An Improved Spatiotemporal Data Fusion Method Using Surface Heterogeneity Information Based on ESTARFM

Xiangnan Liu,Xinyu Zou,Hejie Wei,Xiaobin Dong,Mengxue Liu,Ling Wu,Bingyu Zhao

doi:10.3390/rs12213673

Abstract

The use of the spatiotemporal data fusion method as an effective data interpolation method has received extensive attention in remote sensing (RS) academia. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) is one of the most famous spatiotemporal data fusion methods, as it is widely used to generate synthetic data. However, the ESTARFM algorithm uses moving windows with a fixed size to get the information around the central pixel, which hampers the efficiency and precision of spatiotemporal data fusion. In this paper, a modified ESTARFM data fusion algorithm that integrated the surface spatial information via a statistical method was developed. In the modified algorithm, the local variance of pixels around the central one was used as an index to adaptively determine the window size. Satellite images from two regions were acquired by employing the ESTARFM and modified algorithm. Results showed that the images predicted using the modified algorithm obtained more details than ESTARFM, as the frequency of pixels with the absolute difference of mean value of six bands’ reflectance between true observed image and predicted between 0 and 0.04 were 78% by ESTARFM and 85% by modified algorithm, respectively. In addition, the efficiency of the modified algorithm improved and the verification test showed the robustness of the modified algorithm. These promising results demonstrated the superiority of the modified algorithm to provide synthetic images compared with ESTARFM. Our research enriches the spatiotemporal data fusion method, and the automatic selection of moving window strategy lays the foundation of automatic processing of spatiotemporal data fusion on a large scale.

Highlights

Remote sensing has become a universal technology to monitor dynamic changes of resources and the environment [1]
In order to obtain the prediction accuracy of the spatiotemporal data fusion algorithm based on surface spatial features, this study compared the result obtained by the modified algorithm, as well as the results obtained by ESTARFM and real images
In order to automatically attain moving window size in a spatiotemporal data fusion algorithm, we introduced surface spatial information in a spatiotemporal data fusion method to improve ESTARFM prediction accuracy and efficiency

Summary

Introduction

Remote sensing has become a universal technology to monitor dynamic changes of resources and the environment [1]. Spatiotemporal data fusion methods have received extensive attention [2] in remote sensing studies because of its capability to generate images with high spatiotemporal resolution images from frequent coarse resolution images and sparse fine resolution images [3]. It is a flexible, inexpensive and effective solution to cover the data shortage problem in some situations. Spatiotemporal data fusion methods have been applied to generate synthetic remote sensing imagery from multiple sources with different spatial, temporal, and spectral characteristics, and the fused results can convey more abundant and accurate information than individual sensors alone [4]. Spatiotemporal data fusion methods can provide data foundation for remote sensing field such as environmental dynamic monitoring [5,6], changes of land cover [7], and land surface temperature [4,8]

Methods

Results

Discussion

Conclusion