Assessment of water quality parameters in Muthupet estuary using hyperspectral PRISMA satellite and multispectral images

被引:3
作者
Rahul, T. S. [1 ]
Brema, J. [1 ]
机构
[1] Karunya Inst Technol & Sci, Dept Civil Engn, Coimbatore 641114, Tamil Nadu, India
关键词
PRISMA hyperspectral data; Sentinel-2 satellite image; Surface water quality parameters; Stepwise regression models; Total dissolved solids; Chloride; Groundwater; TURBID PRODUCTIVE WATERS; REMOTE ESTIMATION; CHLOROPHYLL-A; REFLECTANCE; MODIS; MODEL; RED;
D O I
10.1007/s10661-023-11497-y
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The continuous availability of spatial and temporal distributed data from satellite sensors provides more accurate and timely information regarding surface water quality parameters. Remote sensing data has the potential to serve as an alternative to traditional on-site measurements, which can be resource-intensive due to the time and labor involved. This present study aims in exploring the possibility and comparison of hyperspectral and multispectral imageries (PRISMA) for accurate prediction of surface water quality parameters. Muthupet estuary, situated on the south side of the Cauvery River delta on the Bay of Bengal, is selected as the study area. The remote sensing data is acquired from the PRISMA hyperspectral satellite and the Sentinel-2 multispectral instrument (MSI) satellite. The in situ sampling from the study area is performed, and the testing procedures are carried out for analyzing different water quality parameters. The correlations between the water sample results and the reflectance values of satellites are analyzed to generate appropriate algorithmic models. The study utilized data from both the PRISMA and Sentinel satellites to develop models for assessing water quality parameters such as total dissolved solids, chlorophyll, pH, and chlorides. The developed models demonstrated strong correlations with R-2 values above 0.80 in the validation phase. PRISMA-based models for pH and chlorophyll displayed higher accuracy levels than Sentinel-based models with R-2 > 0.90.
引用
收藏
页数:21
相关论文
共 58 条
  • [1] Abdullah HS., 2017, J ENG, V23, P13
  • [2] Adam H., 2021, FUNDAMENTAL ANAL STE
  • [3] Estimating Water Reflectance at Near-Infrared Wavelengths for Turbid Water Atmospheric Correction: A Preliminary Study for GOCI-II
    Ahn, Jae-Hyun
    Park, Young-Je
    [J]. REMOTE SENSING, 2020, 12 (22) : 1 - 14
  • [4] Development and Application of Exceedance Model for Surface Water Quality Parameters
    Akbar, Tahir Ali
    Achari, Gopal
    Hassan, Quazi K.
    Mahmood, Qaisar
    [J]. POLISH JOURNAL OF ENVIRONMENTAL STUDIES, 2021, 30 (02): : 1497 - 1511
  • [5] A new insight for real-time wastewater quality prediction using hybridized kernel-based extreme learning machines with advanced optimization algorithms
    Alavi, Javad
    Ewees, Ahmed A.
    Ansari, Sepideh
    Shahid, Shamsuddin
    Yaseen, Zaher Mundher
    [J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2022, 29 (14) : 20496 - 20516
  • [6] [Anonymous], 2000, US GEOLOGICAL SURVEY
  • [7] Landsat-based remote sensing of lake water quality characteristics, including chlorophyll and colored dissolved organic matter (CDOM)
    Brezonik, P
    Menken, KD
    Bauer, M
    [J]. LAKE AND RESERVOIR MANAGEMENT, 2005, 21 (04) : 373 - 382
  • [8] Cahyono B.E., 2019, SINGAP J SCI RES, V9, P117, DOI [10.3923/sjsres.2019.117.123, DOI 10.3923/SJSRES.2019.117.123]
  • [9] Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties
    Chang, CW
    Laird, DA
    Mausbach, MJ
    Hurburgh, CR
    [J]. SOIL SCIENCE SOCIETY OF AMERICA JOURNAL, 2001, 65 (02) : 480 - 490
  • [10] Coppo P., 2019, PROCEEDINGS, V27, P1, DOI [10.3390/proceedings2019027001, DOI 10.3390/PROCEEDINGS2019027001]