Improve State-Level Wheat Yield Forecasts in Kazakhstan on GEOGLAM's EO
  Data by Leveraging A Simple Spatial-Aware Technique
Anh Nhat Nhu Ritvik Sahajpal Christina Justice Inbal Becker-Reshef
Accurate yield forecasting is essential for making informed policies and
long-term decisions for food security. Earth Observation (EO) data and machine
learning algorithms play a key role in providing a comprehensive and timely
view of crop conditions from field to national scales. However, machine
learning algorithms' prediction accuracy is often harmed by spatial
heterogeneity caused by exogenous factors not reflected in remote sensing data,
such as differences in crop management strategies. In this paper, we propose
and investigate a simple technique called state-wise additive bias to
explicitly address the cross-region yield heterogeneity in Kazakhstan. Compared
to baseline machine learning models (Random Forest, CatBoost, XGBoost), our
method reduces the overall RMSE by 8.9\% and the highest state-wise RMSE by
28.37\%. The effectiveness of state-wise additive bias indicates machine
learning's performance can be significantly improved by explicitly addressing
the spatial heterogeneity, motivating future work on spatial-aware machine
learning algorithms for yield forecasts as well as for general geospatial
forecasting problems.