Assessment of Remotely Sensed and Modelled Soil Moisture Data Products in the US Southern Great Plains
文献类型：期刊论文
| 作者 | Jiang, Bo1,2; Su, Hongbo3; Liu, Kai1; Chen, Shaohui1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2020-06-01 |
| 卷号 | 12期号:12页码:20 |
| 关键词 | SMAP ESACCI NLDAS-2 U S SGP soil moisture |
| DOI | 10.3390/rs12122030 |
| 通讯作者 | Su, Hongbo(hongbo@ieee.org) |
| 英文摘要 | Soil moisture (SM) plays a crucial role in the water and energy flux exchange between the atmosphere and the land surface. Remote sensing and modeling are two main approaches to obtain SM over a large-scale area. However, there is a big difference between them due to algorithm, spatial-temporal resolution, observation depth and measurement uncertainties. In this study, an assessment of the comparison of two state-of-the-art remotely sensed SM products, Soil Moisture Active Passive (SMAP) and European Space Agency Climate Change Initiative (ESACCI), and one land surface modeled dataset from the North American Land Data Assimilation System project phase 2 (NLDAS-2), were conducted using 17 permanent SM observation sites located in the Southern Great Plains (SGP) in the U.S. We first compared the daily mean SM of three products with in-situ measurements; then, we decompose the raw time series into a short-term seasonal part and anomaly by using a moving smooth window (35 days). In addition, we calculate the daily spatial difference between three products based on in-situ data and assess their temporal evolution. The results demonstrate that (1) in terms of temporal correlation R, the SMAP (R = 0.78) outperforms ESACCI (R = 0.62) and NLDAS-2 (R = 0.72) overall; (2) for the seasonal component, the correlation R of SMAP still outperforms the other two products, and the correlation R of ESACCI and NLDAS-2 have not improved like the SMAP; as for anomaly, there is no difference between the remotely sensed and modeling data, which implies the potential for the satellite products to capture the variations of short-term rainfall events; (3) the distribution pattern of spatial bias is different between the three products. For NLDAS-2, it is strongly dependent on precipitation; meanwhile, the spatial distribution of bias represents less correlation with the precipitation for two remotely sensed products, especially for the SMAP. Overall, the SMAP was superior to the other two products, especially when the SM was of low value. The difference between the remotely sensed and modeling products with respect to the vegetation type might be an important reason for the errors. |
| WOS关键词 | IN-SITU ; VALIDATION ; PRECIPITATION ; RETRIEVALS ; RESOLUTION ; WATER ; SMOS |
| 资助项目 | National Natural Science Foundation of China[41971315/41571356] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000553532700001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/158242]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Su, Hongbo |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Florida Atlantic Univ, Dept Civil Environm & Geomat Engn, Boca Raton, FL 33431 USA |
| 推荐引用方式 GB/T 7714 | Jiang, Bo,Su, Hongbo,Liu, Kai,et al. Assessment of Remotely Sensed and Modelled Soil Moisture Data Products in the US Southern Great Plains[J]. REMOTE SENSING,2020,12(12):20. |
| APA | Jiang, Bo,Su, Hongbo,Liu, Kai,&Chen, Shaohui.(2020).Assessment of Remotely Sensed and Modelled Soil Moisture Data Products in the US Southern Great Plains.REMOTE SENSING,12(12),20. |
| MLA | Jiang, Bo,et al."Assessment of Remotely Sensed and Modelled Soil Moisture Data Products in the US Southern Great Plains".REMOTE SENSING 12.12(2020):20. |
入库方式： OAI收割
来源：地理科学与资源研究所
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