High-Resolution Soil-Moisture Maps Over Landslide Regions in Northern California Grassland Derived From SAR Backscattering Coefficients

Liao, Tien-Hao; Kim, Seungbum; Handwerger, Alexander L.; Fielding, E. J.; Cosh, Michael H.; Schulz, William H.

doi:10.1109/jstars.2021.3069010

Cited by 8 publications

(2 citation statements)

References 48 publications

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“…Continuous and local-scale (e.g., from 1-m to 100-m resolutions) surface wetness information is essential for characterizing environmental heterogeneity and dynamics [22,28] and improving applications such as irrigation management [20], household-level flood risk assessment [29], and landslide monitoring [30]. Despite previous downscaling efforts, the potential of meter and sub-meter satellite remote sensing has not been fully explored, considering the growing capability of small or micro-satellites for rapid and high-resolution earth observations.…”

Section: Introductionmentioning

confidence: 99%

Local Scale (3-m) Soil Moisture Mapping Using SMAP and Planet SuperDove

Kimball

Bindlish

et al. 2022

Remote Sensing

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A capability for mapping meter-level resolution soil moisture with frequent temporal sampling over large regions is essential for quantifying local-scale environmental heterogeneity and eco-hydrologic behavior. However, available surface soil moisture (SSM) products generally involve much coarser grain sizes ranging from 30 m to several 10 s of kilometers. Hence, a new method is proposed to estimate 3-m resolution SSM using a combination of multi-sensor fusion, machine-learning (ML), and Cumulative Distribution Function (CDF) matching approaches. This method established favorable SSM correspondence between 3-m pixels and overlying 9-km grid cells from overlapping Planet SuperDove (PSD) observations and NASA Soil Moisture Active-Passive (SMAP) mission products. The resulting 3-m SSM predictions showed improved accuracy by reducing absolute bias and RMSE by ~0.01 cm3/cm3 over the original SMAP data in relation to in situ soil moisture measurements for the Australian Yanco region while preserving the high sampling frequency (1–3 day global revisit) and sensitivity to surface wetness (R 0.865) from SMAP. Heterogeneous soil moisture distributions varying with vegetation biomass gradients and irrigation regimes were generally captured within a selected study area. Further algorithm refinement and implementation for regional applications will allow for improvement in water resources management, precision agriculture, and disaster forecasts and responses.

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Section: Introductionmentioning

confidence: 99%

Local Scale (3-m) Soil Moisture Mapping Using SMAP and Planet SuperDove

Kimball

Bindlish

et al. 2022

Remote Sensing

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“…In previous studies about soil moisture retrieval, many alternative parameters for the Rms height have been proposed, but the parameters are easily affected by many factors, such as the precipitation, incidence direction of microwave and soil texture (Davidson et al, 2000;Benninga et al, 2019;Liao et al, 2021), so soil roughness is still an important parameter that affects the accuracy of the measured soil moisture content. Cultivating in the farmland significantly affects the surface roughness.…”

Section: Introductionmentioning

confidence: 99%

Retrieving the Soil Moisture in Bare Farmland Areas Using a Modified Dubois Model

Han

Quan-ming

2021

Front. Earth Sci.

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Soil moisture is an important parameter for global soil moisture transport, environmental evaluation, and precision agricultural research. The accurate retrieval of soil moisture in farmland areas using Synthetic Aperture Radar (SAR) depends on the accurate description of surface and SAR parameters. In these parameters, surface roughness and incidence angle are the key factors that affect the accuracy of the soil moisture retrieval model. This article proposes a modified Dubois model to retrieve soil moisture suitable for the bare surface of farmland area. The model eliminates the incidence angle parameters and uses polarization parameters to depict the surface roughness parameters in the Dubois model. To eliminate the incidence angle, the backscattering coefficients gamma0, which eliminates the effect of the incidence angles, are used to replace the sigma0. Under rain and no rain condition, the trend of backscattering coefficients (VH and VV) and cross-polarization ratio (VH-VV) of different soil texture with the soil moisture are compared. Then, the polarization parameter based on VH backscattering coefficients is used to describe surface roughness. The model is evaluated with time-series soil moisture observation data in situ of the study area. The results indicate that the modified model can retrieve soil moisture with high accuracy, and the total RMSE can reach 0.064 cm3cm−3 while the Dubois model is 0.124 cm3cm−3. Under rain and no rain condition, the retrieval accuracy of the modified model is 0.066 cm3cm−3 and 0.063 cm3cm−3. The retrieval accuracy is 0.060 cm3cm−3 and 0.067 cm3cm−3 under high and low incidence angles conditions, respectively. These results indicate that the modified Dubois model can retrieve soil moisture with high accuracy under different conditions.

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Three-dimensional hydrological thresholds to predict shallow landslides

Lee,

Oh,

Ray

et al. 2023

Terr Atmos Ocean Sci

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Past studies have focused on the importance of hydrological variables in analyzing landslide initiation condition. Even though precipitation is the main driver of shallow landslides and debris flows, use of only rainfall-based parameters has shown some limitations. Soil moisture has been used widely to improve threshold detection capabilities. Since soil moisture directly reflects the wetness status of the ground, it can be used to identify pore pressure fluctuations more effectively. This study used rainfall and soil moisture simultaneously to capture landslide initiation conditions in detail. Results showed that continued rainfall on the day of landslide leaded to a sudden increase in soil moisture, and that soil moisture increments (∆SM) were positive in 155 out of 170 landslide cases (91%). Two simple thresholds (daily precipitation over 40 mm, ∆SM over 0) and daily precipitation (P), Antecedent Precipitation Index (API), ∆SM-based three-dimensional threshold planes having 5%, 20% probability levels were applied and compared. With respect to false alarms (FA), P-based threshold was most effective among the single thresholds (FA ranging from 24 to 28 from September 2016 to December 2019 at five validation locations). Combining P- and ∆SM-based thresholds, FA reduced without compromising the detection accuracy (2 to 3 reduction in FA). Additionally combining three-dimensional threshold with 20% probability level, FA reduced significantly (ranging from 12 to 16), at the cost of two detection failures. These findings demonstrate the need for combining precipitation and soil moisture to determine landslide thresholds.

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High-Resolution Soil-Moisture Maps Over Landslide Regions in Northern California Grassland Derived From SAR Backscattering Coefficients

Cited by 8 publications

References 48 publications

Local Scale (3-m) Soil Moisture Mapping Using SMAP and Planet SuperDove

Local Scale (3-m) Soil Moisture Mapping Using SMAP and Planet SuperDove

Retrieving the Soil Moisture in Bare Farmland Areas Using a Modified Dubois Model

Three-dimensional hydrological thresholds to predict shallow landslides

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