Knowledge Resource Center for Ecological Environment in Arid Area
| DOI | 10.1515/geo-2020-0286 |
| A PLSR model to predict soil salinity using Sentinel-2 MSI data | |
| Sahbeni, Ghada | |
| 通讯作者 | Sahbeni, G (corresponding author), Eotvos Lorand Univ, Dept Geophys & Space Sci, Pazmany Peter Stny 1-A, H-1117 Budapest, Hungary. |
| 来源期刊 | OPEN GEOSCIENCES
 |
| ISSN | 2391-5447 |
| 出版年 | 2021 |
| 卷号 | 13期号:1页码:977-987 |
| 英文摘要 | Salinization is one of the most widespread environmental threats in arid and semi-arid regions that occur either naturally or artificially within the soil. When exceeding the thresholds, salinity becomes a severe danger, damaging agricultural production, water and soil quality, biodiversity, and infrastructures. This study used spectral indices, including salinity and vegetation indices, Sentinel-2 MSI original bands, and DEM, to model soil salinity in the Great Hungarian Plain. Eighty-one soil samples in the upper 30 cm of the soil surface were collected from vegetated and nonvegetated areas by the Research Institute for Soil Sciences and Agricultural Chemistry (RISSAC). The sampling campaign of salinity monitoring was performed in the dry season to enhance salt spectral characteristics during its accumulation in the subsoil. Hence, applying a partial least squares regression (PLSR) between salt content (g/kg) and remotely sensed data manifested a highly moderate correlation with a coefficient of determination R-2 of 0.68, a p-value of 0.000017, and a root mean square error of 0.22. The final model can be deployed to highlight soil salinity levels in the study area and assist in understanding the efficacy of land management strategies. |
| 英文关键词 | soil salinity Sentinel-2 MSI PLSR regression analysis multispectral remote sensing statistical modeling the Great Hungarian Plain |
| 类型 | Article |
| 语种 | 英语 |
| 开放获取类型 | gold |
| 收录类别 | SCI-E |
| WOS记录号 | WOS:000709472600001 |
| WOS关键词 | SPECTRAL INDEXES ; SALINIZATION ; REGRESSION ; LAND ; BIOMASS ; REGION ; IMAGES |
| WOS类目 | Geosciences, Multidisciplinary |
| WOS研究方向 | Geology |
| 资源类型 | 期刊论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/367992 |
| 作者单位 | [Sahbeni, Ghada] Eotvos Lorand Univ, Dept Geophys & Space Sci, Pazmany Peter Stny 1-A, H-1117 Budapest, Hungary |
| 推荐引用方式 GB/T 7714 | Sahbeni, Ghada. A PLSR model to predict soil salinity using Sentinel-2 MSI data[J],2021,13(1):977-987. |
| APA | Sahbeni, Ghada.(2021).A PLSR model to predict soil salinity using Sentinel-2 MSI data.OPEN GEOSCIENCES,13(1),977-987. |
| MLA | Sahbeni, Ghada."A PLSR model to predict soil salinity using Sentinel-2 MSI data".OPEN GEOSCIENCES 13.1(2021):977-987. |
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