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

Jingzhe Wang; Jianli Ding; Danlin Yu; Dexiong Teng; Bin He; Xiangyue Chen; Xiangyu Ge; Zipeng Zhang; Yi Wang; Xiaodong Yang; Tiezhu Shi; Fenzhen Su

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

Sci Total Environ. 2020 Mar 10:707:136092. doi: 10.1016/j.scitotenv.2019.136092. Epub 2019 Dec 13.

Authors

Jingzhe Wang¹, Jianli Ding², Danlin Yu³, Dexiong Teng⁴, Bin He⁵, Xiangyue Chen⁴, Xiangyu Ge⁴, Zipeng Zhang⁴, Yi Wang⁶, Xiaodong Yang⁷, Tiezhu Shi⁸, Fenzhen Su⁹

Affiliations

¹ 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.
² 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. Electronic address: watarid@xju.edu.cn.
³ 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, USA.
⁴ 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.
⁵ Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Guangdong Institute of Eco-environmental Science & Technology, Guangzhou 510650, China.
⁶ State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China.
⁷ Department of Geography & Spatial Information Technology, Ningbo University, Ningbo 315211, China.
⁸ 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.
⁹ State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.

PMID: 31972911
DOI: 10.1016/j.scitotenv.2019.136092

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.

Keywords: Cubist; Landsat-8 OLI; Remote sensing; Sentinel-2 MSI; Soil salinization; Surface soil moisture.