A Two-Source Normalized Soil Thermal Inertia Model for Estimating Field-Scale Soil Moisture from MODIS and ASTER Data
文献类型：期刊论文
| 作者 | Hao, Guibin1,2; Su, Hongbo3; Zhang, Renhua2; Tian, Jing2; Chen, Shaohui2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-03-01 |
| 卷号 | 14期号:5页码:20 |
| 关键词 | soil moisture soil thermal inertia ASTER MODIS remote sensing |
| DOI | 10.3390/rs14051215 |
| 通讯作者 | Su, Hongbo(hongbo@ieee.org) |
| 英文摘要 | Soil moisture (SM) is a crucial component for understanding, modeling, and forecasting terrestrial water cycles and energy budgets. However, estimating field-scale SM based on thermal infrared remote-sensing data is still a challenging task. In this study, an improved Flexible Spatiotemporal DAta Fusion (FSDAF) method based on land-surface Diurnal Temperature Cycle (DTC) model (DFSDAF) was proposed to fuse Moderate Resolution Imaging Spectroradiometer (MODIS) and Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) land-surface temperature (LST) data to generate ASTER-like LST during the night. The reconstructed diurnal LST data at a high spatial resolution (90 m) was then utilized to drive a two-source normalized soil thermal inertia model (TNSTI) for the vegetated surfaces to estimate field-scale SM. The results of the proposed methods were validated at different observation depths (2, 4, 10, 20, 40, 60, and 100 cm) over the Zhangye oasis in the middle region of the Heihe River basin in the northwest of China and were compared with the SM estimates from the TNSTI model and other SM products, including AMSR2/AMSR-E, GLDAS-Noah, and ERA5-land. The results showed the following: (1) The DFSDAF method increased the accuracy of LST prediction, with the determination coefficient (R-2) increasing from 0.71 to 0.77, and root mean square error (RMSE) decreasing from 2.17 to 1.89 K. (2) the estimated SMs had the best correlation with the observations at the 10 cm depth (with R-2 of 0.657; RMSE of 0.069 m(3)/m(3)), but the worst correlation with observations at the 40 cm depth (with R-2 of 0.262; RMSE of 0.092 m(3)/m(3)); meanwhile, the modeled SMs were significantly underestimated above 40 cm (2, 4, 10, and 20 cm) and slightly overestimated below 40 cm (60 and 100 cm); in addition, the field-scale SM series at high spatial resolution (90 m) showed significant spatiotemporal variation. (3) The SM estimates based on the TNSTI for the vegetated surfaces are more capable of characterizing the SM status in the root zone (~80 cm) or even deeper, while the SMs from AMSR2/AMSR-E, GLDAS-Noah, or ERA5-land products are closer to the SM in the surface layer (the depth is less than 5 cm). The TNSTI provided favorable data supports for hydrological model simulations and showed potential advantages for agricultural refinement managements and smart agriculture. |
| WOS关键词 | HEAT-FLUX ; AMSR-E ; RETRIEVAL ; TEMPERATURE ; SATELLITE ; ALGORITHM ; DROUGHT ; IMAGES ; VEGETATION ; RADIOMETER |
| 资助项目 | National Natural Science Foundation of China[41971315/41571356/U2003105/42071327] ; National Key Research and Development Program of China[2021YFC3201102] ; National Aeronautics and Space Administration |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000768262900001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Aeronautics and Space Administration |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/172134]  |
| 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.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, 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 | Hao, Guibin,Su, Hongbo,Zhang, Renhua,et al. A Two-Source Normalized Soil Thermal Inertia Model for Estimating Field-Scale Soil Moisture from MODIS and ASTER Data[J]. REMOTE SENSING,2022,14(5):20. |
| APA | Hao, Guibin,Su, Hongbo,Zhang, Renhua,Tian, Jing,&Chen, Shaohui.(2022).A Two-Source Normalized Soil Thermal Inertia Model for Estimating Field-Scale Soil Moisture from MODIS and ASTER Data.REMOTE SENSING,14(5),20. |
| MLA | Hao, Guibin,et al."A Two-Source Normalized Soil Thermal Inertia Model for Estimating Field-Scale Soil Moisture from MODIS and ASTER Data".REMOTE SENSING 14.5(2022):20. |
入库方式： OAI收割
来源：地理科学与资源研究所
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