Arid
DOI10.1016/j.geoderma.2019.06.040
Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China
Wang, Jingzhe1,2; Ding, Jianli1,2; Yu, Danlin3,4; Ma, Xuankai1; Zhang, Zipeng1,2; Ge, Xiangyu1,2; Teng, Dexiong1,2; Li, Xiaohang1,2; Liang, Jing1,2; Lizag, Ivan5; Chen, Xiangyue1,2; Yuan, Lin6; Guo, Yahui7
Corresponding AuthorDing, Jianli ; Yu, Danlin
JournalGEODERMA
ISSN0016-7061
EISSN1872-6259
Year Published2019
Volume353Pages:172-187
Abstract in EnglishSoil salinization is one of the most important causes for land degradation and desertification and is an important threat to land management, farming activities, water quality, and sustainable development in arid and semi-arid areas. Soil salinization is often characterized with significant spatiotemporal dynamics. The salt-affected soil is predominant in the Ebinur Lake region in the Northwestern China. However, detailed local soil salinity information is ambiguous at the best due to limited monitoring techniques. Nowadays, the availability of Multi-Spectral Instrument (MSI) onboard Sentinel-2, offers unprecedented perspectives for the monitoring and mapping of soil salinity. The use of MSI data is an innovative attempt for salinity detection in arid land. We hypothesize that field observations and MSI data and MSI data-derived spectral indices using the partial least square regression (PISA) approach will yield fairly accurate regional salinity map. Based on electrical conductivity of 1:5 soil:water extract (EC) of 72 ground-truth measurements (out of 116 sample sites) and various spectral parameters, such as satellite band reflectance, published satellite salinity indices, red-edge indices, newly constructed two-band indices, and three-band indices from MSI data, we built a few inversion models in an attempt to produce the regional salinity maps. Different algorithms including Pearson correlation coefficient method (PCC), variable importance in projection (VIP), Gray relational analysis (GRA), and random forest (RF) were applied for variable selection. The results suggest that both the newly proposed normalized difference index (NDI) [(B12 - B7) / (B12 + B7)] and three-band index (TBI4) [(B12 - B3) / (B3 - B11)] show a better correlation with validation data and could be applied to estimate the soil salinity in the Ebinur Lake region. The established models were validated using the remaining 44 independent ground-based measurements. The RF-PLSR model performed the best across the five models with R-V(2), RMSEv, and RPD of 0.92, 7.58 dS m(-1), and 2.36, respectively. The result from this model was then used to map the soil salinity over the study area. Our analyses suggest that soil salinization changes quite significantly in different seasons. Specifically, soil salinity in the dry season was higher than in the wet season, mostly in the lake area and nearby shores. We contend that the results from the study will be useful for soil salinization monitoring and land reclamation in arid or semi-arid regions outside the current study area.
Keyword in EnglishSentinel-2 Soil salinity Red-edge Spectral indices Remote sensing
SubtypeArticle
Language英语
CountryPeoples R China ; USA ; Spain
OA TypeGreen Submitted
Indexed BySCI-E
WOS IDWOS:000482513900017
WOS KeywordGREY RELATIONAL ANALYSIS ; ORGANIC-MATTER CONTENT ; NEAR-INFRARED SPECTROSCOPY ; NATURE-RESERVE ELWNNR ; REFLECTANCE SPECTROSCOPY ; REMOTE ESTIMATION ; SPECTRAL INDEXES ; METHODS PLSR ; SALT CONTENT ; VEGETATION
WOS SubjectSoil Science
WOS Research AreaAgriculture
EI Keywords2019-11-01
Document Type期刊论文
Identifierhttp://119.78.100.177/qdio/handle/2XILL650/310607
Affiliation1.Xinjiang Univ, Coll Resources & Environm Sci, Key Lab Smart City & Environm Modelling, Higher Educ Inst, Urumqi 800046, Peoples R China;
2.Xinjiang Univ, Key Lab Oasis Ecol, Urumqi 830046, Peoples R China;
3.Renmin Univ China, Sch Sociol & Populat Studies, Beijing 100872, Peoples R China;
4.Montclair State Univ, Dept Earth & Environm Studies, Montclair, NJ 07043 USA;
5.Estn Expt Aula Dei EEAD CSIC, Dept Soil & Water, Avda Montanana 1005, Zaragoza 50059, Spain;
6.Harbin Inst Technol Shenzhen, Sch Architecture, Shenzhen 518055, Peoples R China;
7.Beijing Normal Univ, Coll Water Sci, Beijing Key Lab Urban Hydrol Cycle & Sponge City, Beijing 100875, Peoples R China
Recommended Citation
GB/T 7714
Wang, Jingzhe,Ding, Jianli,Yu, Danlin,et al. Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China[J],2019,353:172-187.
APA Wang, Jingzhe.,Ding, Jianli.,Yu, Danlin.,Ma, Xuankai.,Zhang, Zipeng.,...&Guo, Yahui.(2019).Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China.GEODERMA,353,172-187.
MLA Wang, Jingzhe,et al."Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China".GEODERMA 353(2019):172-187.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Jingzhe]'s Articles
[Ding, Jianli]'s Articles
[Yu, Danlin]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Jingzhe]'s Articles
[Ding, Jianli]'s Articles
[Yu, Danlin]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Jingzhe]'s Articles
[Ding, Jianli]'s Articles
[Yu, Danlin]'s Articles
Terms of Use
No data!
Social Bookmark/Share

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.