A Hybrid Spatiotemporal Fusion Method for High Spatial Resolution Imagery: Fusion of Gaofen-1 and Sentinel-2 over Agricultural Landscapes

Author:

Liu Shuaijun1ORCID,Liu Jia2,Tan Xiaoyue3,Chen Xuehong1,Chen Jin1ORCID

Affiliation:

1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.

2. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100875, China.

3. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China.

Abstract

Agricultural applications of remote sensing data typically require high spatial resolution and frequent observations. The increasing availability of high spatial resolution imagery meets the spatial resolution requirement well. However, the long revisit period and frequent cloud contamination severely compromise their ability to monitor crop growth, which is characterized by high temporal heterogeneity. Many spatiotemporal fusion methods have been developed to produce synthetic images with high spatial and temporal resolutions. However, these existing methods focus on fusing low and medium spatial resolution satellite data in terms of model development and validation. When it comes to fusing medium and high spatial resolution images, the applicability remains unknown and may face various challenges. To address this issue, we propose a novel spatiotemporal fusion method, the dual-stream spatiotemporal decoupling fusion architecture model, to fully realize the prediction of high spatial resolution images. Compared with other fusion methods, the model has distinct advantages: (a) It maintains high fusion accuracy and good spatial detail by combining deep-learning-based super-resolution method and partial least squares regression model through edge and color-based weighting loss function; and (b) it demonstrates improved transferability over time by introducing image gradient maps and partial least squares regression model. We tested the StarFusion model at 3 experimental sites and compared it with 4 traditional methods: STARFM (spatial and temporal adaptive reflectance fusion), FSDAF (flexible spatiotemporal data fusion), Fit-FC (regression model fitting, spatial filtering, and residual compensation), FIRST (fusion incorporating spectral autocorrelation), and a deep learning base method—super-resolution generative adversarial network. In addition, we also investigated the possibility of our method to use multiple pairs of coarse and fine images in the training process. The results show that multiple pairs of images provide better overall performance but both of them are better than other comparison methods. Considering the difficulty in obtaining multiple cloud-free image pairs in practice, our method is recommended to provide high-quality Gaofen-1 data with improved temporal resolution in most cases since the performance degradation of single pair is not significant.

Funder

High-resolution Earth observation system

Publisher

American Association for the Advancement of Science (AAAS)

Reference60 articles.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3