An improved analytical algorithm for remote estimation of chlorophyll-a in highly turbid waters

被引:19
作者
Li, Linhai [1 ]
Li, Lin [1 ]
Song, Kaishan [1 ,2 ]
Li, Yunmei [3 ]
Shi, Kun [1 ,3 ]
Li, Zuchuan [1 ]
机构
[1] Indiana Univ Purdue Univ, Dept Earth Sci, Indianapolis, IN 46202 USA
[2] Chinese Acad Sci, NE Inst Geog & Agr Ecol, Changchun 130012, Jilin, Peoples R China
[3] Nanjing Normal Univ, Coll Geog Sci, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210046, Peoples R China
来源
ENVIRONMENTAL RESEARCH LETTERS | 2011年 / 6卷 / 03期
关键词
chlorophyll-a; remote sensing; turbid waters; MERIS; inherent optical properties; INLAND; COASTAL; CYANOBACTERIA; RETRIEVAL; QUALITY; BLOOMS; MODEL;
D O I
10.1088/1748-9326/6/3/034037
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
An analytical three-band algorithm for spectrally estimating chlorophyll-a (Chl-a) has been proposed recently and the model does not need to be trained. However, the model did not consider the effects of the absorption due to colored detritus matter (CDM) and backscattering of the water column, resulting in an overestimation when Chl-a < 50 mg m(-3) and an underestimation when Chl-a >= 50 mg m(-3). In this letter, an improved three-band algorithm is proposed by integrating both backscattering and CDM absorption coefficients into the model. The results demonstrate that the improved three-band model resulted in more accurate estimation of Chl-a than the previously used three-band model when they were applied to water samples collected from five highly turbid water bodies with Chl-a ranging from 2.54 to 285.8 mg m(-3). The best results, after model modification, were observed in three Indiana reservoirs with R-2 = 0.905 and relative root mean square error of 20.7%, respectively.
引用
收藏
页数:7
相关论文
共 50 条
  • [31] Remote sensing method and field data to measure chlorophyll-a in surface waters of Chabahar Bay, Iran
    Khaleghi, Matin
    RESEARCH IN MARINE SCIENCES, 2020, 5 (02): : 709 - 717
  • [32] Optimizing Semi-Analytical Algorithms for Estimating Chlorophyll-a and Phycocyanin Concentrations in Inland Waters in Korea
    Pyo, JongCheol
    Pachepsky, Yakov
    Baek, Sang-Soo
    Kwon, YongSeong
    Kim, MinJeong
    Lee, Hyuk
    Park, Sanghyun
    Cha, YoonKyung
    Ha, Rim
    Nam, Gibeom
    Park, Yongeun
    Cho, Kyung Hwa
    REMOTE SENSING, 2017, 9 (06)
  • [33] Remote sensing of chlorophyll-a in coastal waters based on the light absorption coefficient of phytoplankton
    Zheng, Guangming
    DiGiacomo, Paul M.
    REMOTE SENSING OF ENVIRONMENT, 2017, 201 : 331 - 341
  • [34] Remote sensing estimation of chlorophyll-a concentration in Taihu Lake considering spatial and temporal variations
    Cheng, Chunmei
    Wei, Yuchun
    Lv, Guonian
    Xu, Ning
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2019, 191 (02)
  • [35] Removal of Chlorophyll-a Spectral Interference for Improved Phycocyanin Estimation from Remote Sensing Reflectance
    Ogashawara, Igor
    Li, Lin
    REMOTE SENSING, 2019, 11 (15)
  • [36] Using a Remote-Sensing-Based Piecewise Retrieval Algorithm to Map Chlorophyll-a Concentration in a Highland River System
    Ma, Yuanxu
    Sun, Dongqi
    Liu, Weihua
    You, Yongfa
    Wang, Siyuan
    Sun, Zhongchang
    Wang, Shaohua
    REMOTE SENSING, 2022, 14 (23)
  • [37] Hybrid Semi-Analytical Algorithm for Estimating Chlorophyll-A Concentration in Lower Amazon Floodplain Waters
    Flores, Rogerio
    Faria Barbosa, Claudio Clemente
    Maciel, Daniel Andrade
    Leao de Moraes Novo, Evlyn Marcia
    Martins, Vitor Souza
    Lobo, Felipe de Lucia
    Sander de Carvalho, Lino Augusto
    Carlos, Felipe Menino
    FRONTIERS IN REMOTE SENSING, 2022, 3
  • [38] MOD2SEA: A Coupled Atmosphere-Hydro-Optical Model for the Retrieval of Chlorophyll-a from Remote Sensing Observations in Complex Turbid Waters
    Arabi, Behnaz
    Salama, Mhd. Suhyb
    Wernand, Marcel Robert
    Verhoef, Wouter
    REMOTE SENSING, 2016, 8 (09)
  • [39] Remote Sensing of Secchi Depth in Highly Turbid Lake Waters and Its Application with MERIS Data
    Liu, Xiaohan
    Lee, Zhongping
    Zhang, Yunlin
    Lin, Junfang
    Shi, Kun
    Zhou, Yongqiang
    Qin, Boqiang
    Sun, Zhaohua
    REMOTE SENSING, 2019, 11 (19)
  • [40] An improved atmospheric correction algorithm for applying MERIS data to very turbid inland waters
    Jaelani, Lalu Muhamad
    Matsushita, Bunkei
    Yang, Wei
    Fukushima, Takehiko
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2015, 39 : 128 - 141