  Soil moisture is critical component of crop health and monitoring it can
enable further actions for increasing yield or preventing catastrophic die off.
As climate change increases the likelihood of extreme weather events and
reduces the predictability of weather, and non-optimal soil moistures for crops
may become more likely. In this work, we a series of LSTM architectures to
analyze measurements of soil moisture and vegetation indiced derived from
satellite imagery. The system learns to predict the future values of these
measurements. These spatially sparse values and indices are used as input
features to an interpolation method that infer spatially dense moisture map for
a future time point. This has the potential to provide advance warning for soil
moistures that may be inhospitable to crops across an area with limited
monitoring capacity.
