How Important Are the Critical Points in Selecting the Optimal Samples for Accurate Estimation of Subsurface Soil Moisture?

Abstract We propose a novel physics‐aware sampling framework to extract the most informative training instances (critical points) from the conventional 70% training data set using six diversified sampling strategies. These critical points are used to train a spectral Fourier Neural Operator (FNO) augmented with a lagged term to predict subsurface soil moisture (20 and 40 cm) from near‐surface observations (5 cm). This approach is evaluated at eight stations of the International Soil Moisture Network spanning two climatic regimes (Dwc: monsoon‐influenced subarctic; and BWk: arid and cold desert) with hourly soil moisture measurements available at these depths. We demonstrate that judiciously selected training subsets achieve predictive accuracy comparable or even superior to the full‐training‐data baseline model. Uncertainty‐based sampling is optimal for Dwc stations (with 10% training data). Distribution‐based selection excels at BWk sites incorporating training proportions between 10% and 62% with minimal nRMSE. Temporal analysis of the selection of critical points reveals significant climatic controls.