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Water can be redistributed through, in physical terms, water transfer projects and virtually, embodied water for the production of traded products. Here, we explore whether such water redistributions can help mitigate water stress in China. This study, for the first time to our knowledge, both compiles a full inventory for physical water transfers at a provincial level and maps virtual water flows between Chinese provinces in 2007 and 2030. Our results show that, at the national level, physical water flows because of the major water transfer projects amounted to 4.5% of national water supply, whereas virtual water flows accounted for 35% (varies between 11% and 65% at the provincial level) in 2007. Furthermore, our analysis shows that both physical and virtual water flows do not play a major role in mitigating water stress in the water-receiving regions but exacerbate water stress for the water-exporting regions of China. Future water stress in the main water-exporting provinces is likely to increase further based on our analysis of the historical trajectory of the major governing socioeconomic and technical factors and the full implementation of policy initiatives relating to water use and economic development. Improving water use efficiency is key to mitigating water stress, but the efficiency gains will be largely offset by the water demand increase caused by continued economic development. We conclude that much greater attention needs to be paid to water demand management rather than the current focus on supply-oriented management. Significance Freshwater resources are unevenly distributed in China. This situation drives a significant amount of water flow both physically and virtually across China. Here, we report on our quantification of China’s physical and virtual water flows and associated water stress at the provincial level. In 2007, interprovincial physical water flows amounted to only a small part of China’s total water supply, but virtual water flows amounted to over one-third of supply. We found that both physical and virtual water flows exacerbated water stress for the main water-exporting provinces. The results highlight the need for more emphasis to be placed on water demand management rather than the current focus on supply-oriented management.

Stomatal behavior plays a critical role in determining the vegetation water‐carbon cycles under climate change. While research on the response of leaf stomata to environmental stresses has become increasingly prevalent, understanding of the variability of stomatal behavior in climate‐sensitive and ecologically fragile regions remains limited. We selected typical ecosystems of the inland Heihe River Basin in China, compiled data on water‐carbon fluxes, meteorological factors, and soil water content across ecosystems and environmental gradients, quantified thresholds of atmospheric (AS), soil (SS), and compound water stresses (CS) that suppress ecosystem evapotranspiration, then estimated the canopy stomatal conductance (Gc) by eliminating non‐vegetation information from the surface conductance, finally analyzed differences in water‐stressed Gc and quantified the sensitivity of Gc to environmental and biological factors using the machine learning approach. We found that water stress thresholds varied along with environmental conditions, and these thresholds were correlated with stomatal parameters in the Medlyn equation (R2 ≥ 0.30). The mean Gc was minimal under CS, and the mean Gc under AS was lower than that under SS. The sensitivity of Gc to influencing factors under SS was greater than under other water stresses, and the effects of soil water on Gc were uniquely negative in riparian ecosystems. By emphasizing the complexity and variability of canopy stomatal behaviors under water stresses within the basin, our study advances the understanding of plant physiological responses to environmental stresses and informs the enhancement of stomatal conductance parameterizations in Earth system models.

Currently, the world is facing high competition and market risks in improving yield, crop illness, and crop water stress. This could potentially be addressed by technological advancements in the form of precision systems, improvements in production, and through ensuring the sustainability of development. In this context, remote-sensing systems are fully equipped to address the complex and technical assessment of crop production, security, and crop water stress in an easy and efficient way. They provide simple and timely solutions for a diverse set of ecological zones. This critical review highlights novel methods for evaluating crop water stress and its correlation with certain measurable parameters, investigated using remote-sensing systems. Through an examination of previous literature, technologies, and data, we review the application of remote-sensing systems in the analysis of crop water stress. Initially, the study presents the relationship of relative water content (RWC) with equivalent water thickness (EWT) and soil moisture crop water stress. Evapotranspiration and sun-induced chlorophyll fluorescence are then analyzed in relation to crop water stress using remote sensing. Finally, the study presents various remote-sensing technologies used to detect crop water stress, including optical sensing systems, thermometric sensing systems, land-surface temperature-sensing systems, multispectral (spaceborne and airborne) sensing systems, hyperspectral sensing systems, and the LiDAR sensing system. The study also presents the future prospects of remote-sensing systems in analyzing crop water stress and how they could be further improved.

High-resolution airborne thermal infrared (TIR) together with sun-induced fluorescence (SIF) and hyperspectral optical images (visible, near- and shortwave infrared; VNIR/SWIR) were jointly acquired over an experimental site. The objective of this study was to evaluate the potential of these state-of-the-art remote sensing techniques for detecting symptoms similar to those occurring during water stress (hereinafter referred to as ‘water stress symptoms’) at airborne level. Flights with two camera systems (Telops Hyper-Cam LW, Specim HyPlant) took place during 11th and 12th June 2014 in Latisana, Italy over a commercial grass (Festuca arundinacea and Poa pratense) farm with plots that were treated with an anti-transpirant agent (Vapor Gard®; VG) and a highly reflective powder (kaolin; KA). Both agents affect energy balance of the vegetation by reducing transpiration and thus reducing latent heat dissipation (VG) and by increasing albedo, i.e., decreasing energy absorption (KA). Concurrent in situ meteorological data from an on-site weather station, surface temperature and chamber flux measurements were obtained. Image data were processed to orthorectified maps of TIR indices (surface temperature (Ts), Crop Water Stress Index (CWSI)), SIF indices (F687, F780) and VNIR/SWIR indices (photochemical reflectance index (PRI), normalised difference vegetation index (NDVI), moisture stress index (MSI), etc.). A linear mixed effects model that respects the nested structure of the experimental setup was employed to analyse treatment effects on the remote sensing parameters. Airborne Ts were in good agreement (∆T < 0.35 K) compared to in situ Ts measurements. Maps and boxplots of TIR-based indices show diurnal changes: Ts was lowest in the early morning, increased by 6 K up to late morning as a consequence of increasing net radiation and air temperature (Tair) and remained stable towards noon due to the compensatory cooling effect of increased plant transpiration; this was also confirmed by the chamber measurements. In the early morning, VG treated plots revealed significantly higher Ts compared to control (CR) plots (p = 0.01), while SIF indices showed no significant difference (p = 1.00) at any of the overpasses. A comparative assessment of the spectral domains regarding their capabilities for water stress detection was limited due to: (i) synchronously overpasses of the two airborne sensors were not feasible, and (ii) instead of a real water stress occurrence only water stress symptoms were simulated by the chemical agents. Nevertheless, the results of the study show that the polymer di-1-p-menthene had an anti-transpiring effect on the plant while photosynthetic efficiency of light reactions remained unaffected. VNIR/SWIR indices as well as SIF indices were highly sensitive to KA, because of an overall increase in spectral reflectance and thus a reduced absorbed energy. On the contrary, the TIR domain was highly sensitive to subtle changes in the temperature regime as induced by VG and KA, whereas VNIR/SWIR and SIF domain were less affected by VG treatment. The benefit of a multi-sensor approach is not only to provide useful information about actual plant status but also on the causes of biophysical, physiological and photochemical changes.

The remote sensing (RS) technique is less cost- and labour- intensive than ground-based surveys for diverse applications in agriculture. Machine learning (ML), a branch of artificial intelligence (AI), provides an effective approach to construct a model for regression and classification of a multivariate and non-linear system. Without being explicitly programmed, machine learning models learn from training data, i.e., past experience. Machine learning, when applied to remotely sensed data, has the potential to evolve a real-time farm-specific management system to reinforce farmers' ability to make appropriate decisions. Recently, the use of machine learning techniques combined with RS data has reshaped precision agriculture in many ways, such as crop identification, yield prediction and crop water stress assessment, with better accuracy than conventional RS methods. As agriculture accounts for approximately 70% of the worldwide water withdrawals, it must be used in the most efficient way to obtain maximum yields and food production. The use of water management and irrigation based on plant water stress have been demonstrated to not only save water but also increase yield. To date, RS and ML-based results have encouraged farmers and decision-makers to adopt this technology to meet global food demands. This phenomenon has led to the much-needed interest of researchers in using ML to improve agriculture outcomes. However, the use of ML for the potential evaluation of water stress continues to be unexplored and the existing methods can still be greatly improved. This study aims to present an overall review of the widely used methods for crop water stress monitoring using remote sensing and machine learning and focuses on future directions for researchers.

Low-altitude remote sensing (RS) using unmanned aerial vehicles (UAVs) is a powerful tool in precision agriculture (PA). In that context, thermal RS has many potential uses. The surface temperature of plants changes rapidly under stress conditions, which makes thermal RS a useful tool for real-time detection of plant stress conditions. Current applications of UAV thermal RS include monitoring plant water stress, detecting plant diseases, assessing crop yield estimation, and plant phenotyping. However, the correct use and interpretation of thermal data are based on basic knowledge of the nature of thermal radiation. Therefore, aspects that are related to calibration and ground data collection, in which the use of reference panels is highly recommended, as well as data processing, must be carefully considered. This paper aims to review the state of the art of UAV thermal RS in agriculture, outlining an overview of the latest applications and providing a future research outlook.

Metabolic regulation is one of the main mechanisms involved in the maintenance of cell osmotic potential under abiotic stress. To date, metabolite profiling approaches have been extensively used to characterize the molecular responses to abiotic stress in many plant species. However, studies revealing the specific metabolic responses of plants exposed to water-deficit stress remain limited. Here, we review the most recent developments that advance our understanding of the metabolic response to drought stress in Arabidopsis and rice. We provide an updated schematic overview of the specific metabolic signature of wild-type plants in response to drought.

Butterhead lettuce (Lactuca sativa var. capitata) is a nutrient-rich leafy vegetable beneficial for human health. Lettuce growth and yield performance hampered under water stress conditions. This study aimed to assess its growth and recovery under shortterm shallow water conditions in the tropical urban ecosystem. A randomized block design was used with three water table treatments: 16.7 cm, 12.7 cm, and 9.7 cm from the substrate surface. The Results showed that butterhead lettuce is intolerant of excess water, with stunted growth at the 9.7 cm water level, by affecting leaf length, leaf width, leaf initiation, and canopy area. Substrate moisture also indicated excess water at this level. Optimal recovery was observed two weeks after water stress. Leaf length and leaf width were analyzed using zerointercept linear regression and the results were reliable predictors of leaf area (y = 0.6076LLxLW; R² = 0.9694). In conclusion, butterhead lettuce is sensitive to excess water, as shown by morphological changes, and requires two weeks to recover after water stress.

Irrigation is critical to sustain agricultural productivity in dry or semi-dry environments, and center pivots, due to their versatility and ruggedness, are the most widely used irrigation systems. To effectively use center pivot irrigation systems, producers require tools to support their decision-making on when and how much water to irrigate. However, currently producers make these decisions primarily based on experience and/or limited information of weather. Ineffective use of irrigation systems can lead to overuse of water resources, compromise crop productivity, and directly reduce producers’ economic return as well as bring negative impacts on environmental sustainability. In this paper, we surveyed existing precision irrigation research and tools from peer-reviewed literature, land-grant university extension and industry products, and U.S. patents. We focused on four challenge areas related to precision irrigation decision-support systems: (a) data availability and scalability, (b) quantification of plant water stress, (c) model uncertainties and constraints, and (d) producers’ participation and motivation. We then identified opportunities to address the above four challenge areas: (a) increase the use of high spatial-temporal-resolution satellite fusion products and inexpensive sensor networks to scale up the adoption of precision irrigation decision-support systems; (b) use mechanistic quantification of ‘plant water stress’ as triggers to improve irrigation decision, by explicitly considering the interaction between soil water supply, atmospheric water demand, and plant physiological regulation; (c) constrain the process-based and statistical/machine learning models at each individual field using data-model fusion methods for scalable solutions; and (d) develop easy-to-use tools with flexibility, and increase governments’ financial incentives and support. We conclude this review by laying out our vision for precision irrigation decision-support systems for center pivots that can achieve scalable, economical, reliable, and easy-to-use irrigation management for producers.

Mapping maize water stress status and monitoring its spatial variability at a farm scale are a prerequisite for precision irrigation. High-resolution multispectral images acquired from an unmanned aerial vehicle (UAV) were used to evaluate the applicability of the data in mapping water stress status of maize under different levels of deficit irrigation at the late vegetative, reproductive and maturation growth stages. Canopy temperature, field air temperature and relative humidity obtained by a handheld infrared thermometer and a portable air temperature/relative humidity meter were used to establish a crop water stress index (CWSI) empirical model under the weather conditions in Ordos, Inner Mongolia, China. Nine vegetation indices (VIs) related to crop water stress were derived from the UAV multispectral imagery and used to establish CWSI inversion models. The results showed that non-water-stressed baseline had significant difference in the reproductive and maturation stages with an increase of 2.1 °C, however, the non-transpiring baseline did not change significantly with an increase of 0.1 °C. The ratio of transformed chlorophyll absorption in reflectance index (TCARI) and renormalized difference vegetation index (RDVI), and the TCARI and soil-adjusted vegetation index (SAVI) had the best correlations with CWSI. R2 values were 0.47 and 0.50 for TCARI/RDVI and TCARI/SAVI at the reproductive and maturation stages, respectively; and 0.81 and 0.80 for TCARI/RDVI and TCARI/SAVI at the late reproductive and maturation stages, respectively. Compared to CWSI calculated by on-site measurements, CWSI values retrieved by VI-CWSI regression models established in this study had more abilities to assess the field variability of crop and soil. This study demonstrates the potentiality of using high-resolution UAV multispectral imagery to map maize water stress.