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

Jingzhe Wang, Jianli Ding, Danlin Yu, Xuankai Ma, Zipeng Zhang, Xiangyu Ge, Dexiong Teng, Xiaohang Li, Jing Liang, Ivan Lizaga, Xiangyue Chen, Lin Yuan, Yahui Guo

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Soil 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 (PLSR) 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 R2 V, 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.

Original languageEnglish
Pages (from-to)172-187
Number of pages16
JournalGeoderma
Volume353
DOIs
StatePublished - 1 Nov 2019

Fingerprint

soil salinity
soil salinization
wet season
dry season
lakes
salinization
China
monitoring
salinity
lake
least squares
soil
soil salts
land degradation
desertification
land restoration
land management
sustainable development
arid lands
arid zones

Keywords

  • Red-edge
  • Remote sensing
  • Sentinel-2
  • Soil salinity
  • Spectral indices

Cite this

Wang, Jingzhe ; Ding, Jianli ; Yu, Danlin ; Ma, Xuankai ; Zhang, Zipeng ; Ge, Xiangyu ; Teng, Dexiong ; Li, Xiaohang ; Liang, Jing ; Lizaga, Ivan ; Chen, Xiangyue ; Yuan, Lin ; Guo, Yahui. / 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. In: Geoderma. 2019 ; Vol. 353. pp. 172-187.
@article{12e35fbb340f4324b1e51dbbb2817728,
title = "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",
abstract = "Soil 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 (PLSR) 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 R2 V, 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.",
keywords = "Red-edge, Remote sensing, Sentinel-2, Soil salinity, Spectral indices",
author = "Jingzhe Wang and Jianli Ding and Danlin Yu and Xuankai Ma and Zipeng Zhang and Xiangyu Ge and Dexiong Teng and Xiaohang Li and Jing Liang and Ivan Lizaga and Xiangyue Chen and Lin Yuan and Yahui Guo",
year = "2019",
month = "11",
day = "1",
doi = "10.1016/j.geoderma.2019.06.040",
language = "English",
volume = "353",
pages = "172--187",
journal = "Geoderma",
issn = "0016-7061",
publisher = "Elsevier",

}

Wang, J, Ding, J, Yu, D, Ma, X, Zhang, Z, Ge, X, Teng, D, Li, X, Liang, J, Lizaga, I, Chen, X, Yuan, L & Guo, Y 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, vol. 353, pp. 172-187. https://doi.org/10.1016/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, Jingzhe; Ding, Jianli; Yu, Danlin; Ma, Xuankai; Zhang, Zipeng; Ge, Xiangyu; Teng, Dexiong; Li, Xiaohang; Liang, Jing; Lizaga, Ivan; Chen, Xiangyue; Yuan, Lin; Guo, Yahui.

In: Geoderma, Vol. 353, 01.11.2019, p. 172-187.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - 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

AU - Wang, Jingzhe

AU - Ding, Jianli

AU - Yu, Danlin

AU - Ma, Xuankai

AU - Zhang, Zipeng

AU - Ge, Xiangyu

AU - Teng, Dexiong

AU - Li, Xiaohang

AU - Liang, Jing

AU - Lizaga, Ivan

AU - Chen, Xiangyue

AU - Yuan, Lin

AU - Guo, Yahui

PY - 2019/11/1

Y1 - 2019/11/1

N2 - Soil 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 (PLSR) 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 R2 V, 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.

AB - Soil 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 (PLSR) 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 R2 V, 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.

KW - Red-edge

KW - Remote sensing

KW - Sentinel-2

KW - Soil salinity

KW - Spectral indices

UR - http://www.scopus.com/inward/record.url?scp=85068520510&partnerID=8YFLogxK

U2 - 10.1016/j.geoderma.2019.06.040

DO - 10.1016/j.geoderma.2019.06.040

M3 - Article

VL - 353

SP - 172

EP - 187

JO - Geoderma

JF - Geoderma

SN - 0016-7061

ER -