# Monitoring Water Area Dynamics in Kashgar (2003-2023) Using Multi-Source Remote Sensing Data
## 1 Introduction  
Water resources are fundamental to human survival and socio-economic development, serving as a crucial component of ecosystems [1]. They play a vital role in climate change research, ecological studies, and natural disaster monitoring [2]. Effective monitoring of surface water dynamics is essential for understanding hydrological processes and informing sustainable water resource management [3]. Surface water information extraction provides critical data to support the achievement of the United Nations Sustainable Development Goals (SDGs), specifically those related to ensuring sustainable water management and access [4]. The study of surface water dynamics has traditionally relied on discontinuous time ranges, which can obscure crucial turning points. Utilizing continuous time series remote sensing data to investigate the spatiotemporal evolution of regional surface water bodies is therefore paramount for a comprehensive understanding of hydrological processes and for informing effective water resource management strategies. Recent advancements in remote sensing technology and data processing capabilities have enabled researchers to leverage continuous long-term remote sensing data for surface water change analysis. For instance, [PERSON] et al. (2016) generated the first global 30-m resolution surface water change product using Landsat imagery on the Google Earth Engine (GEE) platform. By calculating water body frequency, they classified different water body types and quantitatively analyzedglobal surface water dynamics from 1984 to 2015 [5]. [PERSON] et al. (2018) employed a mixed index rule based on MNDWI, NDVI, and EVI to extract long-term surface water bodies in the United States from 1984 to 2016. They analyzed interannual variations in water area and explored the influence of anthropogenic activities and meteorological factors on water area dynamics [6]. Domestically, numerous studies have also been conducted. [PERSON] et al. (2018) utilized NDWI, NDVI, MNDWI, and a random forest classification method on the GEE platform to rapidly delineate annual maximum and minimum surface water extents in the middle reaches of the Yangtze River from 1990 to 2017 [7]. [PERSON] et al. (2019) developed a multi-index water body detection rule (MIWDR) using MNDWI, AWEI, NDVI, and EVI to efficiently extract long-term surface water bodies in the Yangtze River Basin from 1984 to 2018. They analyzed spatiotemporal variation characteristics and influencing factors [8]. [PERSON] et al. (2020) constructed a new global surface water dataset (Global Surface Water Extent Dataset, GSWED) based on the GEE platform, covering the period from 2000 to 2018, and analyzed long-term trends in surface water bodies across different regions [9]. [PERSON] et al. (2021) extracted surface water areas in the Irtysh River Basin using Landsat imagery from 1990 to 2019 and explored the impact of climate change and urbanization on the basin [10]. Furthermore, [PERSON] et al. (2022) extracted surface water bodies in the Lake Baikal Basin from 2013 to 2021 using GEE and deep learning models. This approach effectively reduced the time required for manual water body labeling and improved classification accuracy [11]. [PERSON] et al. (2023) employed Landsat sub-pixel mapping to map surface water bodies in Canada's Hudson Bay region from 1985 to 2021, calculating surface water area trends across the entire time series [12]. In conclusion, the study of surface water dynamics is transitioning from discontinuous time segment analysis to continuous long-term remote sensing analysis. This shift will provide crucial insights into hydrological processes and support the development of more scientifically informed water resource management strategies. Surface water area exhibits significant interannual and intra-annual variability. Consequently, delineating water bodies solely based on single remote sensing images introduces considerable uncertainty [13]. This uncertainty stems from two primary factors.