Monitoring soil organic carbon under coastal restoration using time series Sentinel-1
文献类型：期刊论文
| 作者 | Yang, Ren-Min; Huang, Lai-Ming |
| 刊名 | LAND DEGRADATION & DEVELOPMENT
 |
| 出版日期 | 2024-03-19 |
| 卷号 | N/A |
| 关键词 | Monte Carlo feature selection random forests soil organic carbon structural equation modeling synthetic aperture radar vegetation phenology |
| DOI | 10.1002/ldr.5105 |
| 产权排序 | 2 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Salt marsh soils are key reservoirs of organic carbon. Although the potential for using Sentinel-1 data to estimate organic carbon in marsh soils has been recognized, there is still a need to find suitable proxies and effective algorithms that enhance predictions. In this study, we assessed the effectiveness of Sentinel-1 data for predicting soil organic carbon (SOC) stocks in salt marshes located on the coast of eastern-central China. We defined the relations between SOC stocks and remotely sensed data using a knowledge-based approach (i.e., structural equation modeling (SEM)), and the results were compared with those obtained using a common data-driven approach (i.e., random forest (RF)). Predictive models were developed using (1) refined images associated with phenological stages (phenological variables), (2) refined images that were classified as statistically important using Monte Carlo feature selection (MCFS-based variables), and (3) images from the combination of these refined datasets. The predictions were validated using a leave-one-out cross-validation approach. The results showed that SEM was more accurate than RF when predicting SOC stocks using the same type of predictor variables. Models using phenological variables alone yielded the least accurate predictions. Adding phenological variables to the structural equation model with MCFS-based variables increased the prediction accuracy by 36% in terms of the R2. The results of the case study suggest that images related to phenological stages are good predictors for mapping SOC stocks in marshes. This study highlights the superiority of SEM over RF for developing effective remote sensing-based models to quantify and map SOC stocks in salt marshes. |
| WOS关键词 | SPARTINA-ALTERNIFLORA ; SALT-MARSH ; PREDICTION ; ALTERS |
| WOS研究方向 | Environmental Sciences & Ecology ; Agriculture |
| 语种 | 英语 |
| 出版者 | WILEY |
| WOS记录号 | WOS:001187102600001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/203318]  |
| 专题 | 黄河三角洲现代农业工程实验室_外文论文 |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resource & Environm, Beijing, Peoples R China 2.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Yellow River Delta Modern Agr Engn Lab, Beijing 100101, Peoples R China 4.Tianjin Univ, Sch Earth Syst Sci, Tianjin, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Ren-Min,Huang, Lai-Ming. Monitoring soil organic carbon under coastal restoration using time series Sentinel-1[J]. LAND DEGRADATION & DEVELOPMENT,2024,N/A. |
| APA | Yang, Ren-Min,&Huang, Lai-Ming.(2024).Monitoring soil organic carbon under coastal restoration using time series Sentinel-1.LAND DEGRADATION & DEVELOPMENT,N/A. |
| MLA | Yang, Ren-Min,et al."Monitoring soil organic carbon under coastal restoration using time series Sentinel-1".LAND DEGRADATION & DEVELOPMENT N/A(2024). |
入库方式： OAI收割
来源：地理科学与资源研究所
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