Summary: A warming climate coupled with reductions in water availability and rising salinity are increasingly affecting rice (Oryza sativa) yields. Elevated temperatures combined with vapour pressure deficit (VPD) rises are causing stomatal closure, further reducing plant productivity and cooling. It is unclear what stomatal size (SS) and stomatal density (SD) will best suit all these environmental extremes. To understand how stomatal differences contribute to rice abiotic stress resilience, we screened the stomatal characteristics of 72 traditionally bred varieties. We found significant variation in SS, SD and calculated anatomical maximal stomatal conductance (g_smax) but did not identify any varieties with SD and g_smax as low as transgenic OsEPF1oe plants. Traditionally bred varieties with high SD and small SS (resulting in higher g_smax) typically had lower biomasses, and these plants were more resilient to drought than low SD and large SS plants, which were physically larger. None of the varieties assessed were as resilient to drought or salinity as low SD OsEPF1oe transgenic plants. High SD and small SS rice displayed faster stomatal closure during increasing temperature and VPD, but photosynthesis and plant cooling were reduced. Compromises will be required when choosing rice SS and SD to tackle multiple future environmental stresses.

Remote sensing-based evapotranspiration (ET) models with various levels of sophistication have emerged recently with the possibilities of user-defined model calibrations. Their application for water resources management and climate studies from regional to global scale has been rapidly increasing, which makes it important to validate field scale ET in a complex crop assemblage before operational use. Based on in situ flux-tower measurements by the eddy-covariance (EC) system, this study tested three single-source energy balance models for estimating daily ET from fennel/maize/ryegrass-clover cropland rotations in a Mediterranean context in southern Italy. The sensitivity of three user-friendly ET models (SEBS, QWaterModel, and METRIC-EEFlux) with reference to the EC system over a center pivot irrigated cropland is discussed in detail. Results in terms of statistical indicators revealed that SEBS and METRIC-EEFlux showed reasonable agreements with measured ET (r² = 0.59_SEBS, RMSE = 0.71 mm day⁻¹; r² = 0.65_METRIC, RMSE = 1.13 mm day⁻¹) in terms of trends and magnitudes. At 30 m spatial resolution, both models were able to capture the in-field variations only during the maize development stage. The presence of spurious scan lines due to sensor defects in Landsat L7 ETM+ can contribute to the qualities of the METRIC-Efflux’s ET product. In our observation, the QWaterModel did not perform well and showed the weakest congruency (r² = 0.08_QWaterModel) with ground-based ET estimates. In a nutshell, the study evaluated these automated remote sensing-based ET estimations and suggested improvements in the context of a generic approach used in their underlying algorithm for robust ET retrievals in rotational cropland ecosystems.

Environmental monitoring using satellite remote sensing is challenging because of data gaps in eddy-covariance (EC)-based in situ flux tower observations. In this study, we obtain the latent heat flux (LE) from an EC station and perform gap filling using two deep learning methods (two-dimensional convolutional neural network (CNN) and long short-term memory (LSTM) neural networks) and two machine learning (ML) models (support vector machine (SVM), and random forest (RF)), and we investigate their accuracies and uncertainties. The average model performance based on ~25 input and hysteresis combinations show that the mean absolute error is in an acceptable range (34.9 to 38.5 Wm⁻²), which indicates a marginal difference among the performances of the four models. In fact, the model performance is ranked in the following order: SVM > CNN > RF > LSTM. We conduct a robust analysis of variance and post-hoc tests, which yielded statistically insignificant results (p-value ranging from 0.28 to 0.76). This indicates that the distribution of means is equal within groups and among pairs, thereby implying similar performances among the four models. The time-series analysis and Taylor diagram indicate that the improved two-dimensional CNN captures the temporal trend of LE the best, i.e., with a Pearson’s correlation of >0.87 and a normalized standard deviation of ~0.86, which are similar to those of in situ datasets, thereby demonstrating its superiority over other models. The factor elimination analysis reveals that the CNN performs better when specific meteorological factors are removed from the training stage. Additionally, a strong coupling between the hysteresis time factor and the accuracy of the ML models is observed.

As the global climate continues to change, plants will increasingly experience abiotic stress(es). Stomata on leaf surfaces are the gatekeepers to plant interiors, regulating gaseous exchanges that are crucial for both photosynthesis and outward water release. To optimise future crop productivity, accurate modelling of how stomata govern plant–environment interactions will be crucial. Here, we synergise optical and thermal imaging data to improve modelled transpiration estimates during water and/or nutrient stress (where leaf N is reduced). By utilising hyperspectral data and partial least squares regression analysis of six plant traits and fluxes in wheat (Triticum aestivum), we develop a new spectral vegetation index; the Combined Nitrogen and Drought Index (CNDI), which can be used to detect both water stress and/or nitrogen deficiency. Upon full stomatal closure during drought, CNDI shows a strong relationship with leaf water content (r² = 0.70), with confounding changes in leaf biochemistry. By incorporating CNDI transformed with a sigmoid function into thermal‐based transpiration modelling, we have increased the accuracy of modelling water fluxes during abiotic stress. These findings demonstrate the potential of using combined optical and thermal remote sensing‐based modelling approaches to dynamically model water fluxes to improve both agricultural water usage and yields.

Automated detection of the content of images remains a challenging problem in artificial intelligence. Hence, continuous manual monitoring of restricted development zones is critical to maintaining territorial integrity and national security. In this regard, local governments of the Republic of Korea conduct four periodic inspections per year to preserve national territories from illegal encroachments and unauthorized developments in restricted zones. The considerable expense makes responding to illegal developments difficult for local governments. To address this challenge, we propose a deep-learning-based Cascade Mask region-based convolutional neural network (R-CNN) algorithm designed to perform automated detection of greenhouses in aerial photographs for efficient and continuous monitoring of restricted development zones in the Republic of Korea. Our proposed model is regional-based because it was optimized for the Republic of Korea via transfer learning and hyperparameter tuning, which improved the efficiency of the automated detection of greenhouse facilities. The experimental results demonstrated that the mAP value of the proposed Cascade Mask R-CNN model was 83.6, which was 12.83 higher than baseline mask R-CNN, and 0.9 higher than Mask R-CNN with hyperparameter tuning and transfer learning considered. Similarly, the F1-score of the proposed Cascade Mask R-CNN model was 62.07, which outperformed those of the baseline mask R-CNN and the Mask R-CNN with hyperparameter tuning and transfer learning considered (i.e., the F1-score 52.33 and 59.13, respectively). The proposed improved Cascade Mask R-CNN model is expected to facilitate efficient and continuous monitoring of restricted development zones through routine screening procedures. Moreover, this work provides a baseline for developing an integrated management system for national-scale land-use planning and development infrastructure by synergizing geographical information systems, remote sensing, and deep learning models.

Summary: As global temperatures and the severity of droughts continue to increase, food crops will more frequently experience high vapour pressure deficit (VPD) during heatwave events. However, the interactive effects of rising atmospheric CO ₂ and high‐VPD heatwaves on crop water fluxes and yields are currently unknown. We investigate stomatal, photosynthetic and productivity changes in wheat during simulated future high‐VPD heatwaves, under ambient (450 ppm) or elevated (720 ppm) CO ₂ concentrations, across four N‐fertiliser treatments. We measured the physiological response of abaxial and adaxial leaf surfaces to elevated CO ₂ concentration and/or high‐VPD heatwave exposure, and quantified drought responses, seasonal water usage and ear weight. Transpiration ( E ) and stomatal conductance ( g _sw ) increased during high‐VPD heatwaves (irrespective of CO ₂ concentration or N‐fertiliser), largely due to increased water fluxes from abaxial leaf surfaces. Higher E and water usage increased wheat vulnerability to drought and led to reduced total ear weight. High‐VPD heatwaves also hindered stomatal responses to light, with g _sw only reducing by 37–38% after 1 h of dark treatment. Our results show that wheat stomata are inhibited from closing under future high CO ₂ , high‐VPD heatwave conditions. This has considerable implications for future wheat water requirements, which in‐turn could significantly impact drought susceptibility and yield potential.