干旱区生态环境知识资源中心(Arid): Using spatiotemporal fusion algorithms to fill in potentially absent satellite images for calculating soil salinity: A feasibility study

Arid

DOI	10.1016/j.jag.2022.102839
	Using spatiotemporal fusion algorithms to fill in potentially absent satellite images for calculating soil salinity: A feasibility study
	Han, Lijing; Ding, Jianli; Ge, Xiangyu; He, Baozhong; Wang, Jinjie; Xie, Boqiang; Zhang, Zipeng
通讯作者	Ding, JL
来源期刊	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
ISSN	1569-8432
EISSN	1872-826X
出版年	2022
卷号	111
英文摘要	Currently, remote sensing technology has unique advantages in monitoring soil salinity information, but it is vulnerable to the quality of imaging, and spatiotemporal fusion algorithms can fill these missing images. The purpose of this study is to evaluate the applicability of four commonly used spatiotemporal fusion algorithms for monitoring soil salinity. The applicability of four spatiotemporal fusion techniques for generating fused images based on Landsat 8 and Sentinel-2 data was examined. To estimate soil salinity in the oasis intersection zone region of Xinjiang, China, random forest regression models were built using measured soil salinity and multiple fused images. The results show that the spatiotemporal fusion algorithm can generate fused images with good accuracy and the constructed random forest regression model can assess soil salinity well. We conclude that using a spatiotemporal fusion technique to generate fused pictures can solve the missing data problem caused by poor imaging quality, and that the fused images are well suited for monitoring soil parameters sensitive to environmental changes, such as soil salinity. One of the key influencing variables on the accuracy of the developed assessment model is the correctness of the fused images. When dealing with incomplete data, this work can serve as a scientific reference for monitoring soil salinity utilizing remote sensing methods.
英文关键词	Spatiotemporal fusion Soil salinity Soil remote sensing monitoring Missing image data
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000813495500001
WOS关键词	REFLECTANCE FUSION ; CLIMATE-CHANGE ; LANDSAT ; RESOLUTION ; SCALE ; DYNAMICS ; MODEL ; LAKE
WOS类目	Remote Sensing
WOS研究方向	Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393117
推荐引用方式 GB/T 7714	Han, Lijing,Ding, Jianli,Ge, Xiangyu,et al. Using spatiotemporal fusion algorithms to fill in potentially absent satellite images for calculating soil salinity: A feasibility study[J],2022,111.
APA	Han, Lijing.,Ding, Jianli.,Ge, Xiangyu.,He, Baozhong.,Wang, Jinjie.,...&Zhang, Zipeng.(2022).Using spatiotemporal fusion algorithms to fill in potentially absent satellite images for calculating soil salinity: A feasibility study.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,111.
MLA	Han, Lijing,et al."Using spatiotemporal fusion algorithms to fill in potentially absent satellite images for calculating soil salinity: A feasibility study".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 111(2022).