سابِق|تال

Robust reconstruction of missing data in Feng Yun geostationary satellite land surface temperature products

  • role： First author第一作者
  • Affiliation：

    School of Resources and Environment Engineering, Anhui University, Hefei 230601, China

  • Email：rs312lzh@yeah.net
  • Introduction：刘紫涵(1990— )，男，硕士研究生，研究方向为遥感图像处理与应用。E-mail：rs312lzh@yeah.net
LIU Zihan1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Resources and Environment Engineering, Anhui University, Hefei 230601, China

  • Email：wuph@ahu.edu.cn
  • Introduction：吴鹏海(1987—    )，男，讲师，研究方向为遥感信息处理与应用的研究。E-mail：wuph@ahu.edu.cn
WU Penghai1*,  
  • Affiliation：

    School of Resources and Environment Engineering, Anhui University, Hefei 230601, China

WU Yanlan1,  
  • Affiliation：

    School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

SHEN Huanfeng2,  
  • Affiliation：

    State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China

ZENG Chao3

ملخص

Land Surface Temperature (LST) derived from geostationary satellites (GEO-LST) is one of the key parameters in analyzing diurnal climate and environment changes. Compared with polar-orbiting satellite data, the LST of geostationary satellites has become increasingly attractive because of its high temporal resolution. GEO-LST has been widely applied in meteorological, climatological, and hydrological studies. However, the existing GEO-LST products can suffer from cloud cover, cloud contamination, and atmospheric disturbance, resulting in missing data. The objective of this study is to develop a new method for reconstructing Feng Yun geostationary satellite (FY-2F) LSTs based on Diurnal Temperature Cycle (DTC) and robust regression.The hourly LSTs from FY-2F with 5 km spatial resolution are adopted for this study, and different types of missing pixels are simulated and reconstructed based on a new model. This model is composed of polynomial, Fourier, and Gaussian functions (PFG), and a robust regression called Levenberg–Marquardt (LM) algorithm is adopted for solving and optimizing the modelparameters. A performance test is conducted by comparing the PFG model based on the LM algorithm with the other two methods implemented on FY-2F data, namely, a method based on Least Square (LS) algorithm modeling of the previous model (van den Bergh. F VAN2006) and a method based on LS algorithm modeling of the PFG. Moreover, the new method is also tested for real LST products (with missing data).By comparing the reconstructed results of different methods (LM-PFG, LS-PFG, and LS-VAN2006) with the real LST values, the simulated experiment indicates that the LM-PFG method obtains the lowest root mean square compared with the other two methods. The stability analysis of these methods shows that LM-PFG is the least sensitive to missing samples, which validates that LM-PFG is more stable than LS-PFG and LS-VAN2006. Simultaneously, experimental results show that LM-PFG can also reconstruct the missing values with high accuracy and better than the other methods.In this study, an approach for reconstructing GEO-LSTs based on DTC and robust regression is proposed. The method is evaluated using simulated and real FY-2F LST products and can obtain high scores on quantitative evaluation and visual connectivity. Further work can be conducted to expand this method to other potential GEO-LSTs, such as Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager LSTs or Geostationary Operational Environmental Satellite Imager LSTs. Similar to previous studies, the reconstructed LST values of the proposed method are estimated under cloud-free conditions. LSTs under cloudy conditions should be investigated in future studies.

مفهوم

land surface temperature;geostationary satellite;diurnal temperature cycle;robust regression;FY-2F

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