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\begin{document}

\title{Filling the gap: Estimation of soil composition using InSAR, groundwater
depth, and precipitation data in California's Central Valley}



\author[1]{Kyongsik Yun}%
\author[1]{Kyra H. Kim}%
\author[2]{Anshuman Pradhan}%
\author[1]{John Reager}%
\author[1]{Zhen Liu}%
\author[3]{Michael Turmon}%
\author[1]{Alexander Huyen}%
\author[1]{Thomas Lu}%
\author[2]{Venkat Chandrasekaran}%
\author[2]{Andrew Stuart}%
\affil[1]{NASA Jet Propulsion Laboratory}%
\affil[2]{California Institute of Technology}%
\affil[3]{Jet Propulsion Laboratory, California Institute of Technology}%


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  \date{June 17, 2024}


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\begin{abstract}
California's Central Valley is responsible for \$17 billion of annual
agricultural output, producing 1/4 of the nation's food. However, land
in the Central Valley is sinking at a rapid rate (as much as 20 cm per
year) due to continued groundwater pumping. Land subsidence has a
significant impact on infrastructure resilience and groundwater
sustainability. It is important to understand subsidence and groundwater
depletion in a consistent framework using improved models capable of
simulating in-situ well observations and observed subsidence. Currently,
groundwater well data is sparse and sampled irregularly, compromising
our understanding of groundwater changes. Moreover, groundwater pumping
data is a major missing piece of the puzzle. Limited data availability
and spatial/temporal uncertainty in the available data have hampered
understanding the complex dynamics of groundwater and subsidence. To
address this limitation, we first integrated multimodal data including
InSAR, groundwater, precipitation, and soil composition by interpolating
data with the same spatial and temporal resolutions. We then identified
regions with different temporal dynamics of land displacement,
groundwater depth, and precipitation. Some areas (e.g., Helm) with
coarser grain soil compositions exhibited potentially reversible land
transformations (elastic land compaction). Finally, we fed the
integrated data into the deep neural network of a gated recurrent
unit-based sequence-to-sequence generation model. We found that the
combination of InSAR, groundwater depth, and precipitation data had
predictive power for soil composition using deep neural networks
(correlation coefficient R=0.83, normalized Nash-Sutcliffe model
efficiency NNSE=0.84). A random forest model was tested as baseline
(R=0.65, NNSE=0.69). We also achieved significant accuracy with only
40\% of the training data (NNSE=0.8), suggesting that the model can be
generalized to other regions for indirect estimation of soil
composition. Our results indicate that soil composition can be estimated
using InSAR, groundwater depth and precipitation data. In-situ
measurements of soil composition can be expensive and time consuming and
may be impractical in some areas. The generalizability of the model
sheds light on high spatial resolution soil composition estimation
utilizing existing measurements.%
\end{abstract}%



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