Please wait a minute...
 
Remote Sensing for Natural Resources    2021, Vol. 33 Issue (3) : 246-252     DOI: 10.6046/zrzyyg.2020337
|
Application and exploration of dissolved oxygen inversion of plateau salt lakes based on spectral characteristics
DU Cheng1,2,3(), LI Delin1,2,3(), LI Genjun1,2,3, YANG Xuesong1,2,3
1. Key Laboratory of Geological Processes and Mineral Resources of the Northern Qinghai-Tibet Plateau,Xining 810012, China
2. Qinghai Remote Sensing Big Data Engineering Technology Research Center, Xining 810012, China
3. Qinghai Geological Survey, Xining 810012, China
Download: PDF(3130 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

The studies on the hyperspectral inversion of salt lakes are still scarce due to the limitations of geographical conditions at present. This study explores the inversion ideas and methods of the water quality parameters of salt lakes by taking the dissolved oxygen inversion of a salt lake as an example. Based on the analyses of the hyperspectral data of the Chaerhan Salt Lake in Qinghai Province and the hyperspectral inversion technology of water quality parameters, this study determined the hyperspectral inversion model of the dissolved oxygen in the salt lake by means of waveband combination using the unique spectral information of the water body of the lake. The results show that the correlation coefficient between various wavebands of the original spectrum curve and the dissolved oxygen content was less than 0.3, while that between the band combination data in the unique spectral information of the water body and the dissolved oxygen content was greater than 0.75. According to the precision verification of the finally established band ratio model using the measured value, the inversion result of the dissolved oxygen content was roughly consistent with the measured value. It is impossible for the water quality parameters to significantly change with time owing to the relatively stable nature of the water body of the salt lake. Therefore, the verification using the measured data of November 2019 can also indicate that the waveband ratio model established based on the spectral characteristics of the salt lake enjoys high precision for a long term. Therefore, the hyperspectral inversion model can meet the precision requirements for the large-area monitoring of the dissolved oxygen in the lake area. Meanwhile, this study also proposed a new idea for the establishment of the inversion model of plateau salt lakes, which lays a foundation for the establishment of the monitoring system of plateau lakes in the future.

Keywords hyperspectrum      spectral characteristics of a salt lake      dissolved oxygen inversion     
ZTFLH:  TP79  
Corresponding Authors: LI Delin     E-mail: 1456308204@qq.com;104014137@qq.com
Issue Date: 24 September 2021
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Cheng DU
Delin LI
Genjun LI
Xuesong YANG
Cite this article:   
Cheng DU,Delin LI,Genjun LI, et al. Application and exploration of dissolved oxygen inversion of plateau salt lakes based on spectral characteristics[J]. Remote Sensing for Natural Resources, 2021, 33(3): 246-252.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020337     OR     https://www.gtzyyg.com/EN/Y2021/V33/I3/246
Fig.1  Location map of the study area and sampling points
Fig.2  Comparison before and after strip noise removal
Fig.3  Comparison of spectral curves before and after atmospheric correction in the salt lake water
Fig.4  Data processing and research process
Fig.5  Correlation between original spectral reflectance of salt lake and dissolved oxygen content
Fig.6  Correlation between multi-band combined reflectivity of salt lake spectrum and dissolved oxygen content
Fig.7  Salt lake dissolved oxygen polynomial model
实测值 预测值 实测值 预测值
7.09 7.19 9.33 9.29
7.37 7.39 9.53 9.49
7.9 7.89 9.95 9.91
7.96 7.95 10.3 10.27
8.4 8.37 10.44 10.40
Tab.1  Comparison and verification of 10 sets of measured data and predicted data of dissolved oxygen(mg/L)
Fig.8  Retrieval results of dissolved oxygen in salt lake water
实测点 经度 纬度 实测值/
(mg·L-1)
预测值/
(mg·L-1)
1 E95°5'44.99″ N37°1'5.27″ 8.9 8.7
2 E95°6'2.17″ N37°1'19.17″ 9.8 9.2
3 E95°5'59.83″ N37°1'26.60″ 8.2 8.8
4 E95°5'32.88″ N37°1'25.63″ 7.7 7.8
5 E95°5'44.99″ N37°1'49.23″ 7.3 7.6
Tab.2  Comparison of the measured data and model forecast data of Salt Lake Group
Fig.9  Analysis of measured and predicted values of dissolved oxygen in salt lake water
[1] 刘国新, 李长俊. 对青海省盐湖资源开发利用管理工作的探讨[J]. 青海国土经略, 2019(2):22-26.
[1] Liu G X, Li C J. Discussion on the development and utilization of salt lake resources in Qinghai Province[J]. Journal of Qinghai Homeland Economic Strategy, 2019(2):22-26.
[2] 锁贺祥, 何刚, 毛亚旻. 青海盐湖资源综合开发利用的现状、问题及对策[J]. 攀登, 2006(5):72-74.
[2] Suo H X, He G, Mao Y M. The status quo,problems and countermeasures of comprehensive development and utilization of salt lake resources in Qinghai[J]. Climbing Magazine, 2006(5):72-74.
[3] 祝令亚. 湖泊水质遥感监测与评价方法研究[D]. 北京:中国科学院研究生院(遥感应用研究所), 2006.
[3] Zhu Y L. Research on remote sensing monitoring and evaluation methods of lake water quality[D]. Beijing:Graduate School of Chinese Academy of Sciences(Institute of Remote Sensing Applications), 2006.
[4] 江辉. 基于多源遥感的鄱阳湖水质参数反演与分析[D]. 南昌:南昌大学, 2011.
[4] Jiang H. Inversion and analysis of water quality parameters of Poyang Lake based on multi-source remote sensing[D]. Nanchang:Nanchang University, 2011.
[5] 宋挺, 周文鳞, 刘军志, 等. 利用高光谱反演模型评估太湖水体叶绿素a浓度分布[J]. 环境科学学报, 2017, 37(3):888-899.
[5] Song T, Zhou W L, Liu J Z, et al. Evaluation of Chlorophyll a concentration distribution in Taihu Lake using hyperspectral inversion model[J]. Acta Scientiae Circumstantiae, 2017, 37(3):888-899.
[6] 陈瑶, 黄长平, 张立福, 等. 水体COD光谱特性分析及遥感反演模型构建[J]. 光谱学与光谱分析, 2020, 40(3):824-830.
[6] Chen Y, Huang C P, Zhang L F, et al. Analysis of water COD spectral characteristics and construction of remote sensing inversion model[J]. Spectroscopy and Spectral Analysis, 2020, 40(3):824-830.
[7] 王冰, 安慧君, 吕昌伟. 基于多源遥感数据的呼伦湖溶解氧反演模型[J]. 生态学杂志, 2013, 32(4):993-998.
[7] Wang B, An H J, Lyu C W. Retrieval model of dissolved oxygen in Hulun Lake based on multi-source remote sensing data[J]. Functional Ecology, 2013, 32(4):993-998.
[8] 周亚敏, 张荣群, 马鸿元, 等. 基于BP神经网络的盐湖矿物离子含量高光谱反演[J]. 国土资源遥感, 2016, 28(2):34-40.doi: 10.6046/gtzyyg.2016.02.06.
doi: 10.6046/gtzyyg.2016.02.06
[8] Zhou Y M, Zhang R Q, Ma H Y, et al. Retrieving of salt lake mineral ions salinity from hyperspectral data based on BP neural network[J]. Remote Sensing for Land and Resources, 2016, 28(2):34-40.doi: 10.6046/gtzyyg.2016.02.06.
doi: 10.6046/gtzyyg.2016.02.06
[1] WANG Jiapeng, XU Jianguo, SHEN Jiaxiao, ZHANG Dengrong. Evaluating the remediation effect of heavy metal pollution in the Dexing copper mine based on hyperspectral remote sensing[J]. Remote Sensing for Natural Resources, 2023, 35(3): 284-291.
[2] GUO Xi, YE Yingcong, XIE Biyu, KUANG Lihua, XIE Wen. Inversion of available nitrogen content in hilly paddy soil of southern China based on hyperspectral characteristics[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(2): 94-99.
[3] SHU Le, ZHANG Qin-Yu, ZHU Jun, ZHANG Deng-Rong. A General Approach for Suppressing Vegetation in Optical Remotely Sensed Imagery[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(2): 38-42.
[4] YANG Fei, ZHANG Bai, LIU Zhi-Meng, LIU Dian-Wei, WANG Zong-Meng, SONG Kai-Shan. A STUDY OF CORN FPAR ESTIMATION FROM HYPERSPECTRAL DATA BASED ON PCA APPROACH AND NEAR-INFRARED SHORTWAVE BANDS[J]. REMOTE SENSING FOR LAND & RESOURCES, 2008, 20(4): 9-13.
[5] QIAN Le-xiang, PAN Xue-qin, ZHAO Qian . ADVANCES IN THE APPLICATION AND RESEARCHES OF HYPERSPECTRAL IMAGING REMOTE SENSING IN CHINA[J]. REMOTE SENSING FOR LAND & RESOURCES, 2004, 16(2): 1-6.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech