Determining switching threshold for NIR-SWIR combined atmospheric correction algorithm of ocean color remote sensing

被引:32
作者
Liu, Huizeng [1 ,2 ,3 ]
Zhou, Qiming [4 ]
Li, Qingquan [1 ,2 ,3 ]
Hu, Shuibo [1 ,2 ,3 ]
Shi, Tiezhu [1 ,2 ,3 ]
Wu, Guofeng [1 ,2 ,3 ,5 ]
机构
[1] Shenzhen Univ, Key Lab Geoenvironm Monitoring Coastal Zone, Minist Nat Resources, Shenzhen 518060, Peoples R China
[2] Shenzhen Univ, Guangdong Key Lab Urban Informat, Shenzhen 518060, Peoples R China
[3] Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China
[4] Hong Kong Baptist Univ, Dept Geog, Kowloon, Hong Kong, Peoples R China
[5] Shenzhen Univ, Coll Life Sci & Oceanog, Shenzhen 518060, Peoples R China
基金
国家重点研发计划;
关键词
Water quality; Ocean color atmospheric correction; NIR-SWIR; Aerosol; SUSPENDED PARTICULATE MATTER; AEROSOL OPTICAL-THICKNESS; TURBID COASTAL WATERS; POYANG LAKE; MODIS-AQUA; SEAWIFS; MODELS; REFLECTANCE; RETRIEVAL; SENSORS;
D O I
10.1016/j.isprsjprs.2019.04.013
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Accurate atmospheric correction is decisive for ocean color remote sensing applications. Near infrared (NIR)-based algorithm performs well for clear waters; while shortwave infrared (SWIR)-based algorithm can obtain good results for turbid waters, however, it tends to produce noisy patterns for clear waters. A practical strategy is to apply NIR- and SWIR-based algorithm for clear and turbid waters, respectively, which is called NIR-SWIR combined atmospheric correction algorithm. However, the currently applied switching scheme for the NIR-SWIR algorithm undermines the atmospheric correction performance. This study aimed to find an applicable switching scheme for NIR-SWIR algorithm. Four MODIS land bands were used to switch the NIR- and SWIR-based algorithms. A simulated dataset was used to evaluate atmospheric performance of NIR- and SWIR-based algorithm. The switching threshold for each MODIS land band was determined as an R-rs value at which SWIR-based algorithm performed better than NIR-based algorithm. The switching scheme was evaluated using matchups of simultaneous MODIS Aqua images and AERONET-OC data, and then tested with a MODIS Aqua image over the western Pacific Ocean. Results showed that the switching threshold for R-rs(469), R-rs(555), R-rs(645) and R-rs(859) were 0.009, 0.016, 0.009 and 0.0006 sr(-1), respectively; R-rs(645) with a threshold of 0.009 sr(-1) and R-rs(555) with a threshold of 0.016 sr(-1) worked well for NIR-SWIR algorithm, while R-rs(469) and R-rs(859) produced worse performance. Therefore, R-rs(555) > 0.016 sr(-1) or R-rs(645) > 0.009 sr(-1) was recommended as the switching scheme for NIR-SWIR algorithm. Considering contrasted estuarine, coastal and some inland waters, combining NIR- and SWIR-based atmospheric correction algorithm with the proposed switching scheme should be useful for remote sensing monitoring over these waters.
引用
收藏
页码:59 / 73
页数:15
相关论文
共 66 条
  • [1] New aerosol models for the retrieval of aerosol optical thickness and normalized water-leaving radiances from the SeaWiFS and MODIS sensors over coastal regions and open oceans
    Ahmad, Ziauddin
    Franz, Bryan A.
    McClain, Charles R.
    Kwiatkowska, Ewa J.
    Werdell, Jeremy
    Shettle, Eric P.
    Holben, Brent N.
    [J]. APPLIED OPTICS, 2010, 49 (29) : 5545 - 5560
  • [2] Improved atmospheric correction and chlorophyll-a remote sensing models for turbid waters in a dusty environment
    Al Shehhi, Maryam R.
    Gherboudj, Imen
    Zhao, Jun
    Ghedira, Hosni
    [J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2017, 133 : 46 - 60
  • [3] [Anonymous], ATM CORR REM SENS OC
  • [4] Spatially resolving ocean color and sediment dispersion in river plumes, coastal systems, and continental shelf waters
    Aurin, Dirk
    Mannino, Antonio
    Franz, Bryan
    [J]. REMOTE SENSING OF ENVIRONMENT, 2013, 137 : 212 - 225
  • [5] Estimation of near-infrared water-leaving reflectance for satellite ocean color data processing
    Bailey, Sean W.
    Franz, Bryan A.
    Werdell, P. Jeremy
    [J]. OPTICS EXPRESS, 2010, 18 (07): : 7521 - 7527
  • [6] Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations
    Bi, Shun
    Li, Yunmei
    Wang, Qiao
    Lyu, Heng
    Liu, Ge
    Zheng, Zhubin
    Du, Chenggong
    Mu, Meng
    Xu, Jie
    Lei, Shaohua
    Miao, Song
    [J]. REMOTE SENSING, 2018, 10 (07)
  • [7] Estimating suspended sediment concentrations from ocean colour measurements in moderately turbid waters; the impact of variable particle scattering properties
    Binding, CE
    Bowers, DG
    Mitchelson-Jacob, EG
    [J]. REMOTE SENSING OF ENVIRONMENT, 2005, 94 (03) : 373 - 383
  • [8] The Ocean Colour Climate Change Initiative: III. A round-robin comparison on in-water bio-optical algorithms
    Brewin, Robert J. W.
    Sathyendranath, Shubha
    Mueller, Dagmar
    Brockrnann, Carsten
    Deschamps, Pierre-Yves
    Devred, Emmanuel
    Doerffer, Roland
    Fomferra, Norman
    Franz, Bryan
    Grant, Mike
    Groom, Steve
    Horseman, Andrew
    Hu, Chuanmin
    Krasemann, Hajo
    Lee, ZhongPing
    Maritorena, Stephane
    Melin, Frederic
    Peters, Marco
    Platt, Trevor
    Regner, Peter
    Smyth, Tim
    Steinmetz, Francois
    Swinton, John
    Werdell, Jeremy
    White, George N., III
    [J]. REMOTE SENSING OF ENVIRONMENT, 2015, 162 : 271 - 294
  • [9] A three-band semi-analytical model for deriving total suspended sediment concentration from HJ-1A/CCD data in turbid coastal waters
    Chen, Jun
    Cui, Tingwei
    Qiu, Zhongfeng
    Lin, Changsong
    [J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2014, 93 : 1 - 13
  • [10] An Improved SWIR Atmospheric Correction Model: A Cross-Calibration-Based Model
    Chen, Jun
    Cui, Tingwei
    Lin, Changsong
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2014, 52 (07): : 3959 - 3967