  Soil moisture is an important component of precision agriculture as it
directly impacts the growth and quality of vegetation. Forecasting soil
moisture is essential to schedule the irrigation and optimize the use of water.
Physics based soil moisture models need rich features and heavy computation
which is not scalable. In recent literature, conventional machine learning
models have been applied for this problem. These models are fast and simple,
but they often fail to capture the spatio-temporal correlation that soil
moisture exhibits over a region. In this work, we propose a novel graph neural
network based solution that learns temporal graph structures and forecast soil
moisture in an end-to-end framework. Our solution is able to handle the problem
of missing ground truth soil moisture which is common in practice. We show the
merit of our algorithm on real-world soil moisture data.
