Name
Abstract | ||
|---|---|---|
High-resolution soil moisture dataset is crucial for various application such as meteorology, climatology, hydrology and agriculture. Active microwave remote sensing sensors like radar provide earth observations at high spatial resolutions. This study based on physical model simulations (Advanced Integral Equation Method, AIEM, and Water Cloud Model, WCM) combined with the Artificial Neural Networks to investigate the potential of the ALOS-2 and Sentinel-1 radar images for estimating soil moisture at high spatial resolution. The results shows that the statistical parameters of the relationships between estimated and measured soil moisture, expressed in terms of R, bias, and RMSE, are 0.834 similar to 0.878, 1.59 similar to 3.65 vol% and 3.36 similar to 6.15 vol% for ALOS-2, and 0.722 similar to 0.896, 1.75 similar to 2.97 vol% and 3.24 similar to 6.86 vol%, for Sentinel-1. In densely vegetated area, RMSE significant increases, due to the limited penetration ability of L and C bands in high vegetation areas. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IGARSS39084.2020.9323735 | IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM |
Keywords | DocType | Citations |
soil moisture, ALOS-2, Sentinel-1, ANN | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huizhen Cui | 1 | 1 | 1.71 |
| Lingmei Jiang | 2 | 34 | 7.85 |
| Simonetta Paloscia | 3 | 0 | 0.34 |
| Emanuele Santi | 4 | 119 | 27.15 |
| Simone Pettinato | 5 | 108 | 22.96 |
| Jian Wang | 6 | 0 | 0.34 |
| Gongxue Wang | 7 | 0 | 2.37 |