Estimation of soil moisture in drip-irrigated citrus orchards using multi-modal UAV remote sensing

Zongjun Wu, Ningbo Cui, Wenjiang Zhang, Yenan Yang, Daozhi Gong, Quanshan Liu, Lu Zhao, Liwen Xing, Qingyan He, Shidan Zhu, Shunsheng Zheng, Shenglin Wen and Bin Zhu

Agricultural Water Management, 2024, vol. 302, issue C

Abstract: Accurate and timely prediction of soil moisture in orchards is crucial for making informed irrigation decisions at a regional scale. Conventional methods for monitoring soil moisture are often limited by high cost and disruption of soil structure, etc. However, unmanned aerial vehicle (UAV) remote sensing, with high spatial and temporal resolutions, offers an effective alternative for monitoring regional soil moisture. In this study, multi-modal UAV remote sensing data, including RGB, thermal infrared (TIR), and multi-spectral (Mul) data, were acquired in citrus orchards. The correlations between different sensor data and soil moisture were analyzed to construct seven input combinations. Convolutional neural network (CNN), long short-term memory (LSTM) models and a new hybrid model (CNN-LSTM), were employed to predict soil moisture at depths of 5 cm, 10 cm, 20 cm and 40 cm. Additionally, the impact of standalone sensor, texture features and multi-sensor data fusion on the accuracy of soil moisture prediction was explored. The results indicated that the model with RGB + Mul + TIR achieved the highest prediction accuracy, followed by those with Mul + TIR and RGB + Mul, with the coefficient of determination (R2) ranging 0.80–0.88, 0.64–0.84, and 0.60–0.81, and root mean square error (RMSE) ranging 2.46–2.99 m3·m−3, 2.86–3.89 m3·m−3 and 3.15–4.25 m3·m−3, respectively. Among single sensor inputs, the Mul sensor data has the highest prediction accuracy, followed by TIR and RGB sensor, with the coefficient of determination (R2) ranging 0.54–0.72, 0.36–0.52 and 0.14–0.26, and root mean square error (RMSE) ranging 3.72–4.58 %, 3.81–5.04 % and 4.27–6.21 %, respectively. The hybrid CNN-LSTM model exhibited the highest prediction accuracy, followed by CNN and LSTM models, with the coefficient of determination (R2) ranging 0.20–0.88, 0.16–0.83, and 0.14–0.81, and root mean square error (RMSE) ranging 2.46–5.01 m3·m−3, 2.68–5.35 m3·m−3 and 2.81–6.21 m3·m−3, respectively. The prediction accuracy of the models was the highest at the depth of 5 cm, followed by 10 cm, 20 cm and 40 cm, with the coefficient of determination (R2) average of 0.63, 0.62, 0.59, and 0.55, and root mean square error (RMSE) average of 3.70 m3·m−3, 3.79 m3·m−3, 3.85 m3·m−3 and 4.21 m3·m−3, respectively. Therefore, the hybrid CNN-LSTM model with RGB + Mul + TIR is recommended to predict soil moisture in citrus orchard. It provides method and data support for regional precision irrigation decision-making.

Keywords: Soil moisture; Thermal infrared data; Multi-spectral data; Unmanned aerial vehicle (UAV); Multi-modality data fusion; Remote sensing (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:302:y:2024:i:c:s037837742400307x

DOI: 10.1016/j.agwat.2024.108972

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