遥感算法
  • 辐射传输和大气校正
  • 水光学和遥感算法

   

     

1、辐射传输和大气校正

        辐射传输模型和大气校正算法是卫星探测海洋碳参数的前端核心技术。对于受陆源影响显著的近海或内陆水体,高精度的辐射传输模拟和大气校正仍然是国际难题。同时,随着从早到晚连续观测的静止轨道水色卫星观测技术的发展,晨昏大太阳天顶角下的辐射传输模型和大气校正算法已成为瓶颈问题。

        He et al. (2010)建立了综合考虑海气耦合、粗糙海面和偏振的海洋-大气耦合矢量辐射传输模型PCOART。PCOART采用矩阵算法(或累加倍加法)实现偏振辐射传输方程的数值解;采用Cox-Munk模型实现风生粗糙海面;采用“无限薄海-气界面层叠加技术”解决海-气耦合。PCOART不仅可模拟总辐亮度,且可模拟辐射偏振信息。在平面平行分层的PCOART模型基础上,He et al. (2018)研制出了考虑地球曲率的海-气耦合矢量辐射传输模型PCOART-SA,并首次生成了考虑地球曲率影响的大气瑞利散射、气溶胶散射和大气漫射透过率查找表,构建了考虑地球曲率的大气校正模型,为静止水色卫星晨昏观测资料的处理打下了基础。

        在近海浑浊水体大气校正方面,He et al. (2012)通过分析全球高浑浊河口水体的实测光谱特性,发现由于陆源有机物(黄色物质、有机碎屑)对短波的强吸光作用,蓝紫光波段离水辐亮度较小,可替代近红外波段进行气溶胶散射估算,从而提出了基于紫外波段的高浑浊水体大气校正算法(UV-AC)。与采用近红外作为参考波段的标准算法相比,UV-AC可反演近红外的离水辐亮度,有利于悬浮物浓度反演。模拟及现场数据集验证表明,UV-AC算法有效解决了业务化算法在高浑浊水体的失效难题。


部分代表性文章:

1. He, X., Bai, Y., Wei, J., Ding, J., Shanmugam, P., Wang, D., et al. (2017). Ocean color retrieval from MWI onboard the Tiangong-2 Space Lab: preliminary results. Optics Express, 25(20), 23955-23973.

2. He, X., Bai, Y., Zhu, Q., & Gong, F. (2010). A vector radiative transfer model of coupled ocean–atmosphere system using matrix-operator method for rough sea-surface. Journal Of Quantitative Spectroscopy & Radiative Transfer, 111(10), 1426-1448.

3. He, X., Pan, D., Bai, Y., Mao, Z., Wang, T., & Hao, Z. (2016). A Practical Method for On-Orbit Estimation of Polarization Response of Satellite Ocean Color Sensor. IEEE Transactions on Geoscience & Remote Sensing, 54(4), 1967-1976.

4. He, X., Pan, D., Yan, B., Wang, D., & Hao, Z. (2014). A new simple concept for ocean colour remote sensing using parallel polarisation radiance. Sci Rep, 4(6168), 3748.

5. Liu, J., He, X., Liu, J., Bai, Y., Wang, D., Chen, T., et al. (2017). Polarization-based enhancement of ocean color signal for estimating suspended particulate matter: radiative transfer simulations and laboratory measurements. Optics Express, 25(8), A323.

6. Mao, Z., Pan, D., Hao, Z., Chen, J., Tao, B., & Zhu, Q. (2014). A potentially universal algorithm for estimating aerosol scattering reflectance from satellite remote sensing data. Remote Sensing of Environment, 142(3), 131-140.

7. Mao, Z., Pan, D., He, X., Chen, J., Tao, B., Chen, P., et al. (2016). A Unified Algorithm for the Atmospheric Correction of Satellite Remote Sensing Data over Land and Ocean. Remote Sensing, 8(7), 536.

8. Pan, D. (2004). Study on Marine Application Potentiality of CMODIS/SZ-3. Engineering Sciences,2, 1-5.

9. Pan, D., Xianqiang, H. E., & Mao, T. (2003). Preliminary study on the orbit cross-calibration of CMODIS by SeaWiFS. Progress in Natural Science, 13(10), 745-749.

10. Teng, L. I., Pan, D., Yan, B., Gang, L. I., Xianqiang, H. E., Chen, C. T. A., et al. (2015). Satellite remote sensing of ultraviolet irradiance on the ocean surface. Acta Oceanologica Sinica, 34(6), 101-112.

11. Xianqiang, H. E., Pan, D., Bai, Y., & Gong, F. (2006). A general purpose exact Rayleigh scattering look-up table for ocean color remote sensing. Acta Oceanologica Sinica, 25(1), 48-56.

12. Xianqiang, H. E., Pan, D. L., Yan, B., Zhu, Q. K., & Fang, G. (2007). Vector radiative transfer numerical model of coupled ocean-atmosphere system using matrix-operator method. Science in China, 50(3), 442-452.

13. Xianqiang He*, Knut Stamnes, Yan Bai, Wei Li, Difeng Wang. Effects of Earth curvature on atmospheric correction for ocean color remote sensing. Remote Sensing of Environment, 209,118–133,2018.

14. Xuchen Jin, Delu Pan, Xianqiang He*, Yan Bai, Palanisamy Shanmugam, Fang Gong, Qiankun Zhu. A vector radiative transfer model for sea surface salinity retrieval from space: a non-raining case. International Journal of Remote Sensing, 2018, DOI: 10.1080/01431161.2018.1488283.

15. He, X., Bai, Y., Pan, D., Tang, J., & Wang, D. (2012). Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters. Optics Express, 20(18), 20754-20770.

16. Wei, J. A., Wang, D., Gong, F., He, X., & Bai, Y. (2017). The Influence of Increasing Water Turbidity on Sea Surface Emissivity. IEEE Transactions on Geoscience & Remote Sensing, PP(99), 1-15.


2、水光学和遥感算法

        光在水体中的辐射传输过程(水光学)是海洋水色遥感反演的物理基础。海洋水色遥感指使用近紫外、可见光或者近红外波段电磁信息研究海洋特征的方法。海洋水色遥感反演的基本机理为:水体中的各个重要光学成分(叶绿素浓度、悬浮物浓度和黄色物质等)浓度发生变化时,会引起水体吸收和散射特性的变化,进而导致水体离水辐亮度的改变。通过传感器接收离水辐亮度信号的变化,从中剥离出反映水体光学成分含量的有用信息,如水体中的悬浮物、叶绿素和黄色物质含量等。

        针对目前的水色遥感理论框架均是基于遥感器接收的总辐亮度I,而忽略了偏振信号的不足,He et al. (2014)创新性地提出了平行偏振等效辐亮度(I+Q)概念,将难以直接应用的偏振分量(Q和U)“标量化”(正值),从而提出了一种新的水色偏振遥感理论框架。PCOART模拟及POLDER卫星偏振观测验证表明,与传统基于总辐亮度的水色遥感相比,平行偏振等效辐亮度(PPR)可有效降低太阳耀班影响,且可提高水色信噪比,有利于水色信息提取。

        针对传统被动水色遥感只能在白天进行观测且观测结果只能表征表层水体成分的不足,目前正在大力发展可用于探测水体剖面的激光遥感探测技术。Behrenfeld et al. (2013)使用CALIPSO激光雷达卫星数据估算了全球海表22.5m水深以浅的颗粒有机碳(POC)剖面分布,实现了全球海区22.5m深度处POC储量的遥感估算。在观测数据的基础上,Chen et al.(2015)建立了使用激光荧光雷达信号反演浮游植物种类的反演方法。此外,激光雷达还被用于近岸及内陆胡泊水体组分的遥感反演(Chen et al, 2017)。


部分代表性文章:

1. Bai, Y., Pan, D., Cai, W. J., He, X., Wang, D., Tao, B., & Zhu, Q. (2013). Remote sensing of salinity from satellite‐derived CDOM in the Changjiang River dominated East China Sea. Journal of Geophysical Research Oceans, 118(1), 227-243.

2. Chen, J., He, X., Zhou, B., & Pan, D. (2017). Deriving colored dissolved organic matter absorption coefficient from ocean color with a neural quasi‐analytical algorithm. Journal of Geophysical Research-Oceans, 122(1).

3. Chen, J., Ni, X., Liu, M., Chen, J., Mao, Z., Jin, H., & Pan, D. (2015). Monitoring the occurrence of seasonal low‐oxygen events off the Changjiang Estuary through integration of remote sensing, buoy observations, and modeling. Journal of Geophysical Research Oceans, 119(8), 5311-5322.

4. Chen, P., Pan, D., & Mao, Z. (2015). Application of a laser fluorometer for discriminating phytoplankton species. Optics & Laser Technology, 67(67), 50-56.

5. Chen, P., Pan, D., Wang, T., Mao, Z., & Zhang, Y. (2017). Coastal and inland water monitoring using a portable hyperspectral laser fluorometer. Marine Pollution Bulletin.

6. He, X., Bai, Y., Pan, D., Huang, N., Dong, X., Chen, J., et al. (2013). Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters. Remote Sensing of Environment, 133(12), 225-239.

7. Hu, Z., Pan, D., He, X., & Bai, Y. (2016). Diurnal Variability of Turbidity Fronts Observed by Geostationary Satellite Ocean Color Remote Sensing. Remote Sensing, 8(2), 147.

8. Hu, Z., Pan, D., He, X., Song, D., Huang, N., Bai, Y., et al. (2017). Assessment of the MCC method to estimate sea surface currents in highly turbid coastal waters from GOCI. International Journal of Remote Sensing, 38(2), 572-597.

9. Hu, Z., Wang, D. P., He, X., Li, M., Wei, J., Pan, D., & Bai, Y. (2017). Episodic surface intrusions in the Yellow Sea during relaxation of northerly winds. Journal of Geophysical Research, 122.

10. Hu, Z., Wang, D. P., Pan, D., He, X., Miyazawa, Y., Bai, Y., et al. (2016). Mapping surface tidal currents and Changjiang plume in the East China Sea from Geostationary Ocean Color Imager. Journal of Geophysical Research Oceans, 121(3).

11. Li, H., He, X., Bai, Y., Gong, F., & Zhu, Q. (2017). Assessment of satellite-based chlorophyll-a retrieval algorithms for high solar zenith angle conditions. Journal of Applied Remote Sensing, 11(1), 012004.

12. Mao, Z., Stuart, V., Pan, D., Chen, J., Gong, F., Huang, H., & Zhu, Q. (2010). Effects of phytoplankton species composition on absorption spectra and modeled hyperspectral reflectance. Ecological Informatics, 5(5), 359-366.

13. Peng, C., Pan, D., Mao, Z., & Tao, B. (2015). Detection of water quality parameters in Hangzhou Bay using a portable laser fluorometer. Marine Pollution Bulletin, 93(1-2), 163-171.

14. Tao, B., Mao, Z., Lei, H., Pan, D., Bai, Y., Zhu, Q., & Zhang, Z. (2017). A semianalytical MERIS green‐red band algorithm for identifying phytoplankton bloom types in the East China Sea. Journal of Geophysical Research Oceans, 122(3).

15. Tao, B., Mao, Z., Lei, H., Pan, D., Shen, Y., Bai, Y., et al. (2015). A novel method for discriminating Prorocentrum donghaiense from diatom blooms in the East China Sea using MODIS measurements. Remote Sensing of Environment, 158, 267-280.

16. Tao, B., Pan, D., Mao, Z., Shen, Y., Zhu, Q., & Chen, J. (2013). Optical detection of Prorocentrum donghaiense blooms based on multispectral reflectance. Acta Oceanologica Sinica, 32(10), 48-56.

17. Wang, D., Gong, F., Pan, D., Hao, Z., & Zhu, Q. (2010). Introduction to the airborne marine surveillance platform and its application to water quality monitoring in China. Acta Oceanologica Sinica, 29(2), 33-39.

18. Tao, B., Mao, Z., Pan, D., Shen, Y., Zhu, Q., & Chen, J. (2013). Influence of bio-optical parameter variability on the reflectance peak position in the red band of algal bloom waters. Ecological Informatics, 16(16), 17-24.