предыдущий|следующий

Temporal normalization research of airborne land surface temperature

  • role： First author第一作者
  • Affiliation：

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

    Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China

  • Email：aqiankunjian@126.com
  • Introduction：朱琳清(1988—　)，男，硕士研究生，研究方向为定量遥感。E-mail：aqiankunjian@126.com
ZHU Linqing12,  
  • role： Corresponding author通信作者
  • Affiliation：

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

    Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China

  • Email：jzhou233@uestc.edu.cn
  • Introduction：周纪(1983—　)，男，副教授，研究方向为定量遥感。E-mail：jzhou233@uestc.edu.cn
ZHOU Ji12*,  
  • Affiliation：

    School of Geography and State Key Laboratory for Remote Sensing Science, Beijing Normal University, Beijing 100875, China

LIU Shaomin3,  
  • Affiliation：

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

    Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China

LI Guoquan12

реферат

Airborne thermal infrared remote sensing can obtain Land Surface Temperature (LST) data at a high spatial resolution. However, the swath width of airborne stripes is commonly limited, and researchers must generally acquire multiple stripes of continuous flights to cover a larger study area. LST changes rapidly over time, and the temperature clearly varies among different stripes. Thus, the temporal normalization of LST is necessary to obtain a large scene mosaicking image of different airborne stripes. However, studies on the temporal normalization of airborne LST are still rare to date. The present study first developed a practical method based on the Diurnal Temperature Cycle (DTC) model to temporally normalize the airborne LSTs in cloud-free skies. We assumed that two factors (i.e., the typical diurnal pattern and instantaneous fluctuation from the mean atmospheric conditions) were involved in detecting LST. We then established an Improved DTC (IDTC) model that analyzes the influences of wind speed and fluctuations that cannot be measured by the DTC model. The temporal normalization of the LST requires two parts. The first aspect is the DTC variables, includingTa,tm,ts, andδT. Each land cover type has its unique DTC pattern because of its physical characteristics. Therefore,in-situ LSTs in a diurnal cycle at each ground site were employed to calculate the DTC variables through the Levenberg-Marquardt method. A look-up table (LUT) for the DTC variables of different land cover types was then constructed. The linear regression technique was subsequently utilized to evaluate the LST instantaneous fluctuation of the second part. The TASI stripes were checked pixel by pixel. The variables for each pixel were then searched from the LUT to normalize each TASI stripe.First, the DTC and IDTC models were evaluated in the temporal normalization of the TASI LST on July 10, 2012 by selecting the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment region as the study area. Results show that the TASI LST normalized by the IDTC model was generally more consistent with the ground measured LST than the DTC model. The accuracy of the IDTC model was approximately 0.3 K to 0.6 K higher than the DTC model. Second, to better demonstrate the performance of the IDTC model in the entire study area, the TASI LST images that had been normalized to the ASTER overpass time (12:12 local time) were up-scaled to 90 m and then compared with the ASTER LST. The data indicate that both the temporal normalization methods (DTC and IDTC models) could decrease the LST differences among different TASI stripes. The TASI LST normalized by the IDTC model was more consistent with the ASTER LST, especially in the Gobi Desert and desert steppe. Third, the temporal normalization of the ground measured LST was also implemented to test the IDTC model. It further demonstrates that the IDTC model was better than the DTC model in the temporal normalization of airborne LST.This study proposes a practical method that temporally normalizes the airborne LST based on the DTC and IDTC models to lower the temperature difference among different stripes. Results indicate that the temporal normalization based on the DTC and IDTC models can effectively obtain a large scene composed of multiple flight lines before mosaicking different stripes. The IDTC model, which involves the influences of wind speed and LST fluctuation, also performs better than the DTC model. Our present research has presented a new perspective on the temporal normalization of satellite LST.

ключеви́че слова́

land surface temperature;temporal normalization;diurnal temperature cycle model;airborne remote sensing

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