Analysis of DOC concentration variation and driving forces in the Arctic River Lena based on long-term Landsat time series

被引：0
作者：
Wu M. [1 ]
Huang J. [1 ]
Gong L. [1 ]
Jiang T. [1 ]
机构：
[1] College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao
来源：
National Remote Sensing Bulletin | 2021年 / 25卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Arctic River Lena; Boosted Regression Tree (BRT); CDOM; DOC; Google Earth Engine; Landsat; Long-term;
D O I：
10.11834/jrs.20210279
中图分类号：
学科分类号：
摘要：
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. © 2021, Science Press. All right reserved.
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页码：830 / 845
页数：15
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