Remote sensing of CDOM and DOC in alpine lakes across the Qinghai-Tibet Plateau using Sentinel-2A imagery data

Ge Liu; Sijia Li; Kaishan Song; Xiang Wang; Zhidan Wen; Tiit Kutser; Pierre-Andrew Jacinthe; Yingxin Shang; Lili Lyu; Chong Fang; Ying Yang; Qian Yang; Baohua Zhang; Shuai Cheng; Junbin Hou

doi:10.1016/j.jenvman.2021.112231

Remote sensing of CDOM and DOC in alpine lakes across the Qinghai-Tibet Plateau using Sentinel-2A imagery data

J Environ Manage. 2021 May 15:286:112231. doi: 10.1016/j.jenvman.2021.112231. Epub 2021 Mar 8.

Authors

Ge Liu¹, Sijia Li², Kaishan Song³, Xiang Wang⁴, Zhidan Wen⁵, Tiit Kutser⁶, Pierre-Andrew Jacinthe⁷, Yingxin Shang⁸, Lili Lyu⁹, Chong Fang¹⁰, Ying Yang¹¹, Qian Yang¹², Baohua Zhang¹³, Shuai Cheng¹⁴, Junbin Hou¹⁵

Affiliations

¹ Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Shengbei Street NO.4888, 130102, Changchun, PR China. Electronic address: liuge@iga.ac.cn.
² Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Shengbei Street NO.4888, 130102, Changchun, PR China. Electronic address: lisj983@nenu.edu.cn.
³ Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Shengbei Street NO.4888, 130102, Changchun, PR China. Electronic address: songkaishan@iga.ac.cn.
⁴ Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Shengbei Street NO.4888, 130102, Changchun, PR China. Electronic address: wangxiang5690@outlook.com.
⁵ Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Shengbei Street NO.4888, 130102, Changchun, PR China. Electronic address: wenzhidan@iga.ac.cn.
⁶ Estonian Marine Institute, University of Tartu, Mäealuse 14, 12618, Tallinn, Estonia. Electronic address: tiit.kutser@ut.ee.
⁷ Department of Earth Sciences, Indiana University-Purdue University, 420 University Blvd., 46202, Indianapolis, IN, USA. Electronic address: pjacinth@iupui.edu.
⁸ Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Shengbei Street NO.4888, 130102, Changchun, PR China. Electronic address: goodlucksyx27@163.com.
⁹ Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Shengbei Street NO.4888, 130102, Changchun, PR China. Electronic address: lvlili@iga.ac.cn.
¹⁰ Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Shengbei Street NO.4888, 130102, Changchun, PR China. Electronic address: fangchong1991@gmail.com.
¹¹ Tianjin Research Institute for Water Transport Engineering, Tianjin, 30456, PR China. Electronic address: 39423706@qq.com.
¹² Jilin Jianzhu University, Changchun, 130118, PR China. Electronic address: jluyangqian10@hotmail.com.
¹³ School of Environment and Planning, Liaocheng University, 252000, PR China. Electronic address: zhangbaohua@lcu.edu.cn.
¹⁴ School of Environment and Planning, Liaocheng University, 252000, PR China. Electronic address: gaorenban@lcu.edu.cn.
¹⁵ Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Shengbei Street NO.4888, 130102, Changchun, PR China. Electronic address: 282019432@qq.com.

PMID: 33706125
DOI: 10.1016/j.jenvman.2021.112231

Abstract

As important components of dissolved organic matter (DOM) in an aquatic environment, colored DOM (CDOM) and dissolved organic carbon (DOC) play an essential role in the carbon cycle of an inland aquatic system. Traditionally, CDOM and DOC in inland waters have been primarily determined using in situ observations and laboratory measurements. Most of past lake investigations on CDOM and DOC focused on easily accessible regions and covered a small fraction of lakes worldwide. To our knowledge, little is known about lakes in less accessible areas like the Qinghai-Tibet Plateau (QTP). To address this challenge, optical satellite remote sensing might be useful for capturing a synoptic view of CDOM and DOC with high frequency at large scales, complementing in situ sampling methods for inland waters. In this study, 216 samples collected from 36 lakes across the QTP (2014-2017) were examined to determine the relationships between CDOM absorption coefficient at 350 nm (a₃₅₀) and Sentinel-2A Multi Spectral Instrument (MSI) imagery reflectance data. A strong positive linear correlation with a₃₅₀ was observed with B4/B2 (R² = 0.78, p < 0.01) and with B4/B3 (R² = 0.62). A multi-step regression model was established for estimating a₃₅₀ with B4/B2 and B4/B3 as input variables (R² = 0.81, p < 0.01). A scattered CDOM-DOC relationship was revealed (R² = 0.34, p < 0.05) using a pooled dataset. By dividing the inland waters into four separate groups in accordance with their salinity gradients, we were able to develop much stronger relationships (R² > 0.8, p < 0.01) for CDOM-DOC. Significant differences between fresh and saline waters were demonstrated using satellite-derived CDOM and DOC, where high CDOM (0.86 ± 0.67 m^-1) and low DOC (3.76 ± 4.92 mg L^-1) concentrations were observed for freshwaters, while inverse trends of CDOM (0.53 ± 0.72 m^-1) and DOC (15.76 ± 17.07 mg L^-1) were demonstrated for saline lakes in the Tibetan Plateau. This study confirmed that satellite optical imagery can be used for the monitoring of CDOM and DOC of the lakes of the Tibetan Plateau, which are sensitive to a changing climate and are infrequently investigated due to the harsh environment and poor accessibility. Moreover, it highlighted the importance of combining salinity and remote sensing data in the process of estimating lake DOC.

Keywords: CDOM; DOC; Freshwater; Remote sensing; Saline water.

MeSH terms

Carbon* / analysis
Environmental Monitoring
Lakes*
Remote Sensing Technology
Tibet

Substances

Carbon