A hierarchical spatial-temporal fusion model

doi:10.6046/zrzyyg.2020346

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Abstract

The temporal resolution of high spatial resolution remote sensing data can be effectively improved by spatio-temporal fusion of remote sensing data. However, the most widely used spatial and temporal adaptive reflectance fusion model (STARFM) fails to achieve highly accurate prediction effects for areas with abrupt changes at present. Given this, this paper proposed a hierarchical spatial-temporal fusion model (H-STFM). In this model, the target pixels to be predicted are divided into pixels with phenological change and pixels with abrupt changes, which are predicted using linear regression and weighted filtering methods, respectively. Then the prediction results of the two types of pixels are fused using an optimized time weighted function to form the final prediction map. The H-STFM proposed in this paper was qualitatively and quantitatively assessed using two sets of medium-resolution remote sensing images from moderate resolution imaging spectrometer (MODIS) and Landsat satellite. As indicated by the experiment results, H-STFM is significantly superior to STARFM in terms of structural similarity and relative dimensionless global error.

Keywords spatio-temporal fusion hierarchical surface reflectance Landsat MODIS

ZTFLH:

TP753

Corresponding Authors: SUN Genyun E-mail: zhangaizhu789@163.com;genyunsun@163.com

Issue Date: 24 September 2021

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	Aizhu ZHANG
	Wei WANG
	Xiongwei ZHENG
	Yanjuan YAO
	Genyun SUN
	Lei XIN
	Ning WANG
	Guang HU

Cite this article:

Aizhu ZHANG,Wei WANG,Xiongwei ZHENG, et al. A hierarchical spatial-temporal fusion model[J]. Remote Sensing for Natural Resources, 2021, 33(3): 18-26.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020346 OR https://www.gtzyyg.com/EN/Y2021/V33/I3/18

Fig.1 Algorithm flowchart

Fig.2 Superpixel-based change pixel filtering

Fig.3 Images of MODIS surface reflectance and Landsat surface reflectance of data set 1

Fig.4 Images of MODIS surface reflectance and Landsat surface reflectance of data set 2

Fig.5 Data set 1 prediction image

Fig.6 Scatter plot of the observed and predicted values of data set 1

Tab.1 Quantitative evaluation results of data set 1

Fig.7 Data set 2 prediction image

Fig.8 Scatter plot of the observed and predicted values of data set 2

Tab.2 Quantitative evaluation results of data set 2

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