An improved algorithm for retrieving chlorophyll-a from the Yellow River Estuary using MODIS imagery

被引:1
|
作者
Jun Chen
Wenting Quan
机构
[1] China University of Geosciences,School of Ocean Sciences
[2] Qingdao Institute of Marine Geology,The Key Laboratory of Marine Hydrocarbon Resources and Environmental Geology
[3] Shanxi Remote Sensing Information Center for Agriculture,undefined
来源
关键词
OC3M algorithm; IOC3M algorithm; Chlorophyll-a concentration; Yellow River Estuary; MODIS;
D O I
暂无
中图分类号
学科分类号
摘要
In this study, an improved Moderate-Resolution Imaging Spectroradiometer (MODIS) ocean chlorophyll-a (chla) 3 model (IOC3M) algorithm was developed as a substitute for the MODIS global chla concentration estimation algorithm, OC3M, to estimate chla concentrations in waters with high suspended sediment concentrations, such as the Yellow River Estuary, China. The IOC3M algorithm uses \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {{{\left[ {R_{\text{rs}}^{{ - 1}}\left( {{\lambda_{{1}}}} \right) - {k_1}R_{\text{rs}}^{{ - 1}}\left( {{\lambda_{{2}}}} \right)} \right]}} \left/ {{\left[ {R_{\text{rs}}^{{ - 1}}\left( {{\lambda_{{3}}}} \right) - {k_{{2}}}R_{\text{rs}}^{{ - 1}}\left( {{\lambda_{{4}}}} \right)} \right]}} \right.} $$\end{document} to substitute for switching the two-band ratio of max [Rrs (443 nm), Rrs (488 nm)]/Rrs (551 nm) of the OC3M algorithm. In the IOC3M algorithm, the absorption coefficient of chla can be isolated as long as reasonable bands are selected. The performance of IOC3M and OC3M was calibrated and validated using a bio-optical data set composed of spectral upwelling radiance measurements and chla concentrations collected during three independent cruises in the Yellow River Estuary in September of 2009. It was found that the optimal bands of the IOC3M algorithm were λ1 = 443 nm, λ2 = 748 nm, λ3 = 551 nm, and λ4 = 870 nm. By comparison, the IOC3M algorithm produces superior performance to the OC3M algorithm. Using the IOC3M algorithm in estimating chla concentrations from the Yellow River Estuary decreases 1.03 mg/m3 uncertainty from the OC3M algorithm. Additionally, the chla concentration estimated from MODIS data reveals that more than 90 % of the water in the Yellow River Estuary has a chla concentration lower than 5.0 mg/m3. The averaged chla concentration is close to the in situ measurements. Although the case study presented herein is unique, the modeling procedures employed by the IOC3M algorithm can be useful in remote sensing to estimate the chla concentrations of similar aquatic environments.
引用
收藏
页码:2243 / 2255
页数:12
相关论文
共 50 条
  • [31] Spatiotemporal chlorophyll-a dynamics on the Louisiana continental shelf derived from a dual satellite imagery algorithm
    Le, Chengfeng
    Lehrter, John C.
    Hu, Chuanmin
    Murrell, Michael C.
    Qi, Lin
    JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2014, 119 (11) : 7449 - 7462
  • [32] Estimation of chlorophyll-a concentration in the Zhujiang Estuary from SeaWiFS data
    MagnusLarson
    Lennartjnsson
    ActaOceanologicaSinica, 2002, (01) : 55 - 63
  • [33] Chlorophyll-a concentration measure in coastal waters using MERIS and MODIS data
    Matarrese, R
    Chiaradia, MT
    De Pasquale, V
    Pasquariello, G
    IGARSS 2004: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM PROCEEDINGS, VOLS 1-7: SCIENCE FOR SOCIETY: EXPLORING AND MANAGING A CHANGING PLANET, 2004, : 3639 - 3641
  • [34] Spatiotemporal evolution of chlorophyll-a concentration from MODIS data inversion in the middle and lower reaches of the Hanjiang River, China
    Chen, Zhuo
    Dou, Ming
    Xia, Rui
    Li, Guiqiu
    Shen, Lisha
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2022, 29 (25) : 38143 - 38160
  • [35] Identifying the breeding areas of locusts in the Yellow River estuary using Landsat ETM plus imagery
    Liu Qingsheng
    Liu Gaohuan
    Yang Yuzhen
    Liu Peng
    Huang Jianjie
    REMOTE SENSING OF THE ENVIRONMENT: 15TH NATIONAL SYMPOSIUM ON REMOTE SENSING OF CHINA, 2006, 6200
  • [36] 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
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 125
  • [37] Application of a generalized additive model (GAM) for estimating chlorophyll-a concentration from MODIS data in the Bohai and Yellow Seas, China
    Wang, Yueqi
    Liu, Dongyan
    Tang, DanLing
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2017, 38 (03) : 639 - 661
  • [38] Spatiotemporal evolution of chlorophyll-a concentration from MODIS data inversion in the middle and lower reaches of the Hanjiang River, China
    Zhuo Chen
    Ming Dou
    Rui Xia
    Guiqiu Li
    Lisha Shen
    Environmental Science and Pollution Research, 2022, 29 : 38143 - 38160
  • [39] IMPROVED EXTRACTION OF CHLOROPHYLL-A AND CHLOROPHYLL-B FROM ALGAE USING DIMETHYL-SULFOXIDE
    SHOAF, WT
    LIUM, BW
    LIMNOLOGY AND OCEANOGRAPHY, 1976, 21 (06) : 926 - 928
  • [40] Effects of Dual Fronts on the Spatial Pattern of Chlorophyll-a Concentrations in and off the Changjiang River Estuary
    Weiqi Li
    Jianzhong Ge
    Pingxing Ding
    Jianfei Ma
    Patricia M. Glibert
    Dongyan Liu
    Estuaries and Coasts, 2021, 44 : 1408 - 1418