ISPRS-Annals ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Annals ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9050 Copernicus Publications Göttingen, Germany 10.5194/isprs-annals-V-3-2022-209-2022 RESEARCH ON NDVI NORMALIZATION METHOD BASED ON GF IMAGES Tao Y. 1 Huang W. 1 Gan W. 2 Shen H. 1 School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei, 430079, China School of Civil Engineering and Architecture, Wuhan Institute of Technology, China 17 05 2022 V-3-2022 209 215 Copyright: © 2022 Y. Tao et al. 2022 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-annals.copernicus.org/articles/V-3-2022/209/2022/isprs-annals-V-3-2022-209-2022.html The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/V-3-2022/209/2022/isprs-annals-V-3-2022-209-2022.pdf

The existing NDVI products have problems in terms of low spatial resolution and inconsistent values at a large geographical scale. Based on medium and high-resolution multi-source remote sensing data (GF-1 and GF-2 data), this paper normalized NDVI by combining absolute radiation normalization with relative radiation normalization. And the existing relative radiation normalization method, single-scene global linear normalization (SGloLM) method, is improved to adapt to the production of large-range high-resolution NDVI products. Aiming at the problem of obvious mosaic seams when the SGloLM method is applied to multi-scene images, it is mainly improved from two aspects. One is to improve the coefficient solution of the SGloLM algorithm and propose a new method considering the surrounding multi-scene data, the multi-scene global linear model (MGloLM). The other is to incorporate the Maximum Value Composite (MVC) method to synthesize the maximum value of NDVI at different times in a season, to represent the optimal situation of vegetation growth in the current season. In this study, combined experiments of different methods were performed, as well as qualitative and quantitative evaluations. The experimental results show that SGloLM+MVC and the MGloLM+MVC methods can better eliminate the mosaic seams, and their histogram is most similar to the histogram of standard data, and all quantitative evaluation indexes of SGloLM+MVC are optimal (CC=0.7804, MAD=0.0643, RMSE=0.1012).