SMArtCast: Predicting soil moisture interpolations into the future using Earth observation data in a deep learning framework

TL;DR

This paper introduces a deep learning framework using LSTM architectures to predict future soil moisture levels from satellite data, aiding crop health monitoring amid climate change.

Contribution

It develops a novel LSTM-based approach for forecasting soil moisture and vegetation indices, enabling spatially dense future moisture maps from sparse satellite measurements.

Findings

Effective prediction of future soil moisture levels.

Enhanced spatial interpolation of soil moisture maps.

Potential for early warning of inhospitable soil conditions.

Abstract

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.