Name
Abstract | ||
|---|---|---|
Remote sensing inversion of vegetation-covered soil moisture is often affected by crop canopy, surface roughness, and other factors. In order to eliminate the scattering influence of vegetation effectively, this paper developed a modified vegetation backscattering model to retrieve vegetation-covered soil moisture based on multi-temporal RADARSAT-2 data and field measurements. This model combined the advantages of optical and radar methods by considering scattering contributions of underlying bare soil and vegetation canopy. Vegetation coverage was used to separate the scattering mechanism of the vegetation from bare soil component in a pixel. In addition, advanced integral equation method was presented to define the scattering of underlying bare soil. PROSAIL optical model was applied to calculate crown water content, which is an important variable associated with the scattering of vegetation canopy. Results demonstrated that the modified model on March 29, 2014 performed better in soil moisture retrieval than that at other growth stages with
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of 0.806 and root-mean-square error of 0.043 m
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·m
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, respectively. Soil moisture can be effectively retrieved by using the modified model in an agricultural region where the surface type is ranging from relatively sparse to full cover. Overall, the modified model provides an insight into extensive application of vegetation-covered soil moisture retrieval in agricultural regions. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JSTARS.2019.2891583 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Keywords | Field | DocType |
Soil moisture,Vegetation mapping,Scattering,Backscatter,Optical sensors,Synthetic aperture radar | Radar,Vegetation,Synthetic aperture radar,Backscatter,Remote sensing,Scattering,Water content,Mathematics,Surface roughness,Canopy | Journal |
Volume | Issue | ISSN |
12 | 2 | 1939-1404 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liangliang Tao | 1 | 0 | 0.34 |
| Guojie Wang | 2 | 15 | 5.35 |
| Weijing Chen | 3 | 0 | 0.34 |
| xi chen | 4 | 3 | 4.34 |
| Jing Li | 5 | 54 | 10.54 |
| Qingkong Cai | 6 | 0 | 0.34 |