**1. Introduction**

The scale problem is one of the important and fundamental problems of quantitative remote sensing [1–3]. Scholars have studied the scale effects of different remote sensing (RS) land surface parameters. The study of scale effect is conducive to the synergistic use of RS data of different spatial and temporal resolutions (scales) to solve the problem that "massive" RS images cannot be fully utilized and has important application potential and scientific research value [3]. In view of the spatiotemporal characteristics of the ground objects, the RS land surface parameters not only have spatial scale effects but also have temporal scale effects. Scholars have conducted extensive and in-depth research on the scale effect of land surface parameters, which includes the mechanism, manifestation, effect analysis, and solution of scale effects. The author has previously discussed it in detail [4]. Based on the above research aspects, scale conversion as a solution to scale effects has received attention. The scale conversion model can characterize the numerical or

physical relationship of RS land surface parameter images at different resolutions (scales) and can quantitatively describe scale effects. This paper will also focus on the research progress of the spatial down-scaling and the spatiotemporal scaling.

vegetation water content) of soil moisture scale conversion and then established the IFS to describe the soil moisture downscaling, and the conversion effect was good. The model can describe the dynamic process of soil moisture scale conversion, which has physical significance and demonstrates the advantages of downscaling surface parameters based on fractal IFS. In general, there is currently little research into the causes of fractal dynamics. In mathematics, the fractal IFS is a continuously iterative calculation based on the whole research object [14], and the RS land surface parameter image is created in units of local pixels. This ensures that the mathematical IFS vertical conversion factor (*r* function) is usually constant [21], while the vertical conversion factor of RS land surface parameters (such as soil moisture) is based on the physical elements of each pixel (such as sandy soil). The amount of space and the vegetation water content varies dynamically and temporally [21]. This is why the IFS function can describe the scale switching dynamics of surface parameters and why the model has certain physical meanings. The vertical conversion factor is used to describe the interscale conversion of surface parameter values and is the key to determining the IFS function. Different surface parameters have different values due to the spatial distribution and scale conversion factors (or dynamic factors), and the vertical conversion factor (*r* function) contains different types of variables and function forms. How to determine the *r* function is the difficulty in determining the IFS function, which is also an important reason why the latter is less frequently applied in descriptions of quantitative RS land surface parameter scale conversion. Therefore, the NDVI downscaling model based on the fractal IFS function can be considered to describe the dynamic process of scale conversion. This research covers a wide area and is of great significance. The

*Establishing the Downscaling and Spatiotemporal Scale Conversion Models of NDVI Based on…*

*DOI: http://dx.doi.org/10.5772/intechopen.91359*

following is a description of a preliminary implementation [22].

The solution incorporating these considerations is described below.

How does one build an NDVI downscaling model based on the fractal IFS function? The following points need to be considered: first, how to identify the sensitive factors affecting the spatial distribution and scale effect of NDVI for NDVI; second, how to use this sensitive factor to establish the vertical scale conversion factor *r* function in the IFS and then determine the IFS function to achieve NDVI downscaling; and finally, how to evaluate the downscaled conversion results.

According to the above description, water body is an important parameter affecting the spatial distribution and scale effect of NDVI; thus it can be determined that the pixel water parameter is one of the important dynamic factors of NDVI scale conversion. In addition, Wen et al. [23] gave a method for albedo conversion from small-scale to large-scale images and used the pixel topographical influencing factors to correct the converted results, which demonstrated that the method was effective for albedo scale conversion of rugged terrain. Considering the close relationship between the surface reflectivity and the surface albedo, and that the surface reflectance is the basic parameter for calculating NDVI, the topographic factor parameter can be determined as one of the important kinetic factors for NDVI scale conversion. Therefore, the important dynamic factors in NDVI spatial distribution

and scale conversion are determined to be the pixel water parameters and

**2.2 Methodology**

*2.2.1 Identify sensitive factors*

topographic factors.

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