Volume 14 Issue 3
Jun.  2025
Turn off MathJax
Article Contents
YANG Fan, ZHANG Hongwei, LI Ziwang, et al. Inversion and validation of ocean surface bio-optical parameters using multiplatform ocean LiDAR[J]. Journal of Radars, 2025, 14(3): 562–575. doi: 10.12000/JR25064
Citation: YANG Fan, ZHANG Hongwei, LI Ziwang, et al. Inversion and validation of ocean surface bio-optical parameters using multiplatform ocean LiDAR[J]. Journal of Radars, 2025, 14(3): 562–575. doi: 10.12000/JR25064

Inversion and Validation of Ocean Surface Bio-optical Parameters Using Multiplatform Ocean LiDAR

DOI: 10.12000/JR25064 CSTR: 32380.14.JR25064
Funds:  The National Natural Science Foundation of China (U2106210, 42106182), National Key Research and Development Program (2022YFB3901705, 2022YFB3901702), Laoshan Laboratory Science and Technology Innovation Projects (LSKJ202201405, LSKJ202201202)
More Information
  • Corresponding author: WU Songhua, wush@ouc.edu.cn
  • Received Date: 2025-04-03
  • Rev Recd Date: 2025-05-20
  • Available Online: 2025-05-22
  • Publish Date: 2025-05-29
  • The vertical characteristics of biological optical parameters in the upper ocean are essential for evaluating marine primary productivity and the carbon cycle. Although ocean lidar can effectively detect these parameters, the inversion results are usually highly biased due to the regional differences in the adaptability of empirical models. This study uses multiplatform LiDAR observations collected in a certain sea area of China (2023–2024), combined with a region-adaptive bio-optical model, to achieve high-precision profiling of bio-optical parameters in the region. The derived vertical profiles of chlorophyll-a concentration showed strong agreement with in-situ measurements, with a coefficient of determination (R2) of 0.84 and an average root mean square error of 0.14 μg·L–1. Further quantitative analysis using an error transfer model revealed that differences in band-specific optical sensitivity considerably affected error distribution. The effective detection depth in the blue band was 70 m, notably higher than the 58 m depth in the green band. In addition, at the subsurface chlorophyll maximum layer, the inversion bias in the blue band was 0.18 μg·L–1 lower than that in the green band, highlighting the intrinsic relationship between the optical characteristics of each wavelength and its associated bias. This result provides an effective method for improving the reliability of profile inversion of bio-optical parameters in complex waters and performing error analysis.

     

  • loading
  • [1]
    BEHRENFELD M J and BOSS E. Beam attenuation and chlorophyll concentration as alternative optical indices of phytoplankton biomass[J]. Journal of Marine Research, 2006, 64(3): 431–451. doi: 10.1357/002224006778189563.
    [2]
    BREWIN R J W, DALL’OLMO G, PARDO S, et al. Underway spectrophotometry along the Atlantic Meridional Transect reveals high performance in satellite chlorophyll retrievals[J]. Remote Sensing of Environment, 2016, 183: 82–97. doi: 10.1016/j.rse.2016.05.005.
    [3]
    LE Chengfeng, ZHOU Xueying, HU Chuanmin, et al. A color-index-based empirical algorithm for determining particulate organic carbon concentration in the ocean from satellite observations[J]. Journal of Geophysical Research: Oceans, 2018, 123(10): 7407–7419. doi: 10.1029/2018JC014014.
    [4]
    DICKEY T, LEWIS M, and CHANG G. Optical oceanography: Recent advances and future directions using global remote sensing and in situ observations[J]. Reviews of Geophysics, 2006, 44(1): RG1001. doi: 10.1029/2003RG000148.
    [5]
    VASILKOV A P, GOLDIN Y A, GUREEV B A, et al. Airborne polarized lidar detection of scattering layers in the ocean[J]. Applied Optics, 2001, 40(24): 4353–4364. doi: 10.1364/AO.40.004353.
    [6]
    COLLISTER B L, ZIMMERMAN R C, SUKENIK C I, et al. Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar[J]. Remote Sensing of Environment, 2018, 215: 85–96. doi: 10.1016/j.rse.2018.05.032.
    [7]
    CHEN Peng and PAN Delu. Ocean optical profiling in South China Sea using airborne LiDAR[J]. Remote Sensing, 2019, 11(15): 1826. doi: 10.3390/rs11151826.
    [8]
    LIU Qi, WU Songhua, LIU Bingyi, et al. Shipborne variable-FOV, dual-wavelength, polarized ocean lidar: Design and measurements in the Western Pacific[J]. Optics Express, 2022, 30(6): 8927–8948. doi: 10.1364/OE.449554.
    [9]
    YUAN Dapeng, MAO Zhihua, CHEN Peng, et al. Remote sensing of seawater optical properties and the subsurface phytoplankton layer in coastal waters using an airborne multiwavelength polarimetric ocean lidar[J]. Optics Express, 2022, 30(16): 29564–29583. doi: 10.1364/OE.463146.
    [10]
    ZHANG Kai, CHEN Yatong, ZHAO Hongkai, et al. Comprehensive, continuous, and vertical measurements of seawater constituents with triple-field-of-view high-spectral-resolution lidar[J]. Research, 2023, 6: 0201. doi: 10.34133/research.0201.
    [11]
    SHANGGUAN Mingjia, LIAO Zhuoyang, GUO Yirui, et al. Sensing the profile of particulate beam attenuation coefficient through a single-photon oceanic Raman lidar[J]. Optics Express, 2023, 31(16): 25398–25414. doi: 10.1364/OE.493660.
    [12]
    SHANGGUAN Mingjia, GUO Yirui, and LIAO Zhuoyang. Shipborne single-photon fluorescence oceanic lidar: Instrumentation and inversion[J]. Optics Express, 2024, 32(6): 10204–10218. doi: 10.1364/OE.515477.
    [13]
    ZHAO Hongkai, ZHOU Yudi, GU Qiuling, et al. Lidar-observed diel vertical variations of inland chlorophyll a concentration[J]. Remote Sensing, 2024, 16(19): 3579. doi: 10.3390/rs16193579.
    [14]
    SANG Xuan, MAO Zhihua, LI Youzhi, et al. Observations of optical properties and chlorophyll-a concentration in Qiandao Lake using shipborne lidar[J]. Remote Sensing, 2024, 16(24): 4663. doi: 10.3390/rs16244663.
    [15]
    LI Xinye, CHEN Peng, ZHANG Zhenhua, et al. Vertical structure observation from spaceborne lidar ICESat-2 in East China Sea[J]. Optics Express, 2025, 33(2): 2847–2865. doi: 10.1364/OE.540111.
    [16]
    ZHU Peizhi, TANG Junwu, SONG Xiaoquan, et al. Future spaceborne oceanographic lidar: Exploring the effects of large off-nadir angles on signal dynamic range and depth aliasing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 5701711. doi: 10.1109/TGRS.2025.3545669.
    [17]
    ZHOU Yudi, CHEN Weibiao, CUI Xiaoyu, et al. Validation of the analytical model of oceanic lidar returns: Comparisons with Monte Carlo simulations and experimental results[J]. Remote Sensing, 2019, 11(16): 1870. doi: 10.3390/rs11161870.
    [18]
    HE Huixin, LIU Qi, TANG Junwu, et al. Validation of the polarized Monte Carlo model of shipborne oceanic lidar returns[J]. Optics Express, 2023, 31(26): 43250–43268. doi: 10.1364/OE.511445.
    [19]
    CHEN Su, CHEN Peng, DING Lei, et al. A new semi-analytical mc model for oceanic LIDAR inelastic signals[J]. Remote Sensing, 2023, 15(3): 684. doi: 10.3390/rs15030684.
    [20]
    LIU Qun, LIU Dong, BAI Jian, et al. Relationship between the effective attenuation coefficient of spaceborne lidar signal and the IOPs of seawater[J]. Optics Express, 2018, 26(23): 30278–30291. doi: 10.1364/OE.26.030278.
    [21]
    LIU Qun, CUI Xiaoyu, CHEN Weibiao, et al. A semianalytic Monte Carlo radiative transfer model for polarized oceanic lidar: Experiment-based comparisons and multiple scattering effects analyses[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 2019, 237: 106638. doi: 10.1016/j.jqsrt.2019.106638.
    [22]
    ZHOU Yudi, CHEN Yang, ZHAO Hongkai, et al. Shipborne oceanic high-spectral-resolution lidar for accurate estimation of seawater depth-resolved optical properties[J]. Light: Science & Applications, 2022, 11(1): 261. doi: 10.1038/s41377-022-00951-0.
    [23]
    CHEN Yatong, CUI Xiaoyu, GU Qiuling, et al. This is MATE: A multiple scAttering correcTion rEtrieval algorithm for accurate lidar profiling of seawater optical properties[J]. Remote Sensing of Environment, 2024, 307: 114166. doi: 10.1016/j.rse.2024.114166.
    [24]
    ZHANG Zhenhua, CHEN Peng, MAO Zhihua, et al. A novel fast multiple-scattering approximate model for oceanographic lidar[J]. Remote Sensing, 2021, 13(18): 3677. doi: 10.3390/rs13183677.
    [25]
    COLLIS R T H and RUSSELL P B. Lidar Measurement of Particles and Gases by Elastic Backscattering and Differential Absorption[M]. HINKLEY E D. Laser Monitoring of the Atmosphere. Berlin, Heidelberg: Springer, 1976: 71–151. doi: 10.1007/3-540-07743-X_18.
    [26]
    KLETT J D. Stable analytical inversion solution for processing lidar returns[J]. Applied Optics, 1981, 20(2): 211–220. doi: 10.1364/AO.20.000211.
    [27]
    FERNALD F G, HERMAN B M, and REAGAN J A. Determination of aerosol height distributions by lidar[J]. Journal of Applied Meteorology, 1972, 11(3): 482–489. doi: 10.1175/1520-0450(1972)011<0482:DOAHDB>2.0.CO;2.
    [28]
    MOREL A and MARITORENA S. Bio-optical properties of oceanic waters: A reappraisal[J]. Journal of Geophysical Research: Oceans, 2001, 106(C4): 7163–7180. doi: 10.1029/2000JC000319.
    [29]
    GORDON H R. Interpretation of airborne oceanic lidar: Effects of multiple scattering[J]. Applied Optics, 1982, 21(16): 2996–3001. doi: 10.1364/AO.21.002996.
    [30]
    WALKER R E and MCLEAN J W. Lidar equations for turbid media with pulse stretching[J]. Applied Optics, 1999, 38(12): 2384–2397. doi: 10.1364/AO.38.002384.
    [31]
    CHEN Peng, PAN Delu, MAO Zhihua, et al. Semi-analytic Monte Carlo radiative transfer model of laser propagation in inhomogeneous sea water within subsurface plankton layer[J]. Optics & Laser Technology, 2019, 111: 1–5. doi: 10.1016/j.optlastec.2018.09.028.
    [32]
    CHEN Peng, JAMET C, MAO Zhihua, et al. OLE: A novel oceanic lidar emulator[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(11): 9730–9744. doi: 10.1109/TGRS.2020.3035381.
    [33]
    SON Y B, GARDNER W D, MISHONOV A V, et al. Multispectral remote-sensing algorithms for particulate organic carbon (POC): The Gulf of Mexico[J]. Remote Sensing of Environment, 2009, 113(1): 50–61. doi: 10.1016/j.rse.2008.08.011.
    [34]
    TELLINGHUISEN J. Statistical error propagation[J]. The Journal of Physical Chemistry A, 2001, 105(15): 3917–3921. doi: 10.1021/jp003484u.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Article views(308) PDF downloads(64) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint