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Assessment of thermal infrared kernel-driven models over row-planted canopies

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：caobiao@aircas.ac.cn
  • Introduction：曹彪，1988年生，男，副研究员，研究方向为多角度热红外遥感。E-mail：caobiao@aircas.ac.cn
CAO Biao1,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：duym@aircas.ac.an
  • Introduction：杜永明，1978年生，男，副研究员，研究方向为热红外定量遥感。E-mail：duym@aircas.ac.an
DU Yongming1*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

BIAN Zunjian1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

DONG Yadong1,  
  • Affiliation：

    Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, School of Instrument Science and Opto-Electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

ZHAO Feng2,  
  • Affiliation：

    Remote Sensing and Natural Resources Modeling, ERIN Department, Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg, 4422

HU Tian3,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

RAN Xueting4,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

QIN Boxiong1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

LI Hua1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

XIAO Qing1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

LIU Qinhuo1

реферат

The ignoring of the land surface thermal radiation directionality hampered the accuracy improvement of current land surface temperature products. It is urgent to develop a practical method to correct the angle effect for the products. More and more attention has been paid to the thermal infrared kernel-drive model because of its simplicity and accuracy. For natural surface, there are widely used eight kernel-driven models. Their fitting abilities over continuous and discrete canopies is well-known, however, no report discussed their performances over the important row-planted stage. The objective of this study is to assess the fitting abilities of all existing eight thermal infrared kernel-driven models over row-planted canopies based on airborne measured datasets.Two multi-angle directional anisotropy datasets over row-planted vineyards were obtained through the airborne observation in several flights. The experiment sites were located at Château Talbot, Médoc region, 30 km north of Bordeaux, France. All the measurements were combined and corrected from nadir temperature to derive the directional anisotropy at 1° steps for view zenith angle (0°—60°) and view azimuth angle (0°—360°). The multi-angle directional anisotropy values were used as input to estimate the kernel coefficients of two 3-parameter models within the traditional visible and near infrared framework (Ross-Li and LSF-Li), two 3-parameter models within the new thermal infrared framework (Vinnikov and RL), and four 4-parameter models within the new thermal infrared framework (Vinnikov-RL, Vinnikov-Chen, LSF-RL, LSF-Chen). Then, the forward simulated directional anisotropy values of all models were compared taking the airborne measured values as reference.Results show that all eight kernel-driven models cannot simulate the row-structure signatures (i.e., axisymmetric feature). They have an overall large RMSE about 2 K and a low R2 less than 0.7. In addition, the RMSE differences between the models are small. For the east-west canopy, RMSE is between 1.930 K and 2.153 K, R2 is between 0.616 and 0.691. For the south-north canopy, RMSE is between 2.005 K and 2.353 K, R2 is between 0.600 and 0.670. Therefore, developing a new specific kernel for row-structure to improve the fitting ability is demanded in the thermal infrared band currently.

ключеви́че слова́

remote sensing;land surface temperature;thermal radiation directionality;kernel-driven model;row-planted canopy

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