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Cloud detection over ocean from PARASOL/POLDER3 satellite data

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Optical Calibration and Characterization, Hefei Institutes of Physical Sciences, Chinese Academy of Sciences, Hefei 230031, China

    University of Science and Technology of China, Hefei 230026, China

  • Email：cztgis@qq.com
  • Introduction：陈震霆，1979年生，男，博士研究生，研究方向为卫星偏振遥感、云偏振特性。E-mail：cztgis@qq.com
Zhenting CHEN12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Optical Calibration and Characterization, Hefei Institutes of Physical Sciences, Chinese Academy of Sciences, Hefei 230031, China

  • Email：xbsun@aiofm.ac.cn
  • Introduction：孙晓兵，1966年生，男，研究员，研究方向为大气偏振遥感、地物偏振特性。E-mail：xbsun@aiofm.ac.cn
Xiaobing SUN1*,  
  • Affiliation：

    Key Laboratory of Optical Calibration and Characterization, Hefei Institutes of Physical Sciences, Chinese Academy of Sciences, Hefei 230031, China

Yanli QIAO1

Resümee

Clouds are important regulators of the ocean–atmosphere coupling system in ocean satellite remote sensing. The results of cloud detection have a significant influence on the retrieval accuracy of cloud microphysical properties over the ocean. Therefore, achieving cloud detection over the ocean and determining methods to improve the processing speed of operational algorithm and the precision of cloud pixel recognition for polarized sensors are urgent concerns. This work proposes an Improved Cloud Detection (ICD) algorithm over the ocean according to operational cloud detection problems in satellite polarized sensor data. A series of continuous processes and tests is used to identify the clear-sky and pixel-by-pixel cloudy area using the data of Polarization and Directionality of Earth’s Reflectances (POLDER3). Such data are loaded by Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a LiDAR (PARASOL) satellite. The pixels are divided into ocean and land parts. Then, the ocean glint pixels are eliminated via the glint angle computing formula and the empirical threshold (see MODIS 40 degrees). Thereafter, the cloudy pixels are identified using the characteristic difference of near-infrared reflectance between cloud and clear-sky regions. Cloudy pixels with low reflectance are also further recognized using a polarized reflectance test according to the polarized sensitive characteristics of cloud particles. Next, the clear-sky pixels are identified by the reflectance ratio test between near- infrared and visible light. Finally, the spatial registration rule is created with multi-angle cloud detecting results, and all pixels are relabeled to cloudy, clear-sky, and undetermined pixels with this rule. The Indian Ocean is used as an example for experimental analysis. The results of improved cloud detection are compared with those of the Buriez method. The detection accuracy is very close to the Buriez algorithm but is more time-efficient. In the case of clear-sky pixels, the recognition rates of ICD and of the Buriez algorithm are approximately 30. For cloudy pixels, the recognition rate of ICD is approximately 47%. The error range of cloudy and undetermined pixels is approximately 1% compared with the Buriez algorithm. Furthermore, the efficiency of our cloud detection algorithm greatly improved. The data processing speed is improved approximately three times without considering the time consumption of external data acquisition. Results show that the algorithm is highly effective in achieving high-precision results of cloud detection. At the same time, the processing time of cloudy pixels is significantly reduced, and the operational speed of cloud-detecting products is considerably improved. This algorithm can provide real-time and accurate products for the inversion of atmosphere and ocean parameters to meet the high-precision and -efficiency requirements of operational processing in satellite ground systems. This algorithm is also dominant in the cloud detection process for Directional Polarimetric Camera (DPC) in the GF-5 satellite which is planned for launch in 2017.

Schlüsselwort

cloud detection;POLDER3 sensor;polarized reflectance;multidirectional spatial fusion;near infrared

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