Advances in Earth Science ›› 2018, Vol. 33 ›› Issue (5): 483-492. doi: 10.11867/j.issn.1001-8166.2018.05.0483

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Trends on Scaling Research for Land Surface Parameters in Quantitative Remote Sensing

Haijun Luan 1( ), Qingjiu Tian 2, 3, Xinxin Zhang 1, Qin Nie 1, Xiaoling Zhu 1   

  1. 1.College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
    2.International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
    3.Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China
  • Received:2017-12-15 Revised:2018-04-02 Online:2018-05-20 Published:2018-06-13
  • About author:

    First author:Luan Haijun (1984-), male, Luyi County, Henan Province, Lecturer. Research areas include scale effects research of quantitative remote sensing. E-mail:luanhaijun@xmut.edu.cn

  • Supported by:
    Project supported by the National Natural Science Foundation of China “Coupling of NDVI’s up-scaling and down-scaling fusing with ground objects classification”(No.41601350);The Natural Science Foundation of Fujian Province, China “Research on NDVI’s scaling fusing with ground objects classification”(No.2017J05069).

Haijun Luan, Qingjiu Tian, Xinxin Zhang, Qin Nie, Xiaoling Zhu. Trends on Scaling Research for Land Surface Parameters in Quantitative Remote Sensing[J]. Advances in Earth Science, 2018, 33(5): 483-492.

Scale effect is a crucial scientific problem in quantitative remote sensing, and scholars attempt to solve it with scales transformation models. As a significant land surface parameter, NDVI’s scaling has been studied for a long time. Therefore, we took NDVI as a main example. Its development of scaling research was described and analyzed in the paper, and the development trends were discussed for land surface parameters in quantitative remote sensing. Our opinions are as follows: ① It will be the new trend to establish upscaling models fused with ground objects classification information for land surface parameters in quantitative remote sensing; ② It will be the new trend to establish downscaling models based on fractal for land surface parameters in quantitative remote sensing; ③ It is still the hotspot to establish temporal-spatial coupled scaling models for land surface parameters in quantitative remote sensing in the future. The multi-fractal scaling methodology was proposed and its availability was analyzed in the paper, which presented significant potential; ④ It will be the novel trend to combine scales transformation in quantitative remote sensing presented automatic ground objects recognition in remote sensing images. It is proposed that the two research fields can help each other and both can make much progress in the future.

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