Knowledge Resource Center for Ecological Environment in Arid Area
| DOI | 10.1007/s10661-022-10465-2 |
| Prediction of soil water contents and erodibility indices based on artificial neural networks: using topography and remote sensing | |
| Usta, Ayhan | |
| 通讯作者 | Usta, A |
| 来源期刊 | ENVIRONMENTAL MONITORING AND ASSESSMENT
 |
| ISSN | 0167-6369 |
| EISSN | 1573-2959 |
| 出版年 | 2022 |
| 卷号 | 194期号:10 |
| 英文摘要 | This study aimed to predict some soil water contents and soil erodibility indices with a multilayer perceptron (MLP) artificial neural network (ANN) using remote sensing data (Landsat 8 OLI TIRS) and topographic variables from a digital elevation model (DEM) in a semi-arid ecosystem. In models, the input variables were derived from remote sensing imaging and DEM. The output variables were field capacity, wilting point, aggregate stability index, structural stability index, dispersion ratio, and clay flocculation index. This study was realized in the watersheds of the Koruluk dam, the Kizlarkalesi, and the Telme ponds built for agricultural irrigation in Gumushane-Siran. The soil samples were obtained from two depths (0-10 cm and 10-20 cm) from 59 soil profiles. Besides field capacity, wilting point, and aggregate stability analysis, undispersed/dispersed sand, silt, clay contents, and organic matter analysis were performed due to their strong effect on soil moisture, soil water content, and erodibility indices. The correlation analysis results showed significant relationships between soil characteristics and soil water contents/soil erodibility indices. The remote sensing variables were derived from three Landsat images of 2015 (June, July, and September). The performance results of MLP ANN models predicted for soil water contents and erodibility indices ranged from 0.75 to 0.90 for R-2, 0.046-4.115 for root mean square error (RMSE), 4.46-6.54 for normalized root mean square error (NRMSE), and 0.042-0.186 for mean absolute error (MAE). Topography was a more significant group of variables that affected soil water contents and soil erodibility indices and the feature importance of topography in the prediction was over 55%. The results showed that the use of topographic variables together with remote sensing variables in MLP ANN modeling increased the performance of the models. |
| 英文关键词 | Soil characteristics Digital elevation model Landsat image Feature importance Semi-arid land Artificial neural networks |
| 类型 | Article |
| 语种 | 英语 |
| 收录类别 | SCI-E |
| WOS记录号 | WOS:000854171400005 |
| WOS关键词 | PERMANENT WILTING POINT ; LAND-USE TYPE ; AGGREGATE STABILITY ; ORGANIC-MATTER ; FIELD-CAPACITY ; PEDOTRANSFER FUNCTIONS ; MOISTURE PATTERNS ; TEXTURE ; CLAY ; MANAGEMENT |
| WOS类目 | Environmental Sciences |
| WOS研究方向 | Environmental Sciences & Ecology |
| 资源类型 | 期刊论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/392482 |
| 推荐引用方式 GB/T 7714 | Usta, Ayhan. Prediction of soil water contents and erodibility indices based on artificial neural networks: using topography and remote sensing[J],2022,194(10). |
| APA | Usta, Ayhan.(2022).Prediction of soil water contents and erodibility indices based on artificial neural networks: using topography and remote sensing.ENVIRONMENTAL MONITORING AND ASSESSMENT,194(10). |
| MLA | Usta, Ayhan."Prediction of soil water contents and erodibility indices based on artificial neural networks: using topography and remote sensing".ENVIRONMENTAL MONITORING AND ASSESSMENT 194.10(2022). |
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