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Accurate reference crop evapotranspiration (ET 0) estimation is essential for agricultural water management, crop productivity, and irrigation systems. As the standard ET 0 estimation method, the Penman-Monteith equation has been widely recommended worldwide. However, its application is still restricted to comprehensive meteorological data deficiency, making the exploration of alternative simpler models for acceptable ET 0 estimation highly meaningful. Concerning the aforementioned requirement, this study developed the novel deep learning model (MA-CNN-BiLSTM), which incorporates Multi-Head Attention mechanism (MA), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory network (BiLSTM) as intricate relationship processor, feature extractor, and regression component, to estimate ET 0 based on radiation-based (R n -based), humidity-based (RH-based), and temperature-based (T-based) input combinations at 600 stations during 1961–2020 throughout China under internal and external cross-validation strategies. Besides, through a comparative evaluation among MA-CNN-BiLSTM, CNN-BiLSTM, BiLSTM, LSTM, Multivariate Adaptive Regression Splines (MARS), and empirical models, the result indicated that MA-CNN-BiLSTM achieved superior precision, with values of Determination Coefficient (R2), Nash–Sutcliffe efficiency coefficient (NSE), Relative Root Mean Square Error (RRMSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) ranging 0.877–0.972, 0.844–0.962, 0.129–0.292, 0.294–0.644 mm d−1, 0.244–0.566 mm d−1 for internal strategy and 0.797–0.927, 0.786–0.920, 0.162–0.335, 0.409–0.969 mm d−1, 0.294–0.699 mm d−1 for external strategy. Specifically, R n -based MA-CNN-BiLSTM excelled in the temperate continental zone (TCZ) and mountain plateau zone (MPZ), while RH-based MA-CNN-BiLSTM yielded best precision in others. Furthermore, the internal strategy was superior to external strategy by 2.74–106.04% for R2, 1.11–120.49% for NSE, 1.41–40.27% for RRMSE, 1.68–45.53% for RMSE, and 1.21–38.87% for MAE, respectively. In summary, the main contribution of the present study is the proposal of a novel LSTM-type ET 0 model (MA-CNN-BiLSTM) to cope with various data-missing scenarios throughout China, which can provide effective support for decision-making in regional agriculture water management. • MA-CNN-BiLSTM model provides the highest accuracy for reference crop evapotranspiration estimation across China. • Machine learning and empirical models performed better with internal strategy than external strategy. • Radiation-based models excelled in temperate continental and mountain plateau zone, while humidity-based excelled in others. • Multi-head attention improved Long Short-Term Memory network type model as intricate relationship processor. [ABSTRACT FROM AUTHOR]

• LSTM-type models outperformed empirical methods and classical machine learning models. • BiLSTM was recommended for SPEI-3, and CNN-LSTM was suitable for SPEI-6 and SPEI-12. • Rn-based models performed best for estimating multiscale SPEI throughout China. • RH-based was superior to T-based ML models, but empirical methods differed with zones. Although the accurate prediction of the Standardized Precipitation Evapotranspiration Index (SPEI) is considered meaningful in reducing drought losses, its wide applications are limited to substantial meteorological data requirements. Considering Long Short-Term Memory network (LSTM) has proved its potential in estimating drought index, the concern is justified regarding the performance of its variants for estimating SPEI using limited meteorological input at the national level. Therefore, this study established the SPEI models using empirical methods, SVM, RNN, LSTM, BiLSTM, and CNN-LSTM, respectively, to cope with different data-missing scenarios. Based on a comprehensive comparison among different methods for multiscale SPEI estimation at four climatic zones of China, the results showed that BiLSTM was the most recommended model for estimating SPEI at 3-month timescale, with R2, NSE, and RMSE ranging 0.916–0.997, 0.907–0.997, and 0.143–0.353, respectively. Whereas CNN-LSTM was more suitable for other timescales, with R2, NSE, and RMSE being 0.904–0.999, 0.858–0.989, and 0.145–0.365 for estimating SPEI at 6-month timescale, respectively, and 0.858–0.998, 0.795–0.991, and 0.081–0.568 for estimating SPEI at 12-month timescale. Generally, the accuracy performance of SPEI methods can be ranked from best to worst as LSTM-type models, SVM, RNN, and empirical methods. The exception was that H-S exceeded RNN3 in TCZ and MPZ by 4.1–30.0 % for R2, 4.1–65.0 % for NSE, and 2.3–19.6 % for RMSE, respectively. Moreover, this study found that the accuracy performance of machine learning models for SPEI estimation got worse with the number of independent variables decreased. Overall, the variants of LSTM exerted excellent performance for multiscale SPEI estimation, which provided the most accurate prediction of meteorological, agroecological, and hydrological droughts throughout China. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]