Identification of irrigation events using Bayesian statistics-based change detection and soil moisture measurements
文献类型：期刊论文
| 作者 | Gao, Yu-Xin2,3; Leng, Pei3; Li, Jing3; Shang, Guo-Fei2; Zhang, Xia2; Li, Zhao-Liang1,3 |
| 刊名 | AGRICULTURAL WATER MANAGEMENT
 |
| 出版日期 | 2024-09-01 |
| 卷号 | 302页码:15 |
| 关键词 | Irrigation events Soil moisture time-series BEAST model Trend component Farmlands |
| ISSN号 | 0378-3774 |
| DOI | 10.1016/j.agwat.2024.108999 |
| 产权排序 | 3 |
| 英文摘要 | A comprehensive knowledge of irrigation information is crucial for agricultural water management. However, current investigations have mainly focused on extracting spatial extent of irrigated farmlands and quantifying irrigation amounts, lacking an understanding of irrigation timing at the field scale. In this study, a novel approach for detecting irrigation events from soil moisture (SM) time-series was proposed. To this end, in-situ SM measurements with different depths (10 cm, 25 cm, and 50 cm) were primarily decomposed into seasonal, trend, and residual components using the Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) model over a period of seven years from 2014 to 2020. The rationale for the determination of a specific irrigation timing relies on the observed rising abrupt change of SM time-series in its trend component when precipitation is unavailable. Specifically, the BEAST model was primarily optimized over two irrigated farmlands in the University of Nebraska Agricultural Research and Development Center near Mead, Nebraska, US. were subsequently used to identify irrigation. Results indicate that the decomposed SM time-series by the BEAST model correlate well with in-situ SM measurements with an average coefficient of determination of 0.98 and 0.97 over farmlands with continuous maize and maize-soybean rotation, respectively. Furthermore, it was found that SM measurements with a depth of 10 cm are optimal for detecting irrigation timing over the study area. When compared with local irrigation records, the accuracy of detected irrigation timing over farmlands with continuous maize and maize soybean rotation can reach 84 % and 89 %, respectively, revealing promising prospects for deriving irrigation timing with SM measurements. These results provide a reference for detecting irrigation timing using satellite-derived SM data. |
| WOS关键词 | LANDSAT TIME-SERIES ; TEMPERATURE ; TRENDS ; REGION ; WATER |
| 资助项目 | Central Public-interest Scientific Institution Basal Research Fund[41921001] ; Central Public-interest Scientific Institution Basal Research Fund[42271384] ; [Y2021XK26] |
| WOS研究方向 | Agriculture ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001291734900001 |
| 出版者 | ELSEVIER |
| 资助机构 | Central Public-interest Scientific Institution Basal Research Fund |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/209083]  |
| 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 | Leng, Pei |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Hebei GEO Univ, Hebei Int Joint Res Ctr Remote Sensing Agr Drought, Sch Land Sci & Space Planning, Shijiazhuang 050031, Peoples R China 3.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Yu-Xin,Leng, Pei,Li, Jing,et al. Identification of irrigation events using Bayesian statistics-based change detection and soil moisture measurements[J]. AGRICULTURAL WATER MANAGEMENT,2024,302:15. |
| APA | Gao, Yu-Xin,Leng, Pei,Li, Jing,Shang, Guo-Fei,Zhang, Xia,&Li, Zhao-Liang.(2024).Identification of irrigation events using Bayesian statistics-based change detection and soil moisture measurements.AGRICULTURAL WATER MANAGEMENT,302,15. |
| MLA | Gao, Yu-Xin,et al."Identification of irrigation events using Bayesian statistics-based change detection and soil moisture measurements".AGRICULTURAL WATER MANAGEMENT 302(2024):15. |
入库方式： OAI收割
来源：地理科学与资源研究所
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