Evapotranspiration (ET) represents a significant component of the global water flux cycle, yet nocturnal evapotranspiration (ETn) is often neglected, leading to underestimation of global evapotranspiration. As for cropland, accurate modeling of ETn is essential for rational water management and is important for sustainable agriculture development. We used random forest (RF) to simulate ETn at 16 globally distributed cropland eddy covariance flux sites along with remote sensing and meteorological factors. The recursive feature elimination method was used to remove unimportant variables. We also simulated the ETn of C₃ and C₄ crops separately. The trained RF resulted in a determination coefficient (R²) (root mean square error (RMSE)) of 0.82 (7.30 W m⁻²) on the testing dataset. C₃ and C₄ crops on the testing dataset resulted in an R² (RMSE) of 0.86 (5.59 W m⁻²) and 0.55 (4.86 W m⁻²) for the two types of crops. We also showed that net radiation is the dominant factor in regulating ETn, followed by 2 m horizontal wind speed and vapor pressure deficit (VPD), and these three meteorological factors showed a significant positive correlation with ETn. This research demonstrates that RF can simulate ETn from crops economically and accurately, providing a methodological basis for improving global ETn simulations.

Accurately predicting actual evapotranspiration (${\mathrm{ET}}_{\mathrm{a}}$) at the regional scale is crucial for efficient water resource allocation and management. While previous studies mainly focused on predicting site-scale ${\mathrm{ET}}_{\mathrm{a}}$, in-depth studies on regional-scale ${\mathrm{ET}}_{\mathrm{a}}$ are relatively scarce. This study aims to address this issue by proposing a MulSA-ConvLSTM model, which combines the multi-headed self-attention module with the Pyramidally Attended Feature Extraction (PAFE) method. By extracting feature information and spatial dependencies in various dimensions and scales, the model utilizes remote sensing data from ERA5-Land and TerraClimate to attain regional-scale ${\mathrm{ET}}_{\mathrm{a}}$ prediction in Shandong, China. The MulSA-ConvLSTM model enhances the efficiency of capturing the trend of ${\mathrm{ET}}_{\mathrm{a}}$ successfully, and the prediction results are more accurate than those of the other contrast models. The Pearson’s correlation coefficient between observed and predicted values reaches 0.908. The study has demonstrated that MulSA-ConvLSTM yields superior performance in forecasting various ${\mathrm{ET}}_{\mathrm{a}}$ scenarios and is more responsive to climatic changes than other contrast models. By using a convolutional network feature extraction method, the PAFE method extracts global features via various convolutional kernels. The customized MulSAM module allows the model to concentrate on data from distinct subspaces, focusing on feature changes in multiple directions. The block-based training method is employed for the large-scale regional ${\mathrm{ET}}_{\mathrm{a}}$ prediction, proving to be effective in mitigating the constraints posed by limited hardware resources. This research provides a novel and effective method for accurately predicting regional-scale ${\mathrm{ET}}_{\mathrm{a}}$.