A Neural-Network Based Spatial Resolution Downscaling Method for Soil Moisture: Case Study of Qinghai Province
文献类型：期刊论文
| 作者 | Lv, Aifeng1,3; Zhang, Zhilin2; Zhu, Hongchun2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2021-04-01 |
| 卷号 | 13期号:8页码:22 |
| 关键词 | soil moisture neural network downscaling microwave data MODIS data |
| DOI | 10.3390/rs13081583 |
| 通讯作者 | Lv, Aifeng(lvaf@igsnrr.ac.cn) |
| 英文摘要 | Currently, soil-moisture data extracted from microwave data suffer from poor spatial resolution. To overcome this problem, this study proposes a method to downscale the soil moisture spatial resolution. The proposed method establishes a statistical relationship between low-spatial-resolution input data and soil-moisture data from a land-surface model based on a neural network (NN). This statistical relationship is then applied to high-spatial-resolution input data to obtain high-spatial-resolution soil-moisture data. The input data include passive microwave data (SMAP, AMSR2), active microwave data (ASCAT), MODIS data, and terrain data. The target soil moisture data were collected from CLDAS dataset. The results show that the addition of data such as the land-surface temperature (LST), the normalized difference vegetation index (NDVI), the normalized shortwave-infrared difference bare soil moisture indices (NSDSI), the digital elevation model (DEM), and calculated slope data (SLOPE) to active and passive microwave data improves the retrieval accuracy of the model. Taking the CLDAS soil moisture data as a benchmark, the spatial correlation increases from 0.597 to 0.669, the temporal correlation increases from 0.401 to 0.475, the root mean square error decreases from 0.051 to 0.046, and the mean absolute error decreases from 0.041 to 0.036. Triple collocation was applied in the form of [NN, FY3C, GEOS-5] based on the extracted retrieved soil-moisture data to obtain the error variance and correlation coefficient between each product and the actual soil-moisture data. Therefore, we conclude that NN data, which have the lowest error variance (0.00003) and the highest correlation coefficient (0.811), are the most applicable to Qinghai Province. The high-spatial-resolution data obtained from the NN, CLDAS data, SMAP data, and AMSR2 data were correlated with the ground-station data respectively, and the result of better NN data quality was obtained. This analysis demonstrates that the NN-based method is a promising approach for obtaining high-spatial-resolution soil-moisture data. |
| WOS关键词 | DROUGHT ; TEMPERATURE ; VALIDATION ; RADIOMETER ; ALGORITHM ; DYNAMICS |
| 资助项目 | National Natural Science Foundation of China[41671026] ; Important Science & Technology Specific Projects of Qinghai Province[2019-SF-A4-1] ; Scientific Research and Promotion Projects of the Second Phase Project of Ecological Protection and Construction of the Three Rivers Source in Qinghai Province[2018-S-3] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000644677000001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; Important Science & Technology Specific Projects of Qinghai Province ; Scientific Research and Promotion Projects of the Second Phase Project of Ecological Protection and Construction of the Three Rivers Source in Qinghai Province |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/161694]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Lv, Aifeng |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 2.Shandong Univ Sci & Technol, Sch Surveying & Mapping Sci & Engn, Qingdao 266000, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lv, Aifeng,Zhang, Zhilin,Zhu, Hongchun. A Neural-Network Based Spatial Resolution Downscaling Method for Soil Moisture: Case Study of Qinghai Province[J]. REMOTE SENSING,2021,13(8):22. |
| APA | Lv, Aifeng,Zhang, Zhilin,&Zhu, Hongchun.(2021).A Neural-Network Based Spatial Resolution Downscaling Method for Soil Moisture: Case Study of Qinghai Province.REMOTE SENSING,13(8),22. |
| MLA | Lv, Aifeng,et al."A Neural-Network Based Spatial Resolution Downscaling Method for Soil Moisture: Case Study of Qinghai Province".REMOTE SENSING 13.8(2021):22. |
入库方式： OAI收割
来源：地理科学与资源研究所
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