Work

Enhancing soil moisture prediction for monitoring biological signals: A frequency-domain and spatial weighting approach

Year: 2026

Type: article

Abstract: Biological signals—such as plant electrical activity and microbial byproducts—are increasingly valued as indicators of how ecosystems respond to environmental change. These signals are closely linked to soil moisture, a key environmental factor that shapes plant hydration, microbial function, and root-soil interactions. As such, accurately predicting soil moisture is essential for monitoring ecosystem health and understanding the dynamic interplay between living organisms and their environment. Despite recent advances, many existing prediction models struggle with two main limitations: sensitivity to high-frequency noise and poor spatial generalisation. To address these issues, we present a new modelling approach that combines frequency-domain filtering with spatial weighting. By applying a Fourier transform, our method filters out high-frequency noise while preserving low-frequency patterns that are ecologically and biologically relevant. In parallel, a cosine Latitude-weighted loss function is introduced to balance spatial representation during model training. We evaluated this strategy across four deep learning models. We found consistent gains across all four deep-learning models, with the Lat-FT-LSTM (i.e., the LSTM model combining Fourier transform and Latitude-weighted loss) performing best. Relative to the baseline (LSTM), the coefficient of determination (R 2 ) rose by 6.45% and the Kling-Gupta Efficiency (KGE) by 6.31%, while unbiased RMSE and Bias fell by 10.22% and 13.33%, respectively. Improvements were most substantial in biologically active regions—North Africa, the Middle East, Central Asia, and North America—suggesting that combining frequency-domain filtering with latitude-based weighting improves both accuracy and transferability. Together, these results suggest that integrating basic signal processing and spatial weighting into the modelling workflow can enhance ecosystem monitoring and the interpretation of biological responses.

Source: Applied Computing and Geosciences

DOI: 10.1016/j.acags.2026.100328

Authors: Xiaoning Li, Mengyuan Zhang, Zhichao Zhong, Qingliang Li, Xiaolin Li

Institution: Changchun Normal University

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life on land, Zero hunger

Open Access status: gold

Estimated APC paid: $2,040