文献详情
Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI
文献类型：期刊
通讯作者：Ding, JL (reprint author), Xinjiang Univ, Coll Resources & Environm Sci, Higher Educ Inst, Key Lab Smart City & Environm Modelling, Urumqi 800046, Peoples R China.; Ding, JL (reprint author), Xinjiang Univ, Key Lab Oasis Ecol, Urumqi 830046, Peoples R China.
期刊名称：SCIENCE OF THE TOTAL ENVIRONMENT影响因子和分区
年：2020
卷：707
ISSN：0048-9697
关键词：Soil salinization; Sentinel-2 MSI; Landsat-8 OLI; Cubist; Remote sensing; Surface soil moisture
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 a ...More
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. ...Hide

DOI：10.1016/j.scitotenv.2019.136092
百度学术：Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI
语言：外文
基金：National Natural Science Foundation of ChinaNational Natural Science Foundation of China [41890854, 41961059, 41771470, 41871031, 31860111]
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