globalchange  > 气候变化与战略
DOI: 10.1016/j.scitotenv.2019.136092
Title:
Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI
Author: Wang J.; Ding J.; Yu D.; Teng D.; He B.; Chen X.; Ge X.; Zhang Z.; Wang Y.; Yang X.; Shi T.; Su F.
Source Publication: Science of the Total Environment
ISSN: 489697
Publishing Year: 2020
Volume: 707
Language: 英语
Keyword: Cubist ; Landsat-8 OLI ; Remote sensing ; Sentinel-2 MSI ; Soil salinization ; Surface soil moisture
Scopus Keyword: Climate change ; Landforms ; Machine learning ; Soil moisture ; Cubist ; LANDSAT ; Sentinel-2 MSI ; Soil salinization ; Surface soil moisture ; Remote sensing ; water ; arid region ; comparative study ; desert ; detection method ; Landsat ; machine learning ; remote sensing ; salinity ; salinization ; Sentinel ; soil chemistry ; soil moisture ; spectral resolution ; Article ; China ; controlled study ; correlation analysis ; desert ; electric conductivity ; environmental factor ; environmental monitoring ; extract ; geographic mapping ; image analysis ; intermethod comparison ; machine learning ; mathematical model ; measurement accuracy ; performance ; priority journal ; salinity ; satellite imagery ; soil analysis ; soil moisture ; soil property ; soil salinity ; soil water extract ; spatial analysis ; surface soil ; uncertainty ; China ; Ebinur Lake ; Xinjiang Uygur
English Abstract: 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 (R2 = 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. © 2019 Elsevier B.V.
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被引频次[WOS]:8   [查看WOS记录]     [查看WOS中相关记录]
Document Type: 期刊论文
Identifier: http://119.78.100.158/handle/2HF3EXSE/158732
Appears in Collections:气候变化与战略

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Affiliation: Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi, 800046, China; Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, 830046, China; Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the Ministry of Natural Resources, Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China; School of Sociology and Population Studies, Renmin University of China, Beijing, 100872, China; Department of Earth and Environmental Studies, Montclair State University, Montclair, NJ 07043, United States; Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Guangdong Institute of Eco-environmental Science & Technology, Guangzhou, 510650, China; State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China; Department of Geography & Spatial Information Technology, Ningbo University, Ningbo, 315211, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

Recommended Citation:
Wang J.,Ding J.,Yu D.,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,2020-01-01,707
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