Integrating infiltration processes in hybrid downscaling methods to estimate sub-surface soil moisture
文献类型：期刊论文
| 作者 | Zhang, Mo2,3; Ge, Yong1,2,3,4; Wang, Jianghao2,3 |
| 刊名 | ECOLOGICAL INFORMATICS
 |
| 出版日期 | 2024-12-01 |
| 卷号 | 84页码:102875 |
| 关键词 | Dual-drive method Genetic algorithm Machine learning Soil moisture analytical relationship model Soil moisture downscaling |
| DOI | 10.1016/j.ecoinf.2024.102875 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Soil moisture is a key variable in the water, energy, and carbon cycles. Mapping sub-surface soil moisture with fine spatial resolution requires integrating downscaling approaches and process-based models. However, the effectiveness of hybrid methods, such as regression kriging (RK), in enhancing soil moisture estimates through process-based parameter predictions remains inconclusive. This study aims to integrate infiltration processes into downscaling models to predict 1-km multi-layer soil moisture, while comparing performance of nonlinear and linear models, and evaluating RK improvements. Random forests (RF) and generalized linear model (GLM) were used to downscale surface soil moisture (0-5 cm) from 36-km Soil Moisture Active Passive satellite products to 1 km across the Qinghai-Tibet Plateau. Next, the soil moisture analytical relationship (SMAR) model was applied to simulate infiltration processes and obtain site-scale parameters. RK variants (RFRK and GLMRK) were applied to jointly predict the spatial distribution of multiple infiltration parameters, which were used in SMAR at 1-km grids to estimate sub-surface soil moisture (5-40 cm). The results showed that parameter calibration significantly enhanced sub-surface soil moisture simulation, reducing root mean square error (RMSE) by 61.2 % to 69.8 %, from 0.09 to 0.03. RF outperformed GLM across all depth intervals, providing higher prediction accuracy (average RMSE, RF: 0.07; GLM: 0.09). Moreover, RK enhanced the Nash-Sutcliffe efficiency coefficient (RFRK: 0.34; GLMRK: 0.28) and coefficient of determination (RFRK: 0.5; GLMRK: 0.38) by 7.7 %-13.3 % and 2.2 %-2.4 %. This study provides a reference for mapping multi-layer soil moisture through the integration of data-driven and knowledge-driven approaches in regional-scale study areas. |
| WOS关键词 | SPATIAL PREDICTION ; SURFACE MOISTURE ; WATER ; RESOLUTION ; NETWORK ; VARIABILITY ; DYNAMICS ; IMPACTS ; FOREST ; COVER |
| WOS研究方向 | Environmental Sciences & Ecology |
| WOS记录号 | WOS:001350270500001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/209511]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Zhang, Mo; Ge, Yong |
| 作者单位 | 1.Key Lab Intelligent Monitoring & Comprehens Manage, Nanchang, Jiangxi, Peoples R China 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China 4.Jiangxi Normal Univ, Key Lab Poyang Lake Wetland & Watershed Res, Minist Educ, Nanchang, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Mo,Ge, Yong,Wang, Jianghao. Integrating infiltration processes in hybrid downscaling methods to estimate sub-surface soil moisture[J]. ECOLOGICAL INFORMATICS,2024,84:102875. |
| APA | Zhang, Mo,Ge, Yong,&Wang, Jianghao.(2024).Integrating infiltration processes in hybrid downscaling methods to estimate sub-surface soil moisture.ECOLOGICAL INFORMATICS,84,102875. |
| MLA | Zhang, Mo,et al."Integrating infiltration processes in hybrid downscaling methods to estimate sub-surface soil moisture".ECOLOGICAL INFORMATICS 84(2024):102875. |
入库方式： OAI收割
来源：地理科学与资源研究所
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