Identification and Spatial Analysis of Land Salinity in China's Yellow River Delta Using a Land Salinity Monitoring Index from Harmonized UAV-Landsat Imagery
文献类型：期刊论文
| 作者 | Jiang, Liping; Qiu, Guanghui; Yu, Xinyang1,2 |
| 刊名 | SENSORS
 |
| 出版日期 | 2023-09-01 |
| 卷号 | 23期号:17页码:7584 |
| 关键词 | land salinity retrieval remote sensing spatial analysis random forest Landsat-9 OLI |
| ISSN号 | 1424-8220 |
| DOI | 10.3390/s23177584 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Precise identification and spatial analysis of land salinity in China's Yellow River Delta are essential for the rational utilization and sustainable development of land resources. However, the accurate retrieval model construction for monitoring land salinity remains challenging. This study constructed a land salinity retrieval framework using a harmonized UAV and Landsat-9 multi-spectral dataset. The Kenli district of the Yellow River Delta was selected as the case study area, and a land salinity monitoring index (LSMI) was proposed based on field survey data and UAV multi-spectral image and applied to the reflectance-corrected Landsat-9 OLI image. The land salinity distribution patterns were then mapped and spatially analyzed using Moran's I and Getis-Ord GI* analysis. The results demonstrated the following: (1) The LSMI-based method can accurately retrieve land salinity content with a validation determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of 0.75, 1.89, and 2.11, respectively. (2) Land salinization affected 93.12% of the cultivated land in the study area, and the severely saline soil grade (with a salinity content of 6-8 g/kg) covered 38.41% of the total cultivated land area and was widely distributed throughout the study area. (3) Saline land exhibited a positive spatial autocorrelation with a value of 0.311 at the p = 0.000 level; high-high cluster types occurred mainly in the Kendong and Huanghekou towns (80%), while low-low cluster types were mainly located in the Dongji, Haojia, Kenli, and Shengtuo towns (88.46%). The spatial characteristics of various salinity grades exhibit significant variations, and conducting separate spatial analyses is recommended for future studies. |
| 学科主题 | Chemistry ; Engineering ; Instruments & Instrumentation |
| WOS关键词 | SOIL-SALINITY ; VARIABILITY ; GROUNDWATER ; AREA |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| WOS记录号 | WOS:001062140800001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/197875]  |
| 专题 | 拉萨站高原生态系统研究中心_外文论文 |
| 作者单位 | 1.China Chem Geol & Mine Bur, Shandong Geol Explorat Inst, Jinan 250013, Peoples R China 2.Shandong Agr Univ, Coll Resources & Environm, Tai An 271018, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jiang, Liping,Qiu, Guanghui,Yu, Xinyang. Identification and Spatial Analysis of Land Salinity in China's Yellow River Delta Using a Land Salinity Monitoring Index from Harmonized UAV-Landsat Imagery[J]. SENSORS,2023,23(17):7584. |
| APA | Jiang, Liping,Qiu, Guanghui,&Yu, Xinyang.(2023).Identification and Spatial Analysis of Land Salinity in China's Yellow River Delta Using a Land Salinity Monitoring Index from Harmonized UAV-Landsat Imagery.SENSORS,23(17),7584. |
| MLA | Jiang, Liping,et al."Identification and Spatial Analysis of Land Salinity in China's Yellow River Delta Using a Land Salinity Monitoring Index from Harmonized UAV-Landsat Imagery".SENSORS 23.17(2023):7584. |
入库方式： OAI收割
来源：地理科学与资源研究所
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