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Sensitivity analysis of the training set to the performance of the machine learning-based land surface temperature reconstruction for cloud covered pixels

  • role： First author第一作者
  • Affiliation：

    School of Energy and Power Engineering, Xihua University, Chengdu 610039, China

    Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China

  • Email：m18833028159@163.com
  • Introduction：何坤龙，1995年生，男，硕士研究生，研究方向为遥感水文水资源。E-mail： m18833028159@163.com
HE Kunlong12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China

  • Email：zhaow@imde.ac.cn
  • Introduction：赵伟，1984年生，男，青年研究员，研究方向为山地地表水热过程遥感。E-mail： zhaow@imde.ac.cn
ZHAO Wei2*,  
  • Affiliation：

    School of Energy and Power Engineering, Xihua University, Chengdu 610039, China

LIU Xiaohui1,  
  • Affiliation：

    School of Environment and Resources, Southwest University of Science and Technology, Mianyang 621010, China

LIU Jiao3

реферат

Land Surface Temperature (LST) represents integrated features of land atmosphere physical and dynamic processe, It is a key element in the fields of climate change, the land–atmosphere energy budget, and the global hydrological cycle, vegetation monitoring urban climate and environmental studies. Thermal infrared remote sensing is an important technique for monitoring LST. However, the Moderate Resolution Imaging Spectroradiometer (MODIS) data are severely contaminated by cloud cover, which limits the applications of LST products. In recent years, the development of machine learning algorithms provides a promising technique for the reconstruction of LST under clouds. However, the accuracy of the cloud cover pixel reconstruction method based on machine learning is directly related to the number and regional distribution of training samples. In order to quantitatively evaluate the impact of the number and regional distribution of training samples on the LST reconstruction accuracy, based on MODIS land products and Meteosat Second Generation (MSG) incident short-wave radiation products, the LST reconstruction model depending on random forest method to construct an LST linking model for LSTs and a range of influencing factors were fitted based on clear-sky observations, which was then applied to cloud-covered pixels to obtain an LST reconstruction, the proposed reconstruction model was applied to carry out the influence of different training samples on the reconstruction accuracy of LST. The results show that: (1) A visual comparison with daily LST observations from (MSG) incident short-wave radiation products indicated that the LSTs reconstructed using this method were representative of LST patterns resulting from the influence of key variables including solar radiation intensity, vegetation cover, and geographical factor. (2) The accuracy of LST reconstruction improves significantly with the increase of the amount of training sample data, and the reconstruction accuracy is also different in different seasons. When the amount of training data increases from 5% to 95%, there are seasonal differences between summer and autumn due to the differences in vegetation and solar radiation. The variation range of correlation coefficient and root mean square error in summer is larger than that in autumn. (3) The random sampling method has higher and stabler accuracy than the regional sampling method because of its spatial representativeness, which can reduce the root mean square error to less than 2.1 k and increase the correlation coefficient to more than 0.93. Even if the amount of data is small, the reconstruction accuracy with the random sampling method is relatively stable, the negative effect of the insufficient number of training samples on the reduction of reconstruction accuracy is weakened. (4) The training sets were divided according to different elevations and vegetation coverage ranges to reconstruct the LST, and the results showed that the reconstruction accuracy was better when the range of the training sets included the range of the reconstruction area, that is, when the training set contains enough data features, it has a satisfactory spatial representation. The research results show that the proposed reconstruction model has a strong potential to reconstruct LSTs under cloud-covered conditions, and can also accurately describe the spatial distributions of LST. It also can provide a reference for future machine learning methods to select appropriate training samples and reconstruct the LST with high accuracy.

ключеви́че слова́

land surface temperature;Random Forest;reconstruction;training dataset;elevation;vegetation

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