Empirical relationship between radar coherence and NDVI

doi:10.13474/j.cnki.11-2246.2021.0109

Abstract

Abstract: The degree of vegetation coverage is an important factor that causes the loss of coherence between radar images. Generally, in areas with severe vegetation coverage such as forests, coherence is relatively low, while in areas with low vegetation coverage such as cities, coherence is relatively high. We establish linear regression and power function regression models based on MODIS normalized vegetation index (NDVI) and InSAR in the Pearl River Delta region in 2017. We use these two models to predict the coherence in this region in 2016 and use F distribution to test the accuracy of the models. The results show that the accuracy of the power function regression model is higher than the linear function in both fitting and prediction. Therefore, we suggest the power function model as an optimal empirical model of coherence and NDVI. Our results show that the power model can well predict the coherence not only in the Pearl River Delta in 2016, but also can predict that in high precision in Yunnan. Therefore, we suggest the empirical model might be widely used in other regions.

Key words: normalized vegetation index, coherence, InSAR, power function, linear function

CLC Number:

P237

LIU Zhiyong, ZHANG Chen, LIU Zekai, YUAN Junjian, QI Hongchang, PAN Yifeng, ZHU Huanlian, WU Xiwen, WANG Hua. Empirical relationship between radar coherence and NDVI[J]. Bulletin of Surveying and Mapping, 2021, 0(4): 45-51,59.

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