Hydrological impacts of future climate and land use/cover changes in the Lower Mekong Basin: a case study of the Srepok River Basin, Vietnam.

This study presents hydrological impacts of future climate change (CC) and land use/cover change (LUCC) for the Srepok River Basin (SRB) in the Vietnam's Central Highlands. The hydrology cycle of this basin was reproduced using Soil and Water Assessment Tool (SWAT) allowing an evaluation of hydrological responses to CC and LUCC. Future climate scenarios of the 2015-2100 period under Representative Concentration Pathways (RCP) 4.5 simulated by five General Circulation Models (GCMs) and LUCC scenario in 2050 were developed. Compared to the reference scenario (1980-2005), future LUCC increases the streamflow (0.25%) and surface runoff (1.2%) and reduces the groundwater discharge (2.1%). Climate change may cause upward trends in streamflow (0.1 to 2.7%), surface runoff (0.4 to 4.3%), and evapotranspiration (0.8 to 3%), and a change in the groundwater discharge (- 1.7 to 0.1%). The combination of CC and LUCC increases the streamflow (0.2 to 2.8%), surface runoff (1.6 to 5.6%), and evapotranspiration (1.0 to 3.1%), and reduces the groundwater discharge (1.5 to 2.7%) with respect to the reference scenario. Moreover, the results noted that the water scarcity may happen in the dry-seasonal months.

A time-continuous land surface temperature (LST) data fusion approach based on deep learning with microwave remote sensing and high-density ground truth observations.

Land surface temperature (LST) is a crucial parameter in the circulation of water, exchange of land-atmosphere energy, and turbulence. Currently, most LST products rely heavily on thermal infrared remote sensing, which is susceptible to cloud and rain interference, leading to inferior temporal continuity. Microwave remote sensing has the advantage of being available "all-weather" due to strong penetration capability, which provides the possibility to simulate time-continuous LST data. In addition, the continuous increase in high-density station observations (>10,000 stations) provides reliable measured data for the remote sensing monitoring of LST in China. This study aims to adopt the "Earth big data" generated from high-density station observation and microwave remote sensing data to monitor LST based on deep learning (U-Net family) for the first time. Given the significant spatial and temporal variability of LST and its sensitivity to various factors according to radiation transmission equations, this study incorporated climatic, anthropogenic, geographical, and vegetation datasets to facilitate a multi-source data fusion approach for LST estimation. The results showed that the U-Net++ model with modified skip connections better minimized the semantic discrepancy between the feature maps of the encoder and decoder subnetworks for 0.1° daily LST mapping across China than the U-Net and U2-Net deep learning models. The accuracy of the LST simulation exhibited favorable outcomes in the spatial and temporal dimensions. The station density met the requirements of monitoring air-ground integration monitoring in China. Additionally, the temporal change in the simulation accuracy fluctuated in a W-shape owing to the limited simulation capability of deep learning in extreme scenarios. Anthropogenic factors had the largest influence on LST changes in China, followed by climate, geography, and vegetation. This study highlighted the application of deep learning in remote sensing monitoring against the background of "big data" and provided a scientific foundation for the response of climate change to human activities, ecological environmental protection, and sustainable social and economic development.

Generalized deep iterative reconstruction for sparse-view CT imaging.

Sparse-view CT is a promising approach for reducing the x-ray radiation dose in clinical CT imaging. However, the CT images reconstructed from the conventional filtered backprojection algorithm suffer from severe streaking artifacts. Iterative reconstruction algorithms have been widely adopted to mitigate these streaking artifacts, but they may prolong the CT imaging time due to the intense data-specific computations. Recently, a model-driven deep learning CT image reconstruction method, which unrolls the iterative optimization procedures into a deep neural network, has shown exciting prospects for improving image quality and shortening the reconstruction time. In this work, we explore a generalized unrolling scheme for such an iterative model to further enhance its performance on sparse-view CT imaging. By using it, the iteration parameters, regularizer term, data-fidelity term and even the mathematical operations are all assumed to be learned and optimized via network training. Results from the numerical and experimental sparse-view CT imaging demonstrate that the newly proposed network with the maximum generalization provides the best reconstruction performance.

The effects of climate variability and land-use change on streamflow and nutrient loadings in the Sesan, Sekong, and Srepok (3S) River Basin of the Lower Mekong Basin.

This paper aimed at examining the climate variability and land-use change effects on streamflow and pollutant loadings, namely total suspended sediment (TSS), total nitrogen (T-N), and total phosphorus (T-P), in the Sesan, Sekong, and Srepok (3S) River Basin in the period 1981-2010. The well-calibrated and validated Soil and Water Assessment Tool (SWAT) was used for this purpose. Compared to the reference period, climate variability was found to be responsible to a 1.00% increase in streamflow, 2.91% increase in TSS loading, 11.35% increase in T-N loading, and 19.12% reduction in T-P loading for the whole basin. With regard to the effect of land-use change (LUC), streamflow, TSS, T-N, and T-P loadings increased by 0.01%, 3.70%, 10.12%, and 10.94%, respectively. Therefore, the combination of climate variability and LUC showed amplified increases in streamflow (1.03%), TSS loading (7.09%), and T-N loading (25.05%), and a net effect of decreased T-P loading (10.35%). Regarding the Sekong and Srepok River Basins, the streamflow, TSS, T-N and T-P showed stronger responses to climate variability compared to LUC. In case of the Sesan River Basin, LUC had an effect on water quantity and quality more strongly than the climate variability. In general, the findings of this work play an essential role in providing scientific information to effectively support decision makers in developing sustainable water resources management strategies in the study area.

DIRECT-Net: A unified mutual-domain material decomposition network for quantitative dual-energy CT imaging.

PURPOSE: The purpose of this paper is to present an end-to-end deep convolutional neural network to improve the dual-energy CT (DECT) material decomposition performance. METHODS: In this study, we proposes a unified mutual-domain (sinogram domain and CT domain) material decomposition network (DIRECT-Net) for DECT imaging. By design, the DIRECT-Net has immediate access to mutual-domain data, and utilizes stacked convolution neural network layers for noise reduction and material decomposition. The training data are numerically generated following the fundamental DECT imaging physics. Numerical simulation of the XCAT digital phantom, experiments of a biological specimen, a calcium chloride phantom and an iodine solution phantom are carried out to evaluate the performance of DIRECT-Net. Comparisons are performed with different DECT decomposition algorithms. RESULTS: Results demonstrate that the proposed DIRECT-Net can generate water and bone basis images with less artifacts compared to the other decomposition methods. Additionally, the quantification errors of the calcium chloride (75-375 mg/cm3 ) and the iodine (2-20 mg/cm3 ) are less than 4%. CONCLUSIONS: An end-to-end material decomposition network is proposed for quantitative DECT imaging. The qualitative and quantitative results demonstrate that this new DIRECT-Net has promising benefits in improving the DECT image quality.

Performance Limit of Ultrathin GaAs Transistors.

High-electron-mobility group III-V compounds have been regarded as a promising successor to silicon in next-generation field-effect transistors (FETs). Gallium arsenide (GaAs) is an outstanding member of the III-V family due to its advantage of both good n- and p-type device performance. Monolayer (ML) GaAs is the limit form of ultrathin GaAs. Here, a hydrogenated ML GaAs (GaAsH2) FET is simulated by ab initio quantum-transport methods. The n- and p-type ML GaAsH2 metal-oxide-semiconductor FETs (MOSFETs) can well satisfy the on-state current, delay time, power dissipation, and energy-delay product requirements of the International Technology Roadmap for Semiconductors until the gate length is scaled down to 3/4 and 3/5 nm for the high-performance/low-power applications, respectively. Therefore, ultrathin GaAs is a prominent channel candidate for devices in the post-Moore era. The p-type ML GaAsH2 MOSFETs with a 2% uniaxially compressive strain and the unstrained n-type counterparts have symmetrical performance for the high-performance application, making ultrathin GaAs applicable for complementary MOS integrated circuits.

[Impact of moss soil crust on vegetation indexes interpretation].

Vegetation indexes were the most common and the most important parameters to characterizing large-scale terrestrial ecosystems. It is vital to get precise vegetation indexes for running land surface process models and computation of NPP change, moisture and heat fluxes over surface. Biological soil crusts (BSC) are widely distributed in arid and semi-arid, polar and sub-polar regions. The spectral characteristics of dry and wet BSCs were quite different, which could produce much higher vegetation indexes value for the wet BSC than for the dry BSC as reported. But no research was reported about whether the BSC would impact on regional vegetation indexes and how much dry and wet BSC had impact on regional vegetation indexes. In the present paper, the most common vegetation index NDVI were used to analyze how the moss soil crusts (MSC) dry and wet changes affect regional NDVI values. It was showed that 100% coverage of the wet MSC have a much higher NDVI value (0.657) than the dry MSC NDVI value (0.320), with increased 0.337. Dry and wet MSC NDVI value reached significant difference between the levels of 0.000. In the study area, MSC, which had the average coverage of 12.25%, would have a great contribution to the composition of vegetation index. Linear mixed model was employed to analyze how the NDVI would change in regional scale as wet MSC become dry MSC inversion. The impact of wet moss crust than the dry moss crust in the study area can make the regional NDVI increasing by 0.04 (14.3%). Due to the MSC existence and rainfall variation in arid and semi-arid zones, it was bound to result in NDVI change instability in a short time in the region. For the wet MSC's spectral reflectance curve is similar to those of the higher plants, misinterpretation of the vegetation dynamics could be more severe due to the "maximum value composite" (MVC) technique used to compose the global vegetation maps in the study of vegetation dynamics. The researches would be useful for detecting and mapping MSC from remote sensing imagery. It also is to the advantage to employing vegetation index wisely.

A Practical Predictive Model Based on Ultrasound Imaging and Clinical Indices for Estimation of Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer.

PURPOSE: Clinical responses of neoadjuvant chemotherapy (NACT) are associated with prognosis in patients with breast cancer. The selection of suitable variables for the prediction of clinical responses remains controversial. Herein, we developed a predictive model based on ultrasound imaging and clinical indices to identify patients most likely to benefit from NACT. PATIENTS AND METHODS: We recruited a total of 225 consecutive patients who underwent NACT followed by surgery and axillary lymph node dissection at the Sixth Hospital of Ning Bo City of Zhe Jiang Province between January 1, 2018, and March 31, 2021. All patients had been diagnosed with breast cancer following the clinical examination. First, we created a training cohort of patients who underwent NACT+surgery (N=180) to develop a nomogram. We then validated the performance of the nomogram in a validation cohort of patients who underwent NACT+ surgery (N=45). Multivariate logistic regression was then used to identify independent risk factors that were associated with the response to NACT; these were then incorporated into the nomogram. RESULTS: Multivariate logistic regression analysis identified several significant differences as to clinical responses of NACT, including neutrophil-lymphocyte ratio (NLR), body mass index (BMI), pulsatility index (PI), resistance index (RI), blood flow, Ki67, histological type, molecular subtyping, and tumor size. The performance of the nomogram score exhibited a robust C-index of 0.89 (95% confidence interval [CI]: 0.83 to 0.95) in the training cohort and a high C-index of 0.87 (95% CI: 0.81 to 0.93) in the validation cohort. Clinical impact curves showed that the nomogram had a good predictive ability. CONCLUSION: We successfully established an accurate and optimized nomogram incorporated ultrasound imaging and clinical indices that could be used preoperatively to predict clinical responses of NACT. This model can be used to evaluate the risk of clinical responses to NACT and therefore facilitate the choice of personalized therapy.

DIR-DBTnet: Deep iterative reconstruction network for three-dimensional digital breast tomosynthesis imaging.

PURPOSE: The goal of this study is to develop a three-dimensional (3D) iterative reconstruction framework based on the deep learning (DL) technique to improve the digital breast tomosynthesis (DBT) imaging performance. METHODS: In this work, the DIR-DBTnet is developed for DBT image reconstruction by mapping the conventional iterative reconstruction (IR) algorithm to the deep neural network. By design, the DIR-DBTnet learns and optimizes the regularizer and the iteration parameters automatically during the network training with a large amount of simulated DBT data. Numerical, experimental, and clinical data are used to evaluate its performance. Quantitative metrics such as the artifact spread function (ASF), breast density, and the signal difference to noise ratio (SDNR) are measured to assess the image quality. RESULTS: Results show that the proposed DIR-DBTnet is able to reduce the in-plane shadow artifacts and the out-of-plane signal leaking artifacts compared to the filtered backprojection (FBP) and the total variation (TV)-based IR methods. Quantitatively, the full width half maximum (FWHM) of the measured ASF from the clinical data is 27.1% and 23.0% smaller than those obtained with the FBP and TV methods, while the SDNR is increased by 194.5% and 21.8%, respectively. In addition, the breast density obtained from the DIR-DBTnet network is more accurate and consistent with the ground truth. CONCLUSIONS: In conclusion, a deep iterative reconstruction network, DIR-DBTnet, has been proposed for 3D DBT image reconstruction. Both qualitative and quantitative analyses of the numerical, experimental, and clinical results demonstrate that the DIR-DBTnet has superior DBT imaging performance than the conventional algorithms.

Risk analysis of maize yield losses in mainland China at the county level.

Food security in China is under additional stress due to climate change. The risk analysis of maize yield losses is crucial for sustainable agricultural production and climate change impact assessment. It is difficult to quantify this risk because of the constraints on the high-resolution data available. Moreover, the current results lack spatial comparability due to the area effect. These challenges were addressed by using long-term county-level maize yield and planting area data from 1981 to 2010. We analyzed the spatial distribution of maize yield loss risks in mainland China. A new comprehensive yield loss risk index was established by combining the reduction rate, coefficient of variation, and probability of yield reduction after removing the area effect. A total of 823 counties were divided into areas of lowest, low, moderate, high, and highest risk. High risk in maize production occurred in Heilongjiang and Jilin Provinces, the eastern part of Inner Mongolia, the eastern part of Gansu-Xinjiang, west of the Loess Plateau, and the western part of the Xinjiang Uygur Autonomous Region. Most counties in Northeast China were at high risk, while the Loess Plateau, middle and lower reaches of the Yangtze River and Gansu-Xinjiang were at low risk.

Could Vegetation Index be Derive from Synthetic Aperture Radar? - The Linear Relationship between Interferometric Coherence and NDVI.

Due to many factors in the physical properties of the ground surface, the corresponding interferometric coherence values change dynamically over time. Among these factors, the roles of the vegetation and its temporal variation have not yet been revealed so far. In this paper, synthetic aperture radar (Sentinel-1) data and optical remote sensing (Landsat TM) images over four whole seasons are employed to reveal the relationship between the interferometric coherence and the normalized difference vegetation index (NDVI) at five sites that have ground deformation due to mining in Henan province, China. The result showed: (1) As for the village area with few vegetation cover, the related coherence values are significantly higher than that in the farm land area with high densities of vegetation in the spring and summer, which indicates that the subsidence by mining in few vegetation cover area is easier to be monitored; (2) Linear regression coefficients ([Formula: see text]) between the interfereometric coherence values and the NDVI values is 0.62, which indicate the interferometric coherence values and the NDVI values change reversely in both farm land and village areas over the year. It suggests months between November and March with lower NDVI value are more suitable for deformation detecting. Therefore, the interfereometric coherence values can be used to detect the density of vegetation, while NDVI values can be reference for elucidating when the traditional differential interferometric synthetic aperture radar (DInSAR) could be effectively used.

Author Correction: Mobile Real-Time Grasshopper Detection and Data Aggregation Framework.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Mobile Real-Time Grasshopper Detection and Data Aggregation Framework.

Insects of the family Orthoptera: Acrididae including grasshoppers and locust devastate crops and eco-systems around the globe. The effective control of these insects requires large numbers of trained extension agents who try to spot concentrations of the insects on the ground so that they can be destroyed before they take flight. This is a challenging and difficult task. No automatic detection system is yet available to increase scouting productivity, data scale and fidelity. Here we demonstrate MAESTRO, a novel grasshopper detection framework that deploys deep learning within RBG images to detect insects. MAESTRO uses a state-of-the-art two-stage training deep learning approach. The framework can be deployed not only on desktop computers but also on edge devices without internet connection such as smartphones. MAESTRO can gather data using cloud storge for further research and in-depth analysis. In addition, we provide a challenging new open dataset (GHCID) of highly variable grasshopper populations imaged in Inner Mongolia. The detection performance of the stationary method and the mobile App are 78 and 49 percent respectively; the stationary method requires around 1000 ms to analyze a single image, whereas the mobile app uses only around 400 ms per image. The algorithms are purely data-driven and can be used for other detection tasks in agriculture (e.g. plant disease detection) and beyond. This system can play a crucial role in the collection and analysis of data to enable more effective control of this critical global pest.

[Progress in spectral characteristics of biological soil crust of arid or semiarid region].

The Biological Soil Crusts (BSC) (also known as organic or microphytic crust) can be formed by different combinations of microphytic communities including mosses, lichens, liverworts, algae, fungi, cyanobacteria (= blue-green algae or Cyanophyta), as well as bacteria. Large areas of sand fields in arid and semi-arid regions are covered by BSC. Remote sensing distinction should be made between physical and biogenical crust formations. It was reviewed the advances of domestic and overseas studies of BSC spectral characteristics, as well as spectral reflectance measurement in situ of our workgruop. When the BSC is wet, it turns green, a notable change in the reflectance curve occurs. The wet BSC's spectral reflectance curve is similar to those of the higher plants and therefore may lead to misinterpretation of the vegetation dynamics and to overestimation of ecosystem productivity. This spectral feature produces a much higher NDVI value for the wet moss BSC than for the dry moss BSC (0.65 vs. 0.30 units, respectively), a higher NDVI value for the wet algae BSC than for the dry algae BSC (0.30 vs. 0.15 units, respectively). The "maximum value composite" (MVC) technique is used to eliminate the effect of clouds and haze from vegetation maps. Misinterpretation of the vegetation dynamics could be more severe due to the MVC technique used to compose the global vegetation maps in the study of vegetation dynamics. But relatively limited research has been conducted to investigate the spectral characteristics of BSC change with different moisture conditions and under different seasons. More research works could be considered in spectral characteristics of BSC. The researches would be useful for detecing and mapping BSC, from remote sensing imagery. It also is to the advantage to employ Vegetation Index wisely.

Long-term growth of temperate broadleaved forests no longer benefits soil C accumulation.

It is widely recognized that the long-term growth of forests benefits biomass carbon (C) sequestration, but it is not known whether the long-term growth of forests would also benefit soil C sequestration. We selected 79 representative soil profiles and investigated the influence of the forest stand age on the soil C dynamics of three soil layers (0-10, 10-20 and 20-30 cm) in temperate broadleaved forests in East China. The results suggest that the soil C density in temperature broadleaved forests significantly changes with the stand age, following a convex parabolic curve. At an early stand age, the soil C density usually increases, reaching its peak value at a pre-mature stand age (approximately 50 years old). At later stand ages, the soil C density usually decreases. Therefore, our results reveal a turning point in the soil C density at a pre-mature stand age. The long-term growth of temperate broadleaved forests after pre-mature stand age no longer benefits soil C accumulation, probably promotes topsoil C loss. In addition, we found that the soil C density in the upper soil layer usually changes with the forest stand development more significantly than that in deeper soil layers.

Elemental compositions of lichens from Duolun County, Inner Mongolia, China: Origin, road effect and species difference.

To assess the response of lichen elemental compositions to road traffic and species difference in the context of high dust input and anthropogenic emissions, two foliose epiphytic lichens (Phaeophyscia hirtuosa, PHh; Candelaria fibrosa, CAf) were sampled near a road adjacent to Dolon Nor Town (Duolun County, Inner Mongolia, China). Twenty elements (Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P, Pb, Sb, Sr, Ti, V and Zn) in lichen and surface soil samples were analysed using inductively coupled plasma mass spectrometer (ICP-MS). The results demonstrate that lichen elemental compositions are highly influenced by both their natural environment and anthropogenic input. Windblown dust associated with sand dunes and degraded/desertified steppes represents the predominant source of lichen elements. Road traffic can enhance the lichen elemental burden by increasing the number of soil particles. Anthropogenic emissions from the town and road traffic have also led to the enrichment of Cd and Zn in lichens. PHh was higher than CAf in concentrations of 14 terrigenous metals. Both lichens are applicable to biomonitoring of atmospheric element deposition and, in most cases, yield comparable results.

Use of the lichen Xanthoria mandschurica in monitoring atmospheric elemental deposition in the Taihang Mountains, Hebei, China.

Air pollution is a major concern in China. Lichens are a useful biomonitor for atmospheric elemental deposition but have rarely been used in North China. The aim of this study was to investigate the atmospheric depositions of 30 trace elements (Al, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, K, La, Mg, Mn, Mo, Na, Ni, P, Pb, Rb, Sb, Sc, Sm, Sr, Tb, Th, Ti, Tl, V and Zn) in a region of the Taihang Mountains, Hebei Province, China using lichens as biomonitors. Epilithic foliose lichen Xanthoria mandschurica was sampled from 21 sites and analyzed using inductively coupled plasma mass spectrometry (ICP-MS). The results show that 1) eight elements (Cd, Cr, Cu, Mo, P, Pb, Sb and Zn) are of atmospheric origin and are highly influenced by the atmospheric transportation from the North China Plain, as well as local mining activities, while 2) the remaining 22 elements are primarily of crustal origin, the concentration of which has been enhanced by local mining and quarrying activities. These results clearly validate the applicability of lichens in biomonitoring of atmospheric elemental deposition and demonstrate the spatial pattern for air pollution in the region.

Lichen elemental composition distinguishes anthropogenic emissions from dust storm inputs and differs among species: Evidence from Xilinhot, Inner Mongolia, China.

To test the applicability of lichens in the biomonitoring of atmospheric elemental deposition in a typical steppe zone of Inner Mongolia, China, six foliose lichens (Physcia aipolia, PA; P. tribacia, PT; Xanthoria elegans, XE; X. mandschurica, XM; Xanthoparmelia camtschadalis, XPC; and Xp. tinctina, XPT) were sampled from the Xilin River Basin, Xilinhot, Inner Mongolia, China. Twenty-five elements (Al, Ba, Cd, Ce, Cr, Cs, Cu, Fe, K, La, Mn, Mo, Na, Ni, P, Pb, Sb, Sc, Sm, Tb, Th, Ti, Tl, V and Zn) in the lichens were analysed using inductively coupled plasma mass spectrometry (ICP-MS). The results show that Cd, Pb and Zn were mainly atmospheric in origin, whereas the other elements were predominantly of crustal origin. Compared with other studies, our data were higher in crustal element concentrations and lower in atmospheric element concentrations, matching with the frequent, severe dust storms and road traffic in the area. The elemental concentrations in lichens are both species- and element-specific, highlighting the importance of species selection for biomonitoring air pollution using lichens. We recommend PT, XE, XM and XPT for monitoring atmospheric deposition of crustal elements; XPC and XPT for Cd and Pb; PA for Cd and Zn; and PT for Cd.

[Effects of future climate change on climatic suitability of rubber plantation in China].

Global warming may seriously affect the climatic suitability distribution of rubber plantation in China. Five main climate factors affecting rubber planting were mean temperature of the coldest month, mean extremely minimum temperature, the number of monthly, mean temperature ≥18 °C, annual mean temperature and annual mean precipitation. Climatic suitability areas of rubber plantation in 1981-2010, 2041-2060, 2061-2080 were analyzed by the maximum entropy model based on the five main climate factors and the climate data of 1981-2010 and RCP4.5 scenario data. The results showed that under the background of the future climate change, the climatic suitability area of rubber plantation would have a trend of expansion to the north in 2041-2060, 2061-2080. The climatic suitability areas of rubber plantation in 2041-2060 and 2061-2080 increased more obviously than in 1981-2010. The suitable area and optimum area would increase, while the less suitable area would decrease. The climatic suitability might change in some areas, such as the total suitable area would decrease in Yunnan Province, and the suitability grade in both Jinghong and Mengna would change from optimum area to suitable area. However, the optimum area of rubber plantation would increase significantly in Hainan Island and Leizhou Peninsula of Guangdong Province, and a new less suitable area of rubber planting would appear in Taiwan Island due to the climate change.

Infrared warming reduced winter wheat yields and some physiological parameters, which were mitigated by irrigation and worsened by delayed sowing.

Winter wheat has a central role in ensuring the food security and welfare of 1.3 billion people in China. Extensive previous studies have concluded that winter wheat yields would decrease with higher temperatures, owing to warming-induced soil drying or shortening of phenophase. Temperature in China is predicted to increase by 1-5°C by 2100, which may greatly impact plant production and cause other negative effects. We performed a manipulative field experiment, creating diverse growth regimes for wheat by infrared radiation (IR) warming day and night, including IR warming only (DW), IR warming + delayed sowing dates (DS), IR warming + increased irrigation (IW), and a control (CK). The results show that IR warming increased daily average wheat canopy and soil temperatures by 2.0°C and 2.3°C, respectively. DW was associated with an advanced maturity of 10 days and yield reduction of 8.2%. IR-warming effects on the photosynthetic apparatus of wheat varied with season as well as significant differences were found in the booting stage. DS represented a worsened situation, lowering yield per plant by 16.4%, with a significant decline in aboveground biomass and functional leaf area. Wheat under DS showed double-peak patterns of diurnal gas exchange during booting stages and, consequently, lower photosynthetic capacity with high transpiration for cooling. Significantly lower actual water use efficiency and intrinsic water use efficiency from jointing to anthesis stages were also found under DS. However, IW had no significant difference from CK, irrespective of yield and photosynthesis. Therefore, we concluded that delayed sowing date may not be a good choice for winter wheat, whereas a thoroughly-watered wheat agroecosystem should be promoted in the context of global warming.