Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI

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 5,6,7; Ding, Jianli 5,6; Yu, Danlin 8,9; Teng, Dexiong 5,6; He, Bin 2; Chen, Xiangyue 5,6; Ge, Xiangyu 5,6; Zhang, Zipeng 5,6; Wang, Yi 3; Yang, Xiaodong 4
刊名	SCIENCE OF THE TOTAL ENVIRONMENT
	2020-03-10
卷号	707 页码:11
关键词	Soil salinization Sentinel-2 MSI Landsat-8 OLI Cubist Remote sensing Surface soil moisture
ISSN号	0048-9697
DOI	10.1016/j.scitotenv.2019.136092
通讯作者	Ding, Jianli(watarid@xju.edu.cn)
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.
资助项目	National Natural Science Foundation of China[41890854] ; National Natural Science Foundation of China[41961059] ; National Natural Science Foundation of China[41771470] ; National Natural Science Foundation of China[41871031] ; National Natural Science Foundation of China[31860111]
WOS关键词	ORGANIC-MATTER CONTENT ; EBINUR LAKE ; SPATIAL-DISTRIBUTION ; SPECTRAL INDEXES ; SEMIARID REGION ; WET SEASONS ; REMOTE ; CARBON ; SATELLITE ; DRY
WOS研究方向	Environmental Sciences & Ecology
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000507925700088
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/132330]
专题	中国科学院地理科学与资源研究所
通讯作者	Ding, Jianli
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Guangdong Inst Ecoenvironm Sci Technol, Guangdong Key Lab Integrated Agroenvironm Pollut, Guangzhou 510650, Peoples R China 3.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China 4.Ningbo Univ, Dept Geog & Spatial Informat Technol, Ningbo 315211, Peoples R China 5.Xinjiang Univ, Coll Resources & Environm Sci, Higher Educ Inst, Key Lab Smart City & Environm Modelling, Urumqi 800046, Peoples R China 6.Xinjiang Univ, Key Lab Oasis Ecol, Urumqi 830046, Peoples R China 7.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 8.Renmin Univ China, Sch Sociol & Populat Studies, Beijing 100872, Peoples R China 9.Montclair State Univ, Dept Earth & Environm Studies, Montclair, NJ 07043 USA
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]. SCIENCE OF THE TOTAL ENVIRONMENT,2020,707:11.
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,707,11.
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):11.