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洞庭湖是我国第二大淡水湖，干湿季节水体波动幅度大、频率高。卫星对洞庭湖水体变化的高频观测，对及时、准确地监测其水文动态变化具有重要意义。本研究的主要目标是利用Sentinel-1和Sentinel-2影像（10 m），在兼具高时频与空间分辨率条件下重建和分析2017-2020年间洞庭湖水域面积的精细时序信息。本文发展了一种基于湖区部分清晰影像来重建全湖水域面积的方法，该方法利用湖区某个区块面积与Sentinel-1全幅影像提取的湖泊面积构建的统计拟合关系建立经验模型，从而得到更密集的洞庭湖水域面积时间序列。拟合结果显示，每个区块的面积与总面积呈显著正相关，平均R2值为0.94。2017-2020年，获取119景Sentinel-1影像和38景Sentinel-2影像用于提取洞庭湖区块水体淹没范围，以重建整个湖区面积。结合所有Sentinel-1和Sentinel-2影像后，各月可获取的观测影像平均为6景，某些月内监测次数可达10次，时间间隔为3-6天。此外，本文重建的水域面积时间序列可以精确地刻画显著的季节波动和年际变化，水域面积在7月达到峰值，11月至2月达到谷值。一个月内最大面积与最小面积的平均比率为1.36。地表水域最剧烈的波动发生在11月份，比率为1.52。整合Sentinel-1/2影像观测，2017-2020年洞庭湖的平均水域面积约为1147.13 km2。与基于Sentinel-1/2影像构建的洞庭湖水域面积时间序列相比，结合Landsat-8影像的水体面积时间序列可在某些月份可以将水体观测的时间分辨率提高，但是对于水域面积的月均值和年均值影响较小。本研究联合高时空分辨率的哨兵系列雷达与光学影像发展高时频水域面积序列的精细提取方法，可为提升洞庭湖及长江中下游高动态湖泊水域遥感监测和水资源精细管理提供科技支撑。
Objective: As the second largest freshwater lake in China, Dongting Lake water body fluctuates greatly and frequently in dry and wet seasons. The high-frequency observation of water body changes in Dongting Lake by satellite is of great significance for timely and accurate monitoring of its hydrological dynamic changes. Method: The main aim of this study is to reconstruct and analyze the elaborate time series information of Dongting Lake water area from 2017 to 2020 by using Sentinel-1 and Sentinel-2 images (10m) under the condition of high time-frequency and spatial resolution. This paper develops a method to reconstruct the whole lake water area and encrypt the time series of lake area based on the partially cloudless image of the lake area. This method uses the statistical fitting relationship between the block area and total area extracted from Sentinel-1 full image to establish an empirical model, so as to obtain a dense time series of Dongting Lake water area. Results: The fitting results show that the area of each block is significantly positively correlated with the lake total area, and the average R2 value is 0.94. From 2017 to 2020, 119 Sentinel-1 images and 38 Sentinel-2 images were obtained to extract the block inundation range of Dongting Lake to reconstruct the whole lake area. After combining all Sentinel-1 and Sentinel-2 images, the average observation images available in each month are 6. In some months, the monitoring times can reach 10 times, and the time interval is 3-6 days. It can carry out fine monitoring of the water area change of Dongting Lake. In addition, the time series of water area reconstructed in this paper can accurately describe the significant seasonal fluctuations and interannual changes. The water area reaches the peak in July and the valley from November to February. The average ratio of maximum area to minimum area in a month is 1.36. The most violent fluctuation in surface waters occurred in November, with a ratio of 1.52. Integrating Sentinel-1/2 image observation time series, the average water area of Dongting Lake from 2017 to 2020 is about 1147.13 km2. Conclusion: Compared with the water area time series of Dongting Lake constructed based on Sentinel-1/2 images, the water area time series combined with landsat-8 image can improve the time resolution of water observation, but it has little effect on the monthly and annual mean of water area. This study combines high temporal and spatial resolution sentinel series radar and optical images to develop a fine extraction method of high time-frequency water area series, which can provide scientific and technological support for improving remote sensing monitoring and fine management of water resources in Dongting Lake and high dynamic lakes in the middle and lower reaches of the Yangtze River.