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

• Published in: Agricultural water management Vol. 302; p. 108972
• Main Authors: Wu, Zongjun, Cui, Ningbo, Zhang, Wenjiang, Yang, Yenan, Gong, Daozhi, Liu, Quanshan, Zhao, Lu, Xing, Liwen, He, Qingyan, Zhu, Shidan, Zheng, Shunsheng, Wen, Shenglin, Zhu, Bin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-09-2024
• Subjects: Multi-modality data fusion; Multi-spectral data; Remote sensing; Soil moisture; Thermal infrared data; Unmanned aerial vehicle (UAV); Multi-modality data fusion; Multi-spectral data; Unmanned aerial vehicle (UAV); Soil moisture; Remote sensing; Thermal infrared data
• ISSN: 0378-3774
• DOI: 10.1016/j.agwat.2024.108972

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.
•Multi-modal UAV sensor data was used to predict soil moisture at different depths.•The gradient vanishing and sequence fragmentation in transitional RNN was addressed.•The hybrid CNN-LSTM model outperformed CNN and LSTM models.•The prediction accuracy of the model was highest under RGB + Mul + TIR data fusion.