干旱区生态环境知识资源中心(Arid): Spatial prediction of soil salinity based on the Google Earth Engine platform with multitemporal synthetic remote sensing images

Arid

DOI	10.1016/j.ecoinf.2023.102111
	Spatial prediction of soil salinity based on the Google Earth Engine platform with multitemporal synthetic remote sensing images
	Ma, Shilong; He, Baozhong; Ge, Xiangyu; Luo, Xuefeng
通讯作者	He, BZ
来源期刊	ECOLOGICAL INFORMATICS
ISSN	1574-9541
EISSN	1878-0512
出版年	2023
卷号	75
英文摘要	Accurately analyzing the spatial distribution of salinity at the regional scale is crucial for regional sustainable development. Currently, there are no reliable approaches for mapping salinity due to the substantial spatial variability of soil salinity. However, remote sensing technology has been successfully used to monitor soil properties in various locations. This research investigates an approach to choose the most appropriate remote sensing time window using a cloud computing platform and synthesizes multitemporal remote sensing images for high spatial resolution digital mapping of salinization at the regional scale. The research site for this experiment is the Werigan-Kuqa Oasis in Xinjiang, China. In the first stage, we screen the Landsat-8 surface reflectance datasets for four-time windows: April-October, May-September, June-August multitemporal, and July single -date images using the Google Earth Engine (GEE) cloud platform. Then, we synthesize images based on mini-mum, maximum, mean, and median values and construct multiple spectral indices. We completed model training and digital mapping in the second stage using a geemap. For the first time, we used geemap to integrate a local scikit-learn machine learning library with GEE to train a machine learning model. Next, we performed salinity prediction and mapping based on the best data set. This study demonstrates that (1) Various environmental variables contribute differently to modeling in the same time window. Additionally, these variables exhibit variation across different time windows. However, certain indices, including clay index (CLEX), near infrared (NIR), difference vegetation index (DVI), carbonate index (CAEX), green ratio vegetation index (GRVI), canopy response salinity (CRSI), and elevation, demonstrate stable contributions across different time windows. (2) Employing GEE to choose the suitable time window for synthesizing remote sensing images has a better pre-diction effect compared to single-date image prediction accuracy. Through experimental comparison, the best time window is May-September. (3) The mean synthesis approach from May to September exhibits the best performance in the model, while the median synthesis approach exhibits stable performance in the other two individual time windows. (4) Combining scikit-learn with GEE using Geemap improves model optimization and enhances the mapping performance. This study broadens the application of GEE in soil mapping and improves salinity prediction using multitemporal synthetic pictures with time frames.
英文关键词	Soil salinization Optimal time window Google Earth Engine Multitemporal synthetic Digital soil mapping
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000998857800001
WOS关键词	VEGETATION INDEX ; ORGANIC-MATTER ; LANDSAT IMAGES ; COVER ; UNCERTAINTY ; PERFORMANCE ; CARBON ; AREA ; RED ; MAP
WOS类目	Ecology
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395990
推荐引用方式 GB/T 7714	Ma, Shilong,He, Baozhong,Ge, Xiangyu,et al. Spatial prediction of soil salinity based on the Google Earth Engine platform with multitemporal synthetic remote sensing images[J],2023,75.
APA	Ma, Shilong,He, Baozhong,Ge, Xiangyu,&Luo, Xuefeng.(2023).Spatial prediction of soil salinity based on the Google Earth Engine platform with multitemporal synthetic remote sensing images.ECOLOGICAL INFORMATICS,75.
MLA	Ma, Shilong,et al."Spatial prediction of soil salinity based on the Google Earth Engine platform with multitemporal synthetic remote sensing images".ECOLOGICAL INFORMATICS 75(2023).