2 Data and Methods
2.1 Interferometric Synthetic Aperture Radar Processing and Analysis
Open-access SAR data from the C-band (~5.6 cm radar
wavelength) Copernicus Sentinel-1 A/B satellites were automatically
processed to standardized geocoded, unwrapped interferograms (GUNW) by
the ARIA project (Bekaert et al., 2019). ARIA uses the open-source JPL
InSAR Scientific Computing Environment (ISCE) software to process the
interferograms (Rosen et al., 2012). These standardized interferograms
are corrected for topographic contributions to phase and geocoded to a
~90 m (3 arc second) pixel spacing using the Shuttle
Radar Topography Mission (SRTM) digital elevation model (DEM) (Farr et
al., 2007). ARIA provides key data needed for deformation analyses and
time series inversions including geocoded unwrapped interferograms,
coherence, incidence and azimuth angles, and the SRTM DEM and water
mask.
We used the ARIA-tools open-source package in Python (Buzzanga et al.,
2020) to download and prepare 1689 interferograms covering California
(Table S1). We inverted the interferograms to deformation time series
using the Miami InSAR Time-series software in PYthon (MintPy) (Yunjun et
al., 2019). To remove longer time span interferograms that often have
low coherence and are more likely to contain unwrapping errors for
persistently moving features such as slow-moving landslides (e.g.,
Handwerger, Huang, et al., 2019), we used only 2 consecutive
interferogram pairs in the time series inversion. We quantified InSAR
uncertainty using a bootstrapping technique (Efron & Tibshirani, 1986;
Bekaert et al., 2020) with 400 iterations for each time series. More
information on the InSAR data processing can be found in the Supporting
Information.
2.2 Landslide Reconnaissance and Metrics
We identified active landslides by examining the 5-year time-averaged
InSAR velocity maps. Active slow-moving landslides displayed localized
deformation zones with relatively high velocity (Figures S2 and S3). We
then confirmed that the InSAR signals corresponded to true landslides by
overlaying the InSAR velocity maps onto DEMs, Google Earth imagery, and
previously published landslide inventories. We began searching for
active landslides by examining the InSAR velocity in well-known
landslide areas in northern California (Bennett, Miller, et al., 2016;
Handwerger, Fielding, et al., 2019; Kelsey, 1978), central California
(Booth et al., 2020; Cohen-Waeber et al., 2018; Finnegan et al., 2019;
Scheingross et al., 2013; Wills et al., 2001), and southern California
(Calabro et al., 2010; Jibson, 2006; Merriam, 1960; Swirad & Young,
2021; Young, 2015). We also examined the InSAR data alongside the
California Geologic Survey statewide landslide inventory (Wills et al.,
2017). After examining these known landslide areas, we then
systematically expanded outward from these regions to identify active
landslides in all mountainous regions of California. We quantified
landslide metrics such as area, length, width, and slope angle using the
SRTM ~30 m (1 arc second) DEM.
To estimate landslide thickness (and volume), which is often thought to
be one of the key length scales that controls landslide response to
rainfall (e.g., Handwerger et al., 2013), we applied recently developed
geometric scaling relations for slow-moving landslides in California
(Handwerger et al., 2021). These are particularly useful for estimating
thickness of slow-moving landslides, which is difficult to do without
numerous ground-based instruments. Landslide scaling relations take the
form of a power function as
\(h\ =\ c_{h}A^{\zeta}\) and \(V\ =\ c_{V}A^{\gamma}\) , (1)
where A is the landslide area, h is the estimated mean
thickness, V is the estimated volume, γ and ζ are the
scaling exponents and \(c_{V}\) and \(c_{h}\) are fit intercepts (see
parameters in Table S2). Landslide geometric scaling relations also vary
by landslide type and material (Bunn et al., 2020; Larsen et al., 2010;
Handwerger et al., 2021). To apply the most appropriate scaling
relations (Handwerger et al., 2021), we classified slow-moving
landslides as slumps (one primary kinematic zone and low length/width
aspect ratios), earthflows (one primary kinematic zone and medium aspect
ratios), and landslide complexes (amalgamations of landslides with
multiple kinematic zones and high aspect ratios).
To further assess the kinematic behavior of the slow-moving landslides
in wet and dry environments during wet and dry years, we selected a
subset of landslides to perform detailed time series investigation
(Figure 1d). These landslides were selected based on their relatively
high velocity signal (i.e., strong InSAR signal) and their location
within California’s different hydroclimatic regimes. We characterized
the landslide motion by calculating the spatial mean of the fastest
moving kinematic zone and used a moving median temporal smoothing filter
to further reduce noise and highlight the seasonal and annual
deformation signals (Figures S2 and S3). We explored environmental
controls on landslides by examining the rock type and precipitation data
in active landslide areas. Rock type data are provided by the California
Geologic Survey (Jennings et al., 2010) and precipitation data are
provided by the Parameter-elevation Regressions on Independent Slopes
Model (PRISM) (see Open Research). We then quantified landslide
sensitivity to rainfall by exploring relative changes in precipitation
and landslide velocity. To explore relative changes in precipitation and
velocity, we defined the Precipitation Ratio as the total water year
precipitation divided by the 30-year mean water year precipitation
(calculated from WY1990-WY2019) at each landslide (Figure 1d-i), and the
Velocity Ratio as the water year velocity divided by the average
velocity from WY2016-WY2019.