A spectral space partition guided ensemble method for retrieving chlorophyll-a concentration in inland waters from Sentinel-2A satellite imagery

被引:31
|
作者
Xu, Min [1 ]
Liu, Hongxing [1 ]
Beck, Richard [1 ]
Lekki, John [2 ]
Yang, Bo [1 ]
Shu, Song [1 ]
Kang, Emily L. [3 ]
Anderson, Robert [2 ]
Johansen, Richard [1 ]
Emery, Erich [4 ]
Reif, Molly [5 ]
Benko, Teresa [6 ]
机构
[1] Univ Cincinnati, Dept Geog & Geog Informat Sci, Cincinnati, OH 45221 USA
[2] NASA, Glenn Res Ctr, Cleveland, OH 44135 USA
[3] Univ Cincinnati, Dept Math Sci, Cincinnati, OH 45221 USA
[4] US Army Corps Engineers, Great Lakes & Ohio River Div, Cincinnati, OH 45202 USA
[5] US Army Corps Engineers, ERDC, JALBTCX, Kiln, MS 39556 USA
[6] Vis & Pass Program Management, Kirtland, OH 44094 USA
关键词
Chlorophyll-a; Algal blooms; Empirical algorithms; Ensemble; In situ; Sentinel-2A; MAPPING CYANOBACTERIAL BLOOMS; REMOTE ESTIMATION; LAKES; ALGORITHMS; COASTAL; CARBON; REFLECTANCE;
D O I
10.1016/j.jglr.2018.09.002
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This research proposes an ensemble method for synergistically combining multiple empirical algorithms to better estimate chlorophyll-a (Chl-a) concentration. In previous studies, different empirical algorithms have been employed separately and a single algorithm was often identified as the most suitable predictor for Chl-a retrieval. Our ensemble method combines different individual algorithms to form an ensemble predictor that exploits advantages of each individual algorithm to maximize the overall estimation accuracy. We evaluated two ensemble predictors: the optimally weighted ensemble predictor and the spectral space partition guided ensemble predictor. The ensemble method has been successfully applied to a Sentinel-2A multispectral image acquired over Harsha Lake, Ohio in 2016. Based on in situ water reference data and satellite imagery, we constructed two ensemble predictors that consist of three individual empirical algorithms/estimators, including 2BDA (two-band algorithm), 3BDA (three-band algorithm), and NDCI (Normalized Difference Chlorophyll Index). For the optimally weighted ensemble predictor, the weights for individual algorithms are computed by solving an overdetermined linear system with the pseudoinverse technique. For the spectral space partition guided ensemble predictor, the rules for partitioning spectral space into spectral regions were established as a decision-tree using the CART method. The optimal Chl-a estimate for a pixel is obtained by selectively using the empirical algorithm in the ensemble that has the highest expected accuracy in the spectral region where the pixel is located. Our assessments suggest that the spectral space partition guided ensemble method performs significantly better than three individual empirical algorithms and also better than the optimally weighted ensemble method. (C) 2018 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:454 / 465
页数:12
相关论文
共 16 条
  • [1] Retrieving water chlorophyll-a concentration in inland waters from Sentinel-2 imagery: Review of operability, performance and ways forward
    Llodra-Llabres, Joana
    Martinez-Lopez, Javier
    Postma, Thedmer
    Perez-Martinez, Carmen
    Alcaraz-Segura, Domingo
    [J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 125
  • [2] Regionally and Locally Adaptive Models for Retrieving Chlorophyll-a Concentration in Inland Waters From Remotely Sensed Multispectral and Hyperspectral Imagery
    Xu, Mm
    Liu, Hongxing
    Beck, Richard
    Lekki, John
    Yang, Bo
    Shu, Song
    Liu, Yang
    Benko, Teresa
    Anderson, Robert
    Tokars, Roger
    Johansen, Richard
    Emery, Erich
    Reif, Molly
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 57 (07): : 4758 - 4774
  • [3] Estimation of chlorophyll-a concentration in complex coastal waters from satellite imagery
    Gilerson, Alexander
    Malinowski, Mateusz
    Herrera, Eder
    Tomlinson, Michelle C.
    Stumpf, Richard P.
    Ondrusek, Michael E.
    [J]. OCEAN SENSING AND MONITORING XIII, 2021, 11752
  • [4] Chlorophyll-a and total suspended solids retrieval and mapping using Sentinel-2A and machine learning for inland waters
    Saberioon, Mohammadmehdi
    Brom, Jakub
    Nedbal, Vaclav
    Soucek, Pavel
    Cisar, Petr
    [J]. ECOLOGICAL INDICATORS, 2020, 113
  • [5] Calibration and validation of algorithms for the estimation of chlorophyll-a concentration and Secchi depth in inland waters with Sentinel-2
    Pereira-Sandoval, Marcela
    Patricia Urrego, Esther
    Ruiz-Verdu, Antonio
    Tenjo, Carolina
    Delegido, Jesus
    Soria-Perpinya, Xavier
    Vicente, Eduardo
    Soria, Juan
    Moreno, Jose
    [J]. LIMNETICA, 2019, 38 (01): : 471 - 487
  • [6] OPERATIONAL NIR-RED ALGORITHMS FOR ESTIMATING CHLOROPHYLL-a CONCENTRATION FROM SATELLITE DATA IN INLAND AND COASTAL WATERS
    Moses, Wesley J.
    Gitelson, Anatoly A.
    Berdnikov, Sergey
    Bowles, Jeffrey H.
    Povazhnyi, Vasiliy
    Saprygin, Vladislav
    Wagner, Ellen J.
    Patterson, Karen W.
    [J]. 2014 6TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS), 2014,
  • [7] Detection and mapping of water and chlorophyll-a spread using Sentinel-2 satellite imagery for water quality assessment of inland water bodies
    Avantika Latwal
    Shaik Rehana
    K. S. Rajan
    [J]. Environmental Monitoring and Assessment, 2023, 195
  • [8] Detection and mapping of water and chlorophyll-a spread using Sentinel-2 satellite imagery for water quality assessment of inland water bodies
    Latwal, Avantika
    Rehana, Shaik
    Rajan, K. S.
    [J]. ENVIRONMENTAL MONITORING AND ASSESSMENT, 2023, 195 (11)
  • [9] Empirical model for chlorophyll-a determination in inland waters from the forthcoming Sentinel-2 and 3. Validation from HICO images
    Delegido, J.
    Tenjo, C.
    Ruiz-Verdu, A.
    Pena, R.
    Moreno, J.
    [J]. REVISTA DE TELEDETECCION, 2014, (41): : 37 - 47
  • [10] Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach
    Pahlevan, Nima
    Smith, Brandon
    Schalles, John
    Binding, Caren
    Cao, Zhigang
    Ma, Ronghua
    Alikas, Krista
    Kangro, Kersti
    Gurlin, Daniela
    Nguyen Ha
    Matsushita, Bunkei
    Moses, Wesley
    Greb, Steven
    Lehmann, Moritz K.
    Ondrusek, Michael
    Oppelt, Natascha
    Stumpf, Richard
    [J]. REMOTE SENSING OF ENVIRONMENT, 2020, 240