation cover in the ecological functional zones of the Greater Khingan Mountains area through linear regression analysis and the Mann-Kendall (MK) test for trend detection [26]. Therefore, this study will employ kNDVI instead of NDVI to analyze vegetation growth in the study area. The Huoijtu mining area is part of the Shendong mining area, which is a combination of agriculture and animal husbandry. The area has challenging natural conditions, including strong winds, sandstorms, and low precipitation. As a result, the ecosystem structure and function in this area are generally fragile [27]. The extensive coal mining in this area has damaged the ecological environment of the mining area [28,29]. In recent decades, the vegetation growth of the Shendong mining area has changed significantly under the \"mining while controlling\" policy. Climate, soil environment, and human activities all have an impact on vegetation change, including, for example, mining land-use change and ecological restoration projects [30,31]. Therefore, it is of great scientific significance to study the change of vegetation growth and its influencing factors in mining area to further explore the sustainable development of mining areas. However, previous studies on kNDVI in vegetation growth changes have predominantly focused on areas with robust vegetation growth. Few studies have demonstratedthat kNDVI can more effectively capture growth details in areas with poor vegetation conditions. Additionally, research on the Huojitu mining area has primarily concentrated on mining technologies and ecological restoration [32; 33], with limited attention given to vegetation changes. For studying vegetation growth changes in small-scale mining areas, Landsat provides the most widely used and the most effective remote sensing resource. The high-resolution data provided by Landsat in long time series and with a global scale is widely used in monitoring land resources and the environment. Google Earth Engine (GEE), as a high-performance geospatial data processing and analysis cloud platform, effectively solves the contradiction between the increasing supply of remote sensing data and the insufficient data processing capacity [34]. GEE can quickly calculate vegetation indices such as kNDVI and realize the analysis of long-term vegetation growth changes [35; 36]. Based on the aforementioned analysis, this study employs Landsat remote sensing imagery (1999-2023) to construct a long-term kNDVI vegetation data series using the Google Earth Engine (GEE) cloud platform. First, the study aims to verify the advantages of kNDVI over NDVI in the study area. Subsequently, the MK mutation test and linear regression are utilized to quantitatively analyze the spatiotemporal evolution of vegetation growth in the Huojitu mining area, thereby clarifying the status of vegetation growth. The study also investigates the correlation between vegetation growth and air temperature as well as precipitation.