Non-Linear Models for Assessing Soil Moisture Estimation

Rui Li

doi:10.3390/horticulturae11050492

Non-Linear Models for Assessing Soil Moisture Estimation

2025-04-30

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DOI：10.3390/horticulturae11050492

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Accurately estimating soil moisture (SM) without direct measurements poses significant challenges due to nonlinear interactions in meteorological variables and the lagged response of SM to precipitation. This study evaluates two approaches: the auto-regressive integrated moving average (ARIMA) model for one-day-ahead SM forecasting and a K-means clustering-based multilayer perceptron (K-MLP) for real-time SM estimation at depths of 5 cm, 20 cm, and 50 cm in Changbai Mountain region. Although the K-MLP model outperformed the MLP model, achieving a maximum R2 of 0.728, its estimation accuracy remains suboptimal. By contrast, the ARIMA model effectively leveraged SM persistence, achieving high accuracy in one-day-ahead forecasting. Specifically, the ARIMA (0, 1, 6), ARIMA (1, 1, 2), and ARIMA (2, 1, 1) models yield R2 values of 0.9677, 0.9853, and 0.9684 and RMSE values of 0.02 m3m3, 0.015 m3m3, and 0.006 m3m3 at depths of 5 cm, 20 cm, and 50 cm, respectively. This study explores ARIMA's robustness in short-term SM forecasting and its adaptability to dynamic meteorological conditions, offering potential applications in agricultural water management and ecological monitoring.

Keywords:

soil moisture estimation

auto-regressive integrated moving average

multilayer perceptron

K-means clustering

Non-Linear Models for Assessing Soil Moisture Estimation

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