Assessing spatial variations in soil organic carbon and C:N ratio in Northeast China's black soil region: Insights from Landsat-9 satellite and crop growth information
文献类型：期刊论文
| 作者 | Geng, Jing3,4; Tan, Qiuyuan4; Lv, Junwei4; Fang, Huajun1,2 |
| 刊名 | SOIL & TILLAGE RESEARCH
 |
| 出版日期 | 2024 |
| 卷号 | 235页码:14 |
| ISSN号 | 0167-1987 |
| 关键词 | Black soils Landsat-9 Crop growth Multi -temporal imagery |
| DOI | 10.1016/j.still.2023.105897 |
| 通讯作者 | Geng, Jing(gengj9@mail.sysu.edu.cn) |
| 英文摘要 | Monitoring soil properties, especially soil organic carbon (SOC) and Carbon-to-Nitrogen (C:N) ratios, is vital for understanding degradation and developing black soil conservation strategies. Most soil mapping research has leveraged satellite imagery from limited bare-soil periods. The predictive accuracy using time-series images capturing crop growth is underexplored. Moreover, while the new Landsat-9 satellite surpasses Landsat-8, its use in soil mapping is mostly uncharted. In this study, we compared the performance of single-date bare soil imagery (Landsat-8, Landsat-9, and Sentinel-2) and multi-temporal images (Landsat-8 and Landsat-9 combined, and Sentinel-2) using three machine learning techniques (Boosted Regression Tree, BRT; Random Forest, RF; Extreme Gradient Boosting, XGBoost) for mapping SOC and C:N ratio in a typical Northeast China black soil cropland region. The results revealed that single-date Landsat-9 exhibited great potential for soil mapping with the optimal XGBoost model, improving the prediction accuracy (in terms of R2) of SOC and C:N ratio by 15.01% and 30.07%, respectively, compared to Landsat-8, while also delivering performance slightly inferior to single-date Sentinel-2. Moreover, predictors derived from multi-temporal images significantly outperformed those derived from single-date images. The XGBoost models that utilized multi-temporal Sentinel-2 predictors achieved the highest prediction accuracy for both SOC (R2 = 0.676, RMSE = 1.928 g/kg, MAE = 1.580 g/kg, RPD = 1.535) and C:N ratio (R2 = 0.713, RMSE = 0.585, MAE = 0.484, RPD = 1.718). Interestingly, the combination of Landsat-8 and Landsat-9 demonstrated similar prediction accuracy but lower uncertainties in SOC and C:N ratio mapping compared to multi-temporal Sentinel-2 images. In addition, a comparison of the contemporary prediction soil maps with historical soil data revealed a continual decrease in SOC content and C:N ratio, suggesting a concerning trajectory that is detrimental to organic matter accumulation. Overall, this study highlights the efficacy of Landsat-9 and multi-temporal imagery incorporating crop growth information in accurately predicting soil properties and assessing their spatial variability. |
| WOS关键词 | SENTINEL-2 ; PREDICTION ; CROPLANDS ; EROSION |
| 资助项目 | National Natural Science Foundation of China[32101301] ; National Natural Science Foundation of China[32371725] ; National Natural Science Foundation of China[41977041] ; Guangdong Basic and Applied Basic Research Foundation[2020A1515110172] ; Unveiling the List of Hanging Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone[20222-051244] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA28130100] |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:001083856800001 |
| 资助机构 | National Natural Science Foundation of China ; Guangdong Basic and Applied Basic Research Foundation ; Unveiling the List of Hanging Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone ; Strategic Priority Research Program of the Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/198945]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Geng, Jing |
| 作者单位 | 1.Zhongke Jian Inst Ecoenvironm Sci, Jian 343000, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 3.Minist Nat Resources, Key Lab Nat Resources Monitoring Trop & Subtrop Ar, Zhuhai 519082, Peoples R China 4.Sun Yat Sen Univ, Sch Geospatial Engn & Sci, Zhuhai 519082, Peoples R China |
| 推荐引用方式 GB/T 7714 | Geng, Jing,Tan, Qiuyuan,Lv, Junwei,et al. Assessing spatial variations in soil organic carbon and C:N ratio in Northeast China's black soil region: Insights from Landsat-9 satellite and crop growth information[J]. SOIL & TILLAGE RESEARCH,2024,235:14. |
| APA | Geng, Jing,Tan, Qiuyuan,Lv, Junwei,&Fang, Huajun.(2024).Assessing spatial variations in soil organic carbon and C:N ratio in Northeast China's black soil region: Insights from Landsat-9 satellite and crop growth information.SOIL & TILLAGE RESEARCH,235,14. |
| MLA | Geng, Jing,et al."Assessing spatial variations in soil organic carbon and C:N ratio in Northeast China's black soil region: Insights from Landsat-9 satellite and crop growth information".SOIL & TILLAGE RESEARCH 235(2024):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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