干旱区生态环境知识资源中心(Arid): Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks

Arid

DOI	10.1016/j.jag.2022.102969
	Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks
	Ge, Xiangyu; Ding, Jianli; Teng, Dexiong; Xie, Boqiang; Zhang, Xianlong; Wang, Jinjie; Han, Lijing; Bao, Qingling; Wang, Jingzhe
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	112
英文摘要	Soil salinization has hampered the achievement of sustainable development goals (SDGs) in many countries worldwide. Several countries have recently launched hyperspectral remote sensing satellites, opening new av-enues for accurate soil-salinity monitoring. Among them, Gaofen-5 (GF-5) from China has a high comprehensive performance, including a spectral resolution of 5 nm, 330 bands, and signal-to-noise ratio of 700. However, the potential of GF-5 for estimating soil salinity is not well understood. In this study, we proposed a strategy that includes bootstrap methods, fractional order derivative (FOD) techniques and decision-level fusion models to exploit the soil salinity diagnostic information and reduce estimation uncertainty in the Ebinur Lake oasis in northwestern China. The results showed that the GF-5 data were suitable for assessing soil salinity. The FOD technique enhanced the correlation between soil salinity and spectra, identified more diagnostic bands, improved the accuracy of soil salinity estimation, and reduced model uncertainty. The low-order FOD outperformed the high-order FOD. The spectra processed by the 0.9 order derivative were the most correlated with soil salinity (r =-0.76). The model driven by the 0.8 order derivative produced the optimal estimated model (R2 = 0.95, root mean square error (RMSE) = 3.20 dS m-1 and a ratio of performance to interquartile distance (RPIQ) = 5.96). The model driven by the 0.8 order derivative had less uncertainty than the models based on the original and integer-order derivative (first-and second-derivatives) spectra. This study provides a reference for estimating soil salinity from GF-5 data using the proposed framework with low uncertainty and high accuracy. GF-5 data have great potential for assessing environmental problems and facilitating further SDGs.
英文关键词	Gaofen-5 Soil salinization Fractional order derivative Machine learning Digital soil mapping
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:000844161000003
WOS关键词	ORGANIC-MATTER CONTENT ; YELLOW-RIVER DELTA ; UNCERTAINTY ; PREDICTION ; REGION ; WATER ; OLI ; MSI
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/2XILL650/393121
推荐引用方式 GB/T 7714	Ge, Xiangyu,Ding, Jianli,Teng, Dexiong,et al. Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks[J],2022,112.
APA	Ge, Xiangyu.,Ding, Jianli.,Teng, Dexiong.,Xie, Boqiang.,Zhang, Xianlong.,...&Wang, Jingzhe.(2022).Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,112.
MLA	Ge, Xiangyu,et al."Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 112(2022).