## 1 Introduction
Through the exchange of water and energy between the land and the atmosphere, soil moisture and heat flux change and soil moisture is insufficient, causing vegetation to suffer physiological drought. Prolonged drought will result in ecological drought of vegetation, degradation of plant life, alterations in vegetation structure and function, and effects on the water cycle, carbon cycle, photosynthesis, and cellular respiration [21; 22]. Human activities have an impact on natural systems, causing land cover change, changing land-water vapor coupling processes, altering the global hydrothermal cycle, and amplifying extreme weather and climate events. At present, ecological drought research is mostly divided into micro and macro perspectives. Most micro-ecological drought studies are based on experiments, aiming at the response of specific vegetation communities to drought [23]. Most macro-ecological drought studies are based on drought indices [24] such as the Normalized Difference Vegetation Index (NDVI) [25], Enhanced Vegetation Index (EVI) [26], Temperature Vegetation Drought Index (TVDI) [27], and so on. From the macro perspective, remote sensing is used to carry out research, which is more convenient to obtain data and has a wider research scope. Researchers have found that there is a certain conduction effect between different drought types. Meteorological drought often has a certain impact on hydrological drought and ecological drought. [PERSON] et al. used drought propagation to explain the asymmetric response of groundwater content to drought in the study area when monitoring drought signals that change over time in the whole hydrological cycle of Illinois, USA. He was the first to define the progression from meteorological drought to hydrological drought as \"drought propagation\" [28]. In 2003, [PERSON] continued to use drought propagation on the basis of previous studies to describe the transformation of drought in groundwater circulation systems [29]. Subsequently, this definition has been widely recognized and quoted by the academic community [30]. Over the past two decades, the transition from meteorological droughts to hydrological or agricultural droughts has emerged as a focal point of research, leading to the development of numerous analytical tools for studying this phenomenon. There are two main categories of these methods: the first is the hydrological model or crop model method based on the transition process of drought events; the second is the statistical method based on the characteristics of drought events. Among them, the hydrological model/crop model method can effectively reflect the transition process of drought and reveal the transition mechanism. [PERSON] et al. used the crop simulation model (CSM) to assess the impact of different meteorological drought events on agricultural drought in Storey County, Iowa, USA [31]. Due to the large number of parameter-tuning processes involved in model training, these methods are difficult to be applied to large spatiotemporal scales. In contrast, statistical methods, which do not need to set boundary conditions and are not limited by the scope of time and space, are the main methods in drought transition research. These include the cross-wavelet method [32], the multi-variate joint distribution method [33], Granger causality analysis [34], correlation analysis [35], etc. Currently, the research of drought event transition predominantly employs statistical approaches grounded in run recognition theory, utilizing the time series of the mean drought index for the study area or its subdivisions. This method efficiently accounts for the linkage of various drought events throughout time and accurately illustrates the transition characteristics and interrelation of distinct drought types. However, there are still few spatial studies on the mechanism of meteorological drought driving ecological drought, and it is difficult to reveal the response relationship between ecological and meteorological droughts.