## 1 Introduction
Nevertheless, prior research is characterized by three principal limitations: (i) an emphasis on specific reaches or river bars rather than extensive spatiotemporal investigations; (ii) a focus on bar surface morphology without considering biological adjustments; (iii) a relative scarcity of information regarding the causality of river bar dynamics. However, the exploration of the variation principle concerning vegetation parameters, specifically the leaf area index (LAI), remains scant in extensive dammed river systems, despite its significance in hydrological, ecological, and geomorphic numerical simulations [28; 29]. The advent of Earth observation satellites has enhanced the practicability of monitoring large-scale and protracted river channel transformations in remote regions where in situ measurements are scarce or entirely lacking [1]. LAI is a critical biophysical parameter for monitoring vegetation dynamics, which is defined as half of the total green leaf area per unit of ground area [30]. There are other indices or parameters that can be used for the objective of this study. For example, a previous study directly used the normalized difference vegetation index (_NDVI_) to monitor vegetation dynamics [9], where _NDVI_ is just a mathematical combination of two reflectance bands. Although _NDVI_ is highly related to vegetation growth status, it is rarely used for quantitative analyese because of lacking physical meaning. Another parameter is gross primary production (GPP), which is the total amount of carbon dioxide fixed by land plants per unit of time through photosynthesis. The GPP can also be used to monitor vegetation dynamics and it can be estimated from the satellite products [31; 32], but GPP is relevant to vegetation biochemical processes, including photosynthesis and respiration. In this study, we chose to use LAI because the physical interaction between water and vegetation was our focus. With the advance of Earth observation systems over the past decades, remote-sensing satellites provide a promising way to generate long-term LAI products for large-scale missions [33]. Currently, moderate-resolution LAI products have been applied for various studies, including global vegetation change and improving simulations of land surface models [34; 35]. However, high-resolution LAI products are still required to resolve existing heterogeneity in hydrological and agricultural applications [36]. Current studies have used Landsat or Sentinel imagery to produce high-resolution LAI based on two types of methods: empirical and radiative transfer model (RTM)-based methods [37; 38]. The empirical method requires in situ LAI samples, which are sometimes not available for long-term research, and the empirical relationships cannot be transferred over space and time [33]. Therefore, the RTM-based method is popular, especially when it is integrated with a powerful machine learning (ML) algorithm, which can build non-linear relationships between satellite reflectance and LAI well [39]. Another issue is that the inherent discontinuity of spatial and temporal coverage of high-resolution satellite input owing to revisit cycle and cloud contaminations limits the production of high-quality LAI products at a fine spatial resolution [40]. To address this issue, imagery compositing is proposed to combine several observations within a month or season to generate spatiotemporally seamless remote-sensing products [41; 42; 43]. Hence, we attempt to generate spatiotemporally seamless imagery and apply it to the RTM-based method with an ML algorithm so that the high-resolution LAI products can be gained and adopted for river bar and vegetation dynamics analysis. Here, we aim to investigate river bar and vegetation dynamics in response to upstream damming using the case of the Middle Yangtze River (MYR) and the Three Gorges Dam (TGD). Specifically, we employ high-resolution river cross-sectional profiles, hydrological datasets, and remote-sensing images for the MYR covering both the pre- and post-TGD periods.