سابِق|تال

Research on remote sensing drought monitoring by considering spatial non-stationary characteristics

  • role： First author第一作者
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：zhuxinran_RS@163.com
  • Introduction：朱欣然，1992年生，女，博士研究生，研究方向为遥感数据处理与应用，遥感植被、干旱研究等。E-mail：zhuxinran_RS@163.com
Xinran ZHU12,  
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Changping HUANG1,  
  • Affiliation：

    Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350108, China

    College of Geography and Environment, Jiangxi Normal University, Nanchang 330000, China

Bo WU34,  
  • Affiliation：

    Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350108, China

Hua SU3,  
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

Wenzhe JIAO12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

  • Email：zhanglf@radi.ac.cn
  • Introduction：张立福，1967年生，男，研究员，研究方向为高光谱遥感。E-mail：zhanglf@radi.ac.cn
Lifu ZHANG1*

ملخص

Remote sensing technology has the unique advantages of real-time, fast and spatio-temporal continuity and wide coverage scale. Under the background of global climate deterioration, drought monitoring methods based on remote sensing can provide more real-time, accurate, and stable drought information and better assist scientific decision making than traditional ground monitoring methods. (Methods) Most of the existing drought monitoring methods based on remote sensing rely on global mathematical models that assume the spatial stability of drought events; hence, an accurate representation of local difference characteristics is difficult to achieve. In the current study, a geographic weighted regression (GWR) model is proposed to optimize the traditional global drought monitoring model by considering the spatial non-stationary characteristics of drought events and synthesizing various remote sensing drought indices. (Results) This study, which was conducted in mainland United States, focused on drought monitoring over a ten-year period (2002–2011). The results indicate that the GWR model can provide the best model parameters for the local estimation of spatial variations. Moreover, the monitoring results are consistent with the standard verification data of the United States Drought Monitor. The highest correlation coefficient R between the GWR model and the measured data is 0.8552. The RMSE is 0.972, which is significantly superior to other remote sensing drought monitoring models. (Conclusion) The GWR model has the advantage of spatial non-stationary detection and can realize local fine detection in drought modeling. Moreover, the GWR model can significantly improve the precision of remote sensing drought monitoring and thus has a good application prospect.

مفهوم

drought monitoring;remote sensing;geographically weighted regression;spatial non-stationarity;local optimizing;CUS

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