предыдущий|следующий

Advances in upscaling methods of quantitative remote sensing

  • role： First author第一作者
  • Affiliation：

    Skate Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Bejing 100101, China

    University of Chinese Academy of Sciences, Bejing 100049, China

  • Email：haodl@radi.ac.cn
  • Introduction：郝大磊(1993—　)，男，硕士研究生，研究方向为山区定量遥感。E-mail：haodl@radi.ac.cn
Dalei HAO12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Skate Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Bejing 100101, China

    University of Chinese Academy of Sciences, Bejing 100049, China

  • Email：xiaoqing@radi.ac.cn
  • Introduction：肖青(1971—　)，男，研究员，研究方向为定量遥感反演机理和模型。E-mail：xiaoqing@radi.ac.cn
Qing XIAO12*,  
  • Affiliation：

    Skate Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Bejing 100101, China

    University of Chinese Academy of Sciences, Bejing 100049, China

Jianguang WEN12,  
  • Affiliation：

    Skate Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Bejing 100101, China

Dongqin YOU1,  
  • Affiliation：

    Skate Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Bejing 100101, China

    College of Earth Environmental Sciences, Lanzhou University, Lanzhou 730000, China

Xiaodan WU13,  
  • Affiliation：

    Skate Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Bejing 100101, China

    University of Chinese Academy of Sciences, Bejing 100049, China

Xingwen LIN12,  
  • Affiliation：

    Skate Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Bejing 100101, China

    University of Chinese Academy of Sciences, Bejing 100049, China

Shengbiao WU12

реферат

The scale effect is a common phenomenon in geography that restricts the development of space science, such as remote sensing. Scale issues have elicited increasing attention from scientists due to the development of quantitative remote sensing. Developing a reasonable scaling method to promote the extensive application of remote sensing technology is urgent. In this study, existing upscaling methods in quantitative remote sensing are reviewed from two aspects, namely, pixel-to-pixel and point-to-pixel upscaling. The methods are analyzed and compared in terms of the construction, basic principles, characteristics, limitations, and applicable conditions of the corresponding models. Pixel-to-pixel upscaling methods can be divided into two types, namely, inversion–aggregation and aggregation–inversion, according to the conversion mechanism. Inversion–aggregation methods are classified as mathematics and physics based. Mathematics-based methods consist of classic image process approaches, empirical regression methods, and fractal-based methods. The principle of inversion–aggregation methods is explicit and clear, and the values acquired by such methods are generally considered true values. However, these methods require a pixel-by-pixel inversion process, which leads to low operational efficiency. Aggregation–inversion methods are categorized as input parameter-, model-, and output parameter-based approaches. Pixel-by-pixel retrieval is avoided in such methods. From the perspective of the power determination strategy, point-to-pixel upscaling methods can be classified as simple average, empirical regression, geostatistical, and Bayesian. Simple average methods depend on a reasonable evaluation of spatial heterogeneity and an efficient sampling strategy. Empirical regression methods build the empirical statistical relationship on the basis of a large amount of sample data. Geostatistical methods consider the spatial autocorrelation and spatial distribution characteristics of variables. Bayesian methods integrate high-spatial-resolution remote sensing data and prior knowledge to acquire an optimal estimation of land surface parameters at a low spatial resolution. Different point-to-pixel upscaling methods present different advantages and characteristics. Combining the temporal–spatial distribution characteristics of parameters, prior knowledge, and applicability of upscaling methods is necessary to select reasonable upscaling methods in practical applications. On the basis of this analysis, we summarize the problems in existing scaling research from four aspects, namely, discrete and continuous model, statistical and physical model, universal and targeted model, and use of prior knowledge or not. Several other problems, such as the definition of true value, uncertainty analysis, and scale domain and scale threshold determination, have rarely been discussed in upscaling research and require the attention of scientists. We also provide several possible development directions of upscaling methods in quantitative remote sensing. These directions provide important guidance to scaling theory research and its practical application.

ключеви́че слова́

upscaling;scale effect;scale correction;validation;quantitative remote sensing

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