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Research on land surface temperature downscaling method based on diurnal temperature cycle model deviation coefficient calculation

  • role： First author第一作者
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University,Nanjing 211100, China

  • Email：15195892293@163.com
  • Introduction：王爱辉，1996年生，男，硕士研究生，研究方向为地表温度降尺度理论和应用。E-mail：15195892293@163.com
WANG Aihui1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University,Nanjing 211100, China

  • Email：yyb@hhu.edu.cn
  • Introduction：杨英宝，1976年生，女，教授，研究方向为热红外遥感和生态环境遥感。E-mail：yyb@hhu.edu.cn
YANG Yingbao1*,  
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University,Nanjing 211100, China

PAN Xin1,  
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University,Nanjing 211100, China

    School of Geography and Planning, Chizhou University, Chizhou 247100, China

ZHANG Yong12,  
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University,Nanjing 211100, China

HU Xiejunde1

resumen

Land Surface Temperature (LST) is a key parameter in global climate change research. Remote sensing is a practical means of obtaining surface temperature at global and regional scales, However, the existing single sensor cannot provide LST data with high spatial and temporal resolution, which limits the wide application of LST data obtained by remote sensing. The present downscaling methods are difficult to generate seamless LST data with high spatial and temporal resolution, and the downscaling effect is easily affected by the effective time distribution of LST data with high spatial resolution.In this paper, a land surface temperature downscaling method based on Diurnal Temperature Cycle (DTC)model deviation coefficient calculation is proposed.The LST data from FY-4A, MODIS and Landsat 8 are used to generate the seamless LST data of 100 meters per hour under clear skies and cloudy conditions.The proposed method mainly consists of four parts : (1) the seamless FY-4A LST data are obtained by using DTC model and LST reconstruction method considering spatial and temporal characteristics. (2) Establish the DTC model of FY-4A LST data. (3) The MODIS dataset are extended and then combined with the enhanced spatiotemporal adaptive reflectance fusion model (ESTARFM) to generate LST data of 100 meters at multiple moments every day. (4) Calculate the deviation coefficient of DTC model to obtain seamless LST data with a resolution of 100 meters per hour.Compared with the observation data of three stations, the results showed that: (1) the proposed method in this paper had higher accuracy compared with the ESTARFM model and its average RMSE had been reduced 0.63 K. MAE of the three stations was all less than 3 K, RMSE range was 2.01 K to 3.22 K, and the correlation coefficients r were all higher than 0.98. (2) the proposed method to extend LST data with medium spatial resolution was simple and effective. Compared with the MODIS product accuracy at the transit time (RMSE: 1.14 K to 5.53 K), LST data at the extended time all had higher accuracy (RMSE: 0.90 K to 3.57 K). (3)The method in this paper can generate more complete high resolution LST data set in space and time. On the one hand, it was less affected by high resolution images of missing values, based on the reconstruction of only low spatial resolution LST datasets, seamless and high spatial resolution LST datasets can be generated under clear sky and cloudy conditions. On the other hand, the effective time distribution of high spatial resolution images has little influence on the method in this paper, and the reconstruction results have high stability.In this paper, a land surface temperature downscaling method based on DTC model deviation coefficient calculation is proposed.The method was evaluated by observed datum of three stations and real remote sensing images. The results showed that the proposed method has higher accuracy and can obtain seamless high temporal and spatial LST data under clear sky and cloudy conditions. Moreover, the lack of high spatial resolution LST data and the effective time distribution have less impact on the proposed method. Because the proposed method in this paper is based on the DTC model, it is not applicable to the surface temperature downscaling under rainy weather conditions, which should should be investigated in future studies.

palabra clave

downscaling of land surface temperature;high spatial and temporal resolution;space-time fusion;diurnal temperature cycle;FY-4A

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