Establishing the Downscaling Model of NDVI Based on the Iterated Function System

  • Haijun LuanEmail author
  • Meng Zhang
  • Yunya Wan
  • Yuanrong He
  • Qin Nie
  • Xinxin Zhang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 657)


The moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) product plays an important role in eco-environmental monitoring of natural disasters. However, its validation has long been an important scientific problem that has yet to be resolved. In this study, the downscaling model of NDVI was established based on the fractal Iterated Function System (IFS), and the MOD13 Q1 product was then validated based on the model. Xiamen was selected as the core study area, and utilizing the 30 m resolution Landsat 8 operational land imager (OLI) images as the validation data, the validation of MOD13 Q1 was implemented. The results showed the following. (1) The overall quality of the MOD13 Q1 product is good. While in the NDVI range of 0.2 to 0.6, the MOD13Q1 has an overestimation and the difference recognition of the NDVI is low, which should be paid attention to in practical applications; (2) The experiment proved that the fractal IFS was an effective methodology to establish downscaling models for RS land surface parameters such as NDVI. The inherent physical meaning and dynamic process expression advantages of this method make it have great application potential, which needs further digging.


NDVI Downscaling Iterated Function System MOD13 Q1 OLI 



This work was supported by the National Natural Science Foundation of China “Coupling of NDVI’s up-scaling and downscaling 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), and the “Scientific Research Climbing Plan” Project from Xiamen University of Technology “Spatial Distribution Estimation and Dynamic Monitoring of Soil Organic Matter Based on Multi-source and Heterogeneous Data” (No. XPDKT19010).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Haijun Luan
    • 1
    • 2
    Email author
  • Meng Zhang
    • 1
  • Yunya Wan
    • 1
  • Yuanrong He
    • 1
    • 2
  • Qin Nie
    • 1
  • Xinxin Zhang
    • 1
    • 2
  1. 1.College of Computer and Information EngineeringXiamen University of TechnologyXiamenChina
  2. 2.Digital Fujian Institute of Big Data for Natural Hazards Monitoring, Xiamen University of TechnologyXiamenChina

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