The surface energy balance algorithm for land (SEBAL) is a commonly used method for estimating evapotranspiration (ET) at a regional scale; however, the cloudy and rainy characteristics of subtropical monsoon regions pose a greater challenge for estimating paddy field ET based on remote sensing technology. To this end, a typical subtropical climate region in southern China (Ganfu Plain irrigation system) was selected as the study area. Subsequently, we evaluated the applicability of the SEBAL model for estimating the ET of paddy fields at the daily scale; derived the interannual variation (2000–2017) characteristics of early, middle, and late rice ET; and finally analyzed the spatial distribution patterns of rice in different hydrological years. The results demonstrated that: (1) the SEBAL model estimated ET accurately on a daily scale, with R², NSE, and RMSE values of 0.85, 0.81, and 0.84 mm/day, respectively; (2) the ET of paddy fields in the irrigated area was higher in July and August and the interannual trend of ET of early rice was not obvious, with a declining trend observed in middle rice and late rice from 2000 to 2009, which was followed by an increasing trend from 2009 to 2017; and (3) variations in the spatial distribution of ET were significant for early and late rice at different precipitation levels and less obvious for middle rice in wet years but significant in dry years. Overall, this study verified the applicability of the SEBAL model for estimating ET in paddy fields in subtropical regions and provided a basis and reference for the rational allocation of water resources at a regional scale.

The accurate identification of the water layer condition of paddy fields is a prerequisite for precise water management of paddy fields, which is important for the water-saving irrigation of rice. Until now, the study of unmanned aerial vehicle (UAV) remote sensing data to monitor the moisture condition of field crops has mostly focused on dry crops, and research on the water status of paddy fields has been relatively limited. In this study, visible and thermal infrared images of paddy fields at key growth stages were acquired using a UAV remote sensing platform, and three model input variables were constructed by extracting the color features and temperature features of each field, while K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and logistic regression (LR) analysis methods were applied to establish a model for identifying the water layer presence in paddy fields. The results showed that KNN, SVM, and RF performed well in recognizing the presence of water layers in paddy fields; KNN had the best recognition accuracy (89.29%) via algorithm comparison and parameter preference. In terms of model input variables, using multisource remote sensing data led to better results than using thermal or visible images alone, and thermal data was more effective than visible data for identifying the water layer status of rice fields. This study provides a new paradigm for monitoring the water status of rice fields, which will be key to the precision irrigation of paddy fields in large regions in the future.

The timely identification of key rice growth stages supports water management, crop monitoring, and yield estimation. This study evaluated whether single-date unmanned aerial vehicle (UAV) RGB imagery can identify early tillering, booting, heading, and milk-ripe rice without a continuous image time series. UAV imagery and synchronous field observations were collected from 64 fields in the Zhanghe Irrigation District, China, during 2022–2023. Four visible-light indices and 12 gray-level co-occurrence matrix (GLCM) texture features were used with k-nearest neighbors (KNN), a support vector machine (SVM), random forest (RF), a gradient boosting decision tree (GBDT), and corresponding stacking ensembles. Stacking-RF achieved the highest test performance, with 91.7% accuracy and a macro-F1 of 0.917, exceeding KNN, the best individual model, by 6.8 percentage points and 0.067, respectively. Early tillering and milk-ripe were identified more accurately than booting and heading. Visible-light indices supplied most of the discriminative information, while texture features contributed complementary canopy-structure information. The results show that an RGB-only, single-date input can support a rapid within-site growth-stage assessment. However, the sample-level split, single study area, and absence of radiometric calibration limit inference to new fields, years, and regions.