Establishing a periodic SM model with Fourier analysis for enhancing global soil moisture forecasting

Accurately predicting global soil moisture (SM) is crucial for sustainable agriculture and water resource management. Recognizing the challenges posed by the heterogeneity of SM's spatiotemporal variability, this study proposes a novel approach that leverages Fourier analysis to decompose the periodic fluctuations in SM, revealing underlying trends and cycles. This approach is integrated with Long Short Term Memory (LSTM) networks to enhance the accuracy of global SM prediction. Fourier analysis transforms time series data of SM into frequencies and amplitudes, capturing its intrinsic periodic characteristics. This transformation reveals both variable and invariant features representing changes within and between periods. By integrating these periodic features with sequence data and leveraging the memory and sequence learning capabilities of LSTM neural networks, the accuracy and reliability of global SM prediction can be enhanced. Our experiments on the LandBench1.0 dataset demonstrate that the proposed model reduces the root mean square error by [Formula: see text] to [Formula: see text] compared to the state-of-the-art methods. This study underscores that the LSTM with periodic features of SM can adapt to the inherent complex spatial-temporal patterns in SM dynamics, especially in scenarios characterized by rapid environmental changes and subtle temporal dynamics.