Details
| Original language | English |
|---|---|
| Article number | 106124 |
| Journal | Soil and Tillage Research |
| Volume | 241 |
| Early online date | 27 Apr 2024 |
| Publication status | E-pub ahead of print - 27 Apr 2024 |
Abstract
Soil salinization seriously hinders the development of efficient ecological agriculture in coastal areas. The use of Landsat, Sentinel series and hyperspectral data is an ideal way for assessing soil salinity indicators. However, environmental data (e.g. climate, terrain and parent material) are important factors for estimating such indicators. It is necessary to find the advantages and limitations of a combination of satellite images, hyperspectral data and environmental variables (ENVI) for assessing soil salinity accurately. Various data or their combinations ([I] remote sensing [RS], i.e. bands and salinity indices of Landsat 9 and Sentinel 2; [II] ENVI, including soil attributes, climate and topography; and [III] RS + ENVI) were used to construct the salinity inversion model using random forest (RF) and extremely randomized trees (ERT) for cultivated areas in the coastal plain of Dongtai City, China. The hyperspectral data were also resampled to match the range of the image bands. RF performed better than ERT for all types of analyzed data, and RS + ENVI exhibited the best performance for Sentinel 2 (R2 = 0.86). Compared with the RS data alone, Landsat 9 and Sentinel 2 provided higher salinity simulations (41% and 126%, respectively) after combination with ENVI, and salinity mapping was closer to the actual soil salinity measurements. The variables of slope, salinity index (SIT), difference index and SIT had the highest contribution in Landsat 9, Sentinel 2 and resampled hyperspectrum based on Landsat 9 and Sentinel 2, respectively. In conclusion, RS + ENVI based on Sentinel 2 data is the recommended approach for monitoring the salt content of coastal cultivated soil.
Keywords
- Coastal area, Landsat 9, Remote sensing, Sentinel 2, Soil health, Sustainable land use
ASJC Scopus subject areas
- Agricultural and Biological Sciences(all)
- Agronomy and Crop Science
- Agricultural and Biological Sciences(all)
- Soil Science
- Earth and Planetary Sciences(all)
- Earth-Surface Processes
Sustainable Development Goals
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In: Soil and Tillage Research, Vol. 241, 106124, 09.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Inversion of coastal cultivated soil salt content based on multi-source spectra and environmental variables
AU - Jia, Pingping
AU - He, Wei
AU - Hu, Yi
AU - Liang, Yinku
AU - Liang, Yinku
AU - Xue, Lihua
AU - Zamanian, Kazem
AU - Zhao, Xiaoning
N1 - Funding Information: This work was supported by the Jiangsu Specially-Appointed Professor Project [grant number R2020T29]; the Xinjiang Tian Chi Specially-Appointed Professor Project; the Jiangsu Province Graduate Research and Practice Innovation Project [grant number KYCX23_1323]; Comprehensive analysis, inspection and testing platform for university-enterprise cooperation [grant number 2023-CX-PT].
PY - 2024/4/27
Y1 - 2024/4/27
N2 - Soil salinization seriously hinders the development of efficient ecological agriculture in coastal areas. The use of Landsat, Sentinel series and hyperspectral data is an ideal way for assessing soil salinity indicators. However, environmental data (e.g. climate, terrain and parent material) are important factors for estimating such indicators. It is necessary to find the advantages and limitations of a combination of satellite images, hyperspectral data and environmental variables (ENVI) for assessing soil salinity accurately. Various data or their combinations ([I] remote sensing [RS], i.e. bands and salinity indices of Landsat 9 and Sentinel 2; [II] ENVI, including soil attributes, climate and topography; and [III] RS + ENVI) were used to construct the salinity inversion model using random forest (RF) and extremely randomized trees (ERT) for cultivated areas in the coastal plain of Dongtai City, China. The hyperspectral data were also resampled to match the range of the image bands. RF performed better than ERT for all types of analyzed data, and RS + ENVI exhibited the best performance for Sentinel 2 (R2 = 0.86). Compared with the RS data alone, Landsat 9 and Sentinel 2 provided higher salinity simulations (41% and 126%, respectively) after combination with ENVI, and salinity mapping was closer to the actual soil salinity measurements. The variables of slope, salinity index (SIT), difference index and SIT had the highest contribution in Landsat 9, Sentinel 2 and resampled hyperspectrum based on Landsat 9 and Sentinel 2, respectively. In conclusion, RS + ENVI based on Sentinel 2 data is the recommended approach for monitoring the salt content of coastal cultivated soil.
AB - Soil salinization seriously hinders the development of efficient ecological agriculture in coastal areas. The use of Landsat, Sentinel series and hyperspectral data is an ideal way for assessing soil salinity indicators. However, environmental data (e.g. climate, terrain and parent material) are important factors for estimating such indicators. It is necessary to find the advantages and limitations of a combination of satellite images, hyperspectral data and environmental variables (ENVI) for assessing soil salinity accurately. Various data or their combinations ([I] remote sensing [RS], i.e. bands and salinity indices of Landsat 9 and Sentinel 2; [II] ENVI, including soil attributes, climate and topography; and [III] RS + ENVI) were used to construct the salinity inversion model using random forest (RF) and extremely randomized trees (ERT) for cultivated areas in the coastal plain of Dongtai City, China. The hyperspectral data were also resampled to match the range of the image bands. RF performed better than ERT for all types of analyzed data, and RS + ENVI exhibited the best performance for Sentinel 2 (R2 = 0.86). Compared with the RS data alone, Landsat 9 and Sentinel 2 provided higher salinity simulations (41% and 126%, respectively) after combination with ENVI, and salinity mapping was closer to the actual soil salinity measurements. The variables of slope, salinity index (SIT), difference index and SIT had the highest contribution in Landsat 9, Sentinel 2 and resampled hyperspectrum based on Landsat 9 and Sentinel 2, respectively. In conclusion, RS + ENVI based on Sentinel 2 data is the recommended approach for monitoring the salt content of coastal cultivated soil.
KW - Coastal area
KW - Landsat 9
KW - Remote sensing
KW - Sentinel 2
KW - Soil health
KW - Sustainable land use
UR - http://www.scopus.com/inward/record.url?scp=85191306064&partnerID=8YFLogxK
U2 - 10.1016/j.still.2024.106124
DO - 10.1016/j.still.2024.106124
M3 - Article
AN - SCOPUS:85191306064
VL - 241
JO - Soil and Tillage Research
JF - Soil and Tillage Research
SN - 0167-1987
M1 - 106124
ER -