Deriving Water Quality Parameters Using Sentinel-2 Imagery: A Case Study in the Sado Estuary, Portugal

被引:54
|
作者
Sent, Giulia [1 ]
Biguino, Beatriz [1 ,2 ]
Favareto, Luciane [1 ]
Cruz, Joana [1 ]
Sa, Carolina [1 ,5 ]
Dogliotti, Ana Ines [3 ]
Palma, Carla [2 ]
Brotas, Vanda [1 ,4 ]
Brito, Ana C. [1 ,4 ]
机构
[1] Univ Lisbon, MARE Marine & Environm Sci Ctr, Fac Ciencias, P-1749016 Lisbon, Portugal
[2] Inst Hidrog, Rua Trinas 49, P-1249093 Lisbon, Portugal
[3] Univ Buenos Aires, Inst Astron & Fis Espacio IAFE, CONICET, Pabellon IAFE,Ciudad Univ,C1428EGA, Buenos Aires, DF, Argentina
[4] Univ Lisbon, Dept Biol Vegetal, Fac Ciencias, P-1749016 Lisbon, Portugal
[5] Portugal Space, Estr Laranjeiras, P-1500423 Lisbon, Portugal
基金
欧盟地平线“2020”;
关键词
monitoring; remote sensing; WFD; transitional waters; water policy; suspended particulate matter; chlorophyll-a; CDOM; turbidity; ATMOSPHERIC CORRECTION ALGORITHMS; DISSOLVED ORGANIC-MATTER; COASTAL WATERS; CHLOROPHYLL-A; INLAND; MODEL; PHYTOPLANKTON; PERFORMANCE; RETRIEVAL; CDOM;
D O I
10.3390/rs13051043
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Monitoring water quality parameters and their ecological effects in transitional waters is usually performed through in situ sampling programs. These are expensive and time-consuming, and often do not represent the total area of interest. Remote sensing techniques offer enormous advantages by providing cost-effective systematic observations of a large water system. This study evaluates the potential of water quality monitoring using Sentinel-2 observations for the period 2018-2020 for the Sado estuary (Portugal), through an algorithm intercomparison exercise and time-series analysis of different water quality parameters (i.e., colored dissolved organic matter (CDOM), chlorophyll-a (Chl-a), suspended particulate matter (SPM), and turbidity). Results suggest that Sentinel-2 is useful for monitoring these parameters in a highly dynamic system, however, with challenges in retrieving accurate data for some of the variables, such as Chl-a. Spatio-temporal variability results were consistent with historical data, presenting the highest values of CDOM, Chl-a, SPM and turbidity during Spring and Summer. This work is the first study providing annual and seasonal coverage with high spatial resolution (10 m) for the Sado estuary, being a key contribution for the definition of effective monitoring programs. Moreover, the potential of remote sensing methodologies for continuous water quality monitoring in transitional systems under the scope of the European Water Framework Directive is briefly discussed.
引用
收藏
页码:1 / 30
页数:27
相关论文
共 50 条
  • [41] Forest diversity estimation using Sentinel-2 and RapidEye imagery: A case study of the Northern Pindos National Park
    Chrysafis, Irene
    Mallinis, Giorgos
    Korakis, Georgios
    Dragozi, Eleni
    SEVENTH INTERNATIONAL CONFERENCE ON REMOTE SENSING AND GEOINFORMATION OF THE ENVIRONMENT (RSCY2019), 2019, 11174
  • [42] Monitoring Duckweeds (Lemna minor) in Small Rivers Using Sentinel-2 Satellite Imagery: Application of Vegetation and Water Indices to the Lis River (Portugal)
    Gerardo, Romeu
    de Lima, Isabel P.
    WATER, 2022, 14 (15)
  • [43] Remote sensing retrieval of inland water quality parameters using Sentinel-2 and multiple machine learning algorithms
    Tian, Shang
    Guo, Hongwei
    Xu, Wang
    Zhu, Xiaotong
    Wang, Bo
    Zeng, Qinghuai
    Mai, Youquan
    Huang, Jinhui Jeanne
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2023, 30 (07) : 18617 - 18630
  • [44] Remote sensing retrieval of inland water quality parameters using Sentinel-2 and multiple machine learning algorithms
    Shang Tian
    Hongwei Guo
    Wang Xu
    Xiaotong Zhu
    Bo Wang
    Qinghuai Zeng
    Youquan Mai
    Jinhui Jeanne Huang
    Environmental Science and Pollution Research, 2023, 30 : 18617 - 18630
  • [45] Monitoring Coastal Water Body Health with Sentinel-2 MSI Imagery
    Lock, Marcelle
    Saintilan, Neil
    van Duren, Iris
    Skidmore, Andrew
    REMOTE SENSING, 2023, 15 (07)
  • [46] Water Quality Retrieval from Landsat-9 (OLI-2) Imagery and Comparison to Sentinel-2
    Niroumand-Jadidi, Milad
    Bovolo, Francesca
    Bresciani, Mariano
    Gege, Peter
    Giardino, Claudia
    REMOTE SENSING, 2022, 14 (18)
  • [47] Phycocyanin Monitoring in Some Spanish Water Bodies with Sentinel-2 Imagery
    Perez-Gonzalez, Rebeca
    Soria-Perpinya, Xavier
    Soria, Juan Miguel
    Delegido, Jesus
    Urrego, Patricia
    Sendra, Maria D.
    Ruiz-Verdu, Antonio
    Vicente, Eduardo
    Moreno, Jose
    WATER, 2021, 13 (20)
  • [48] Index-Based Identification of Surface Water Resources Using Sentinel-2 Satellite Imagery
    Sekertekin, Aliihsan
    Cicekli, Sevim Yasemin
    Arslan, Niyazi
    2018 2ND INTERNATIONAL SYMPOSIUM ON MULTIDISCIPLINARY STUDIES AND INNOVATIVE TECHNOLOGIES (ISMSIT), 2018, : 610 - 614
  • [49] Object-based water body extraction model using Sentinel-2 satellite imagery
    Kaplan, Gordana
    Avdan, Ugur
    EUROPEAN JOURNAL OF REMOTE SENSING, 2017, 50 (01) : 137 - 143
  • [50] Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam
    Kinh Bac Dang
    Manh Ha Nguyen
    Duc Anh Nguyen
    Thi Thanh Hai Phan
    Tuan Linh Giang
    Hoang Hai Pham
    Thu Nhung Nguyen
    Thi Thuy Van Tran
    Dieu Tien Bui
    REMOTE SENSING, 2020, 12 (19) : 1 - 26