北极Lena河是世界第十长河，也是北极地区第二大河，其年径流量约占北冰洋淡水总量的20％，同时将陆地生态系统上的大量有机物携带进海洋，并且在全球碳循环中起到非常重要的作用。卫星遥感数据是监测河流有机碳循环的重要数据源，特别是在无冰期的高纬度地区。本研究的目的是：（1）建立一种高精度反演算法估算北极Lena河的溶解有机碳DOC（Dissolved Organic Carbon）浓度；（2）分析长时间序列遥感影像中DOC浓度的变化特征；（3）探讨北极Lena河的DOC浓度变化的主要驱动因素。本文中主要构建了一种基于谷歌地球引擎GEE的遥感反演算法，利用1999年—2018年的Landsat影像反演得到了北极Lena河的有色溶解有机物CDOM（Chromophoric Dissolved Organic Matter）浓度。基于CDOM与DOC这两种水体成分的实地测量值之间的强相关性（R²=0.873），本文将CDOM反演结果转换为DOC浓度。在此基础上，本文分析了近20年的北极Lena河无冰期DOC的时空动态变化。研究结果表明反演算法所表现的性能证实了使用Landsat系列不同传感器长时间监测河流DOC变化的能力。利用增强回归树模型分析了北极Lena河的DOC变化的是土地覆盖变化、流域坡度、气象因子、人类活动以及纬度地带性等众多驱动因素的共同影响，而季节性，地域性和规模性也会影响DOC浓度与上述驱动因素之间的定量关系。总之，本文的结果可以提高监测北极地区不同河流中DOC变化及其通量的能力，并加深对北极碳循环的了解。

The Arctic River Lena is the 10th longest river in the world and the second largest river in the Arctic region. The annual river discharge of the Arctic River Lena accounts for approximately 20% of the total freshwater in the Arctic Ocean. It also drains a large amount of organic matter from terrestrial ecosystems into the ocean and plays a very important role in the global carbon cycle. Satellite remote sensing data are considered a necessary supplement to the ground-based monitoring of riverine organic matter circulation, especially in high-latitude regions during the ice-free period.The objectives of this study are to (1) construct a high-accuracy retrieval algorithm to estimate the Dissolved Organic Carbon （DOC） concentration of the Arctic River Lena, (2) analyze the variation characteristics of DOC concentration over a long time series using remote sensing images, and (3) discuss the main driving factors of DOC concentration variation in the Arctic River Lena.In this paper, a remote sensing retrieval algorithm based on the Google Earth engine was constructed. Landsat images retrieved from 1999 to 2018 were used to obtain the concentration of Chromophoric Dissolved Organic Matter (CDOM) in the Arctic River Lena. Given the strong correlation between the field measurements of CDOM and dissolved organic carbon (R² = 0.873), the CDOM retrieval results were converted to DOC concentrations in this paper. Thus, this paper analyzes the temporal and spatial dynamics of DOC in the Arctic River Lena during the ice-free period over the last two decades.Results showed that the performance of the retrieval algorithm supports the feasibility of using Landsat data of different sensors to monitor riverine DOC variations. The boosted regression tree model was used to analyze the doc variation of the Artic Lena River, which is influenced by many driving factors, including land cover change, watershed slope, meteorological factors, human activities, and latitudinal zonation. The seasonality, geography, and scale could affect quantitative relationships between DOC concentration and these influencing factors.In conclusion, our results could improve the ability to monitor DOC fluxes in Arctic rivers and advance our understanding of the Arctic’s carbon cycle.