High-Resolution Soil-Moisture Maps Over Landslide Regions in Northern California Grassland Derived From SAR Backscattering Coefficients
被引:10
作者:
Liao, Tien-Hao
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机构:
CALTECH, Div Geol & Planetary Sci, Pasadena, CA 91125 USACALTECH, Div Geol & Planetary Sci, Pasadena, CA 91125 USA
Liao, Tien-Hao
[1
]
Kim, Seung-Bum
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h-index: 0
机构:
CALTECH, Jet Prop Lab, Pasadena, CA 91125 USACALTECH, Div Geol & Planetary Sci, Pasadena, CA 91125 USA
Kim, Seung-Bum
[2
]
Handwerger, Alexander
论文数: 0引用数: 0
h-index: 0
机构:
Univ Calif Los Angeles, Joint Inst Reg Earth Syst Sci & Engn, Los Angeles, CA 91125 USA
CALTECH, Jet Prop Lab, Pasadena, CA 91109 USACALTECH, Div Geol & Planetary Sci, Pasadena, CA 91125 USA
Handwerger, Alexander
[3
,4
]
Fielding, Eric
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机构:
CALTECH, Jet Prop Lab, Pasadena, CA 91125 USACALTECH, Div Geol & Planetary Sci, Pasadena, CA 91125 USA
Fielding, Eric
[2
]
Cosh, Michael
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机构:
USDA ARS, Hydrol & Remote Sensing Lab, Beltsville, MD 20705 USACALTECH, Div Geol & Planetary Sci, Pasadena, CA 91125 USA
Cosh, Michael
[5
]
Schulz, William
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机构:
US Geol Survey, Landslide Hazards Program, Denver, CO 80225 USACALTECH, Div Geol & Planetary Sci, Pasadena, CA 91125 USA
Schulz, William
[6
]
机构:
[1] CALTECH, Div Geol & Planetary Sci, Pasadena, CA 91125 USA
[2] CALTECH, Jet Prop Lab, Pasadena, CA 91125 USA
[3] Univ Calif Los Angeles, Joint Inst Reg Earth Syst Sci & Engn, Los Angeles, CA 91125 USA
Terrain factors;
Soil moisture;
Poles and towers;
Synthetic aperture radar;
Soil measurements;
Monitoring;
Moisture measurement;
Landslides;
radar remote sensing;
soil moisture;
TIME-SERIES;
SEASONAL MOVEMENT;
WATER STORAGE;
SCATTERING;
MODELS;
D O I:
10.1109/JSTARS.2021.3069010
中图分类号:
TM [电工技术];
TN [电子技术、通信技术];
学科分类号:
0808 ;
0809 ;
摘要:
Slow-moving landslides are destabilized by accumulated precipitation and consequent soil moisture. Yet, the continuous high-resolution soil-moisture measurements needed to aid the understanding of landslide processes are generally absent in steep terrain. Here, we produce soil-moisture time-series maps for a seasonally active grassland landslide in the northern California coast ranges, USA, using backscattering coefficients from NASA's uninhabited aerial vehicle synthetic aperture radar at 6-m resolution. A physically based radar scattering model is used to retrieve the near-surface (5-cm depth) soil moisture for the landslide. Both forward modeling (backscattering estimation) and the retrieval (soil-moisture validation) show good agreement. The root-mean-square errors (RMSE) for vertical transmit vertical receive (VV) and horizontal transmit horizontal receive (HH) polarizations in forward model comparison are 1.93 dB and 1.88 dB, respectively. The soil-moisture retrieval shows unbiased RMSE of 0.054 m(3)/m(3). Our successful retrieval benefits from the surface and double-bounce scattering, which is common in grasslands. The retrieved maps show saturated wetness conditions within the active landslide boundaries. We also performed sensitivity tests for incidence angle and found that the retrieval is weakly dependent on the angle, especially while using copolarized HH and VV together. Using the two copolarized inputs, the retrieval is also not sensitive to the change of orientation angles of grass cylinders. The physical model inversion presented here can be generally applied for soil-moisture retrieval in areas with the same vegetation cover types in California.
机构:
Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R ChinaBeijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
Zhao, Yuhao
Jiang, Lingmei
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机构:
Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R ChinaBeijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
Jiang, Lingmei
Cui, Huizhen
论文数: 0引用数: 0
h-index: 0
机构:
Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R ChinaBeijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
Cui, Huizhen
IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM,
2023,
: 3106
-
3109