Filling Data Gaps in Soil Moisture Monitoring Networks via Integrating Spatio-temporal Contextual Information

Abstract. As critical inputs for global climate studies, watershed hydrologic modeling, and satellite soil moisture product validation, in situ soil moisture measurements are frequently compromised by sensor-derived data gaps that disrupt hydrological continuity. To overcome this challenge, we develop ST-GapFill, a novel spatiotemporal reconstruction framework integrating multi-source contextual information through two key innovations: (1) Spatial correlation-guided neighbor selection that identifies optimal auxiliary stations; (2) A long short-term memory (LSTM) network is employed to capture the complex temporal dependencies within the soil moisture time series. Validation on in-situ networks demonstrates that ST-GapFill successfully reconstructs soil moisture dynamics with preserved diurnal-phase fluctuations, achieving 0.91 correlation coefficients with ground truth under low missing-rate conditions (<50 %). Comparative analysis reveals the ST-GapFill 's statistically superior performance (RMSE reduction: 27.0 % vs IDW, 67.8 % vs ARIMA). This method establishes a robust spatiotemporal imputation paradigm for environmental sensor networks, effectively bridging observation gaps to support precision agriculture and climate change impact assessments.