干旱区生态环境知识资源中心(Arid): Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI

Arid

DOI	10.1016/j.scitotenv.2019.136092
	Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI
	Wang, Jingzhe 1,2,3; Ding, Jianli1,2 ; Yu, Danlin 4,5; Teng, Dexiong 1,2; He, Bin 6; Chen, Xiangyue 1,2; Ge, Xiangyu 1,2; Zhang, Zipeng 1,2; Wang, Yi 7; Yang, Xiaodong 8; Shi, Tiezhu 3; Su, Fenzhen 9
通讯作者	Ding, Jianli
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
EISSN	1879-1026
出版年	2020
卷号	707
英文摘要	Accurate assessment of soil salinization is considered as one of the most important steps in combating global climate change, especially in arid and semi-arid regions. Multi-spectral remote sensing (RS) data including Landsat series provides the potential for frequent surveys for soil salinization at various scales and resolutions. Additionally, the recently launched Sentinel-2 satellite constellation has temporal revisiting frequency of 5 days, which has been proven to be an ideal approach to assess soil salinity. Yet, studies on detailed comparison in soil salinity tracking between Landsat-8 OLI and Sentinel-2 MSI remain limited. For this purpose, we collected a total of 64 topsoil samples in an arid desert region, the Ebinur Lake Wetland National Nature Reserve (ELWNNR) to compare the monitoring accuracy between Landsat-8 OLI and Sentinel-2 MSI. In this study, the Cubist model was trained using RS-derived covariates (spectral bands, Tasseled Cap transformation-derived wetness (TCW), and satellite salinity indices) and laboratory measured electrical conductivity of 1:5 soil:water extract (EC). The results showed that the measured soil salinity had a significant correlation with surface soil moisture (Pearson's r = 0.75). The introduction of TCW generated satisfactory estimating performance. Compared with OLI dataset, the combination of MSI dataset and Cubist model yielded overall better model performance and accuracy measures (R-2 = 0.912, RMSE = 6.462 dS m(-1), NRMSE = 9.226%, RPD = 3.400 and RPIQ = 6.824, respectively). The differences between Landsat-8 OLI and Sentinel-2 MSI were distinguishable. In conclusion, MSI image with finer spatial resolution performed better than OLI. Combining RS data sets and their derived TCW within a Cubist framework yielded accurate regional salinity map. The increased temporal revisiting frequency and spectral resolution of MSI data are expected to be positive enhancements to the acquisition of high-quality soil salinity information of desert soils. (C) 2019 Elsevier B.V. All rights reserved.
英文关键词	Soil salinization Sentinel-2 MSI Landsat-8 OLI Cubist Remote sensing Surface soil moisture
类型	Article
语种	英语
国家	Peoples R China ; USA
收录类别	SCI-E
WOS记录号	WOS:000507925700088
WOS关键词	ORGANIC-MATTER CONTENT ; EBINUR LAKE ; SPATIAL-DISTRIBUTION ; SPECTRAL INDEXES ; SEMIARID REGION ; WET SEASONS ; REMOTE ; CARBON ; SATELLITE ; DRY
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
来源机构	中国科学院新疆生态与地理研究所 ; 中国科学院地理科学与资源研究所 ; 新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315504
作者单位	1.Xinjiang Univ, Coll Resources & Environm Sci, Higher Educ Inst, Key Lab Smart City & Environm Modelling, Urumqi 800046, Peoples R China; 2.Xinjiang Univ, Key Lab Oasis Ecol, Urumqi 830046, Peoples R China; 3.Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Key Lab Geoenvironm Monitoring Coastal Zone, Minist Nat Resources,Guangdong Key Lab Urban Info, Shenzhen 518060, Peoples R China; 4.Renmin Univ China, Sch Sociol & Populat Studies, Beijing 100872, Peoples R China; 5.Montclair State Univ, Dept Earth & Environm Studies, Montclair, NJ 07043 USA; 6.Guangdong Inst Ecoenvironm Sci Technol, Guangdong Key Lab Integrated Agroenvironm Pollut, Guangzhou 510650, Peoples R China; 7.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China; 8.Ningbo Univ, Dept Geog & Spatial Informat Technol, Ningbo 315211, Peoples R China; 9.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Wang, Jingzhe,Ding, Jianli,Yu, Danlin,et al. Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI[J]. 中国科学院新疆生态与地理研究所, 中国科学院地理科学与资源研究所, 新疆大学,2020,707.
APA	Wang, Jingzhe.,Ding, Jianli.,Yu, Danlin.,Teng, Dexiong.,He, Bin.,...&Su, Fenzhen.(2020).Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI.SCIENCE OF THE TOTAL ENVIRONMENT,707.
MLA	Wang, Jingzhe,et al."Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI".SCIENCE OF THE TOTAL ENVIRONMENT 707(2020).