In open fields, plants experience dynamic changes in environment, particularly radiation, temperature, wind, and humidity but their short-term responses have not been adequately characterized under natural conditions. In this study, we assessed causal effects of rapid radiation fluctuation on seven plant parameters in open fields of cotton and sweet corn canopies. The parameters are evapotranspiration (ET), canopy photosynthesis indicated by CO2 flux from air to canopy (FCO2), sensible heat energy flux (H), canopy conductance for water vapor (gcw), canopy surface temperature (Ts), shoot and leaf elongation rate, and stem diameter change. The energy and CO2 fluxes were measured with Bowen ratio/energy balance/CO2 gradient (BREB+) technique, using averaging time of 5 min. Shoot + leaf elongation and stem diameter change were monitored with position transducers using averaging time of 1 min. All parameters were all found to respond to change in radiation and transpiration within minutes or sooner. While radiation effects on canopy gas exchanges are expected, illuminating are the indirect but immediate effects on shoot + leaf growth and stem diameter change through radiation effects on transpiration and plant water status. A novel finding is that gcw also responded within minutes or sooner to radiation fluctuations and that FCO2 was related almost linearly to gcw. Results are discussed in terms of soil-plant-atmosphere continuum, and interpreted in terms of dynamic interactions between transpiration and plant water status. The clear inverse relationship between ET and elongation rate or stem diameter changes provides additional evidence supporting the validity of 5-min averaging for the BREB + technique.

The infestation of pine shoot beetles (Tomicus spp.) in the forests of Southwestern China has inflicted serious ecological damages to the environment, causing significant economic losses. Therefore, accurate and practical approaches to detect pest infestation have become an urgent necessity to mitigate these harmful consequences. In this study, we explored the efficiency of thermal infrared (TIR) technology in capturing changes in canopy surface temperature (CST) and monitoring forest health at the scale of individual tree crowns. We combined data collected from TIR imagery and light detection and ranging (LiDAR) using unmanned airborne vehicles (UAVs) to estimate the shoot damage ratio (SDR), which is a representative parameter of the damage degree caused by forest infestation. We compared multiple machine learning methods for data analysis, including random forest (RF), partial least squares regression (PLSR), and support vector machine (SVM), to determine the optimal regression model for assessing SDR at the crown scale. Our findings showed that a combination of LiDAR metrics and CST presents the highest accuracy in estimating SDR using the RF model (R2 = 0.7914, RMSE = 15.5685). Our method enables the accurate remote monitoring of forest health and is expected to provide a novel approach for controlling pest infestation, minimizing the associated damages caused.

Crown die-back of peri-urban forests after combined heatwave and drought was species-specific, size-dependent, and also related to tree neighbourhood characteristics

A thorough exploration of the micrometeorological aspects influencing canopy temperature in contrasting wheat cultivars can unveil the specific mechanisms of adaptation to heat stress. However, information on wheat microclimates in hot environments for crop improvement is lacking. Here, we used a micrometeorological method to investigate wheat’s response to high temperatures. Field experiments were conducted in the Gezira Scheme, Sudan, to compare two high-yielding heat-tolerant cultivars, Imam and Bohaine, in terms of canopy temperature depression (CTD), air temperature gradient (ATG), and vapor pressure gradient (VPG) from a 2 m height to canopy level. The maximum air temperature at 2 m during the main growing season was 37 °C. Air temperature at canopy level was mostly lower in the Imam field than in the Bohaine field, and it was positively correlated with and higher than radiometric canopy surface temperature. The maximum CTD during the reproductive stage was 4.7–6.5 °C in the Bohaine field and 5.0–7.2 °C in the Imam field. ATG was also larger in the Imam field, attributed to the greater leaf area of the Imam canopy, as presumed from the NDVI difference between fields. ATG was negatively correlated with VPG in both fields, and the relationship was stronger at lower nighttime wind speeds and weaker at higher daytime wind speeds. These results indicate that the micrometeorological approach can be used to compare cultivars in high-temperature environments.

Norway spruce susceptibility to bark beetles is associated with increased canopy surface temperature in a year prior disturbance

Detailed mapping of below canopy surface temperatures in forests reveals new perspectives on microclimatic processes

STMRT: A simple tree canopy radiative transfer model for outdoor mean radiant temperature

Increasing concentrations of atmospheric CO2 are projected to have positive effects on crop photosynthesis and yield (CO2 fertilization effect, CFE). High-temperature events, such as heatwaves, during sensitive periods can have significant negative impacts on crop yield and quality; however, the combined effects of elevated CO2 (EC) and short-period elevated temperature (ET) have not been determined in the open field. Here, we show a strong negative interaction between EC and ET obtained from a temperature-free-air controlled enhancement treatment embedded in a season-long free-air CO2 enrichment (FACE) experiment on a japonica rice cultivar, Koshihikari, over three seasons at the Tsukuba FACE facility in Ibaraki, Japan. CFE was 15% at ambient temperature, but it was reduced to 3% by ET, where canopy surface temperature (Tc) was elevated by ∼1.6 °C for 20 d after flowering. Reductions in CFE mainly arose from poor grain setting at Tc above ∼30 °C. High Tc also increased the percentage of chalky grains and substantially decreased the grain appearance quality, although the threshold temperature varied between the seasons. Simultaneous increases in atmospheric CO2 concentration and air temperature are expected to increase daytime canopy temperatures more than air warming alone, thereby affecting grain yield and quality. Crop models without these processes are likely to underestimate the negative impacts of climate change on crop yield and quality. The development of adaptation measures against heat stress, particularly during reproductive and grain-filling periods, needs to be enhanced and accelerated.

Based on observation data from the observation tower station in Zhuhai in South China from October 2015 to May 2018, the variations in soil temperature, moisture, and heat flux in the understorey of evergreen broadleaf forest are analyzed. The ground surface temperature is slightly greater than the canopy surface temperature; the soil temperature is the smallest. The seasonal variation in the soil moisture fluctuates around a relative equilibrium as the rain increases or decreases. Both ground and canopy surface temperatures have valley values before sunrise and peak values in the afternoon. The diurnal variations in soil temperature are characterized by a cosine function. With the deepening of the soil layer, the time of valley and peak lag and the amplitude of the variation in the soil temperature decrease. There is almost no diurnal variation in soil temperature at 40 cm depth or the soil heat flux at 30 cm depth. The heat fluxes at depths of 7.5 cm and 15 cm show an “S” pattern variation, with a peak at 17:30. The net radiation of the canopy surface shows a bell-shaped variation, with a peak at 12:30. The averaged soil thermal conductivities are 2.18 W·m−1·K−1, 1.22 W·m−1·K−1, and 1.26 W·m−1·K−1 for 5 ~ 10 cm, 10 ~ 20 cm, and 20 ~ 40 cm depths, respectively.

Quantifying canopy conductance in a pine forest during drought from combined sap flow and canopy surface temperature measurements

Infestations of Tomicus spp. have caused the deaths of millions of Yunnan pine forests in Southwest China; consequently, accurate monitoring methods are required to assess the damage caused by these pest insects at an early stage. Considering the limited sensitivity of optical reflectance on the early stage of beetle stress, the potential of thermal infrared (TIR) can be exploited for monitoring forest health on the basis of the change of canopy surface temperature (CST). However, few studies have investigated the impact of the leaf area index (LAI) on the accuracy of TIR data-based SDR assessments. Therefore, the current study used unmanned airborne vehicle (UAV)-based TIR and light detection and ranging (LiDAR) data to assess the capacity of determining the potential for using TIR data for determining SDR under different LAI conditions. The feasibility of using TIR for monitoring SDRs at the tree level and plot scales were analyzed using the relationship between SDR and canopy temperature. Results revealed that: (1) prediction accuracy of SDR from CST is promising at high LAI values and decreases quickly with LAI, and is higher at the single tree scale (R2 = 0.7890) than at the plot scale (R2 = 0.5532); (2) at either single tree or plot scale, a significant negative correlation can be found between CST and LAI (−0.9121 at tree scale and −0.5902 at plot scale); (3) LAI affects the transmission paths of sunlight and sensor, which mainly disturbs the relationship between CST and SDR. This article evaluated the high possibility of using TIR data to monitor SDRs at both tree and plot levels and assessed the negative impact of a low LAI (<1) on the relationship between temperature and SDR. Accordingly, when measuring forest health using TIR data, additional data sources are required to eliminate the negative impact of low LAIs and to improve the monitoring accuracy.

Unravelling the responses of different apple varieties to water constraints by continuous field thermal monitoring

Summary Tree architecture shows large genotypic variability, but how this affects water‐deficit responses is poorly understood. To assess the possibility of reaching ideotypes with adequate combinations of architectural and functional traits in the face of climate change, we combined high‐throughput field phenotyping and genome‐wide association studies (GWAS) on an apple tree (Malus domestica) core‐collection.We used terrestrial light detection and ranging (T‐LiDAR) scanning and airborne multispectral and thermal imagery to monitor tree architecture, canopy shape, light interception, vegetation indices and transpiration on 241 apple cultivars submitted to progressive field soil drying. GWAS was performed with single nucleotide polymorphism (SNP)‐by‐SNP and multi‐SNP methods.Large phenotypic and genetic variability was observed for all traits examined within the collection, especially canopy surface temperature in both well‐watered and water deficit conditions, suggesting control of water loss was largely genotype‐dependent. Robust genomic associations revealed independent genetic control for the architectural and functional traits. Screening associated genomic regions revealed candidate genes involved in relevant pathways for each trait.We show that multiple allelic combinations exist for all studied traits within this collection. This opens promising avenues to jointly optimize tree architecture, light interception and water use in breeding strategies. Genotypes carrying favourable alleles depending on environmental scenarios and production objectives could thus be targeted.

定量分析植被冠层对降雨再分配过程的影响, 是认识陆地生态系统水文循环的重要环节。然而, 由于干旱区天然植被分布稀疏、形态结构特殊, 其降雨再分配过程的测算较为困难, 相关研究较少, 特别是关于荒漠低矮灌丛的降雨再分配研究鲜有报道。本文以河西走廊中段临泽绿洲—荒漠过渡带的天然建群种泡泡刺灌丛(Nitraria sphaerocarpa)为研究对象, 基于3年逐个单次降雨事件的观测数据分析了生长季泡泡刺灌丛的降雨再分配特征及主要影响因素, 量化了泡泡刺灌丛覆盖下实际进入土壤的有效降雨量及其空间分布特征。结果表明：(1)生长季泡泡刺灌丛的平均穿透率、树干茎流率和冠层截留损失率分别为87.89%、1.61%和10.50%；(2)降雨量是影响泡泡刺灌丛降雨再分配特征的关键气象因素, 其与穿透雨量、树干茎流量、冠层截留损失量之间具有显著的统计关系(P < 0.001)；(3)与干旱区其他稀疏植被相比, 泡泡刺灌丛的穿透率和集流率较高, 冠层截留损失率较低, 与其特殊的植被形态特征有关, 相关分析的结果表明, 泡泡刺灌丛的穿透雨量与植被面积指数和株高呈显著的负相关关系(P < 0.001), 树干茎流量与树干倾角呈显著的正相关关系(P < 0.01)。这些研究结果增进了我们对于绿洲—荒漠过渡带植被对局地水文过程影响的认识, 为合理估算干旱区稀疏植被覆盖下的冠层截留损失提供了方法参考。;Analyzing quantitatively the effect of vegetation canopy on rainfall partitioning is an important part for better understanding the hydrological cycle in terrestrial ecosystems, which is particularly useful for hydrologic budget estimation, hydrological models' establishment and afforestation projects' implement in drylands. However, it is difficult to measure and calculate its rainfall partitioning process of the natural vegetation in the drylands due to its sparse distribution and special morphological structure, and very few field measurements have conducted specially for the natural desert dwarf shrub species, which distributed widely in the oasis-desert ectone of northwestern China. Here we present results of the partitioning of rainfall into throughfall (TF), stemflow (SF) and interception loss (IL) by a shrub species Nitraria sphaerocarpa, a naturally dominant species of Linze oasis-desert ecotone in the middle part of the Hexi Corridor, based on the observation data during the growing season for 3 years. Consequently, the effective rainfall that actually enters the soil and its spatial distribution characteristics beneath the N. sphaerocarpa canopy are quantified. We also analyze the influencing factors for rainfall partitioning by N. sphaerocarpa canopy. The results show that: (1) on average, the measured throughfall, stemflow and derived interception loss by N. sphaerocarpa during growing season account for 87.89%, 1.61% and 10.50% of gross rainfall amount, respectively. The average funneling ratio for N. sphaerocarpa is (129.66 ±93.01) and its canopy storage capacity is 0.42 mm. N. sphaerocarpa's throughfall is produced from rainfall events with total amount more than 0.2 mm, while its stemflow does not occur following rainfall events less than 1.5 mm. (2) Rainfall amount is the key meteorological factor affecting the rainfall partitioning characteristics. There are significant correlations between rainfall amount and throughfall, stemflow and interception loss (P < 0.001). Other meteorological variables like canopy surface temperature, atmospheric temperature, atmospheric humidity and vapor pressure can also affect rainfall redistribution process. (3) Compared with other sparse vegetation in drylands, the N. sphaerocarpa has higher throughfall percentage and funneling ratio, but lower interception loss percentage. Special canopy morphology of N. sphaerocarpa may play an important role in its pattern of rainfall partitioning. Pearson correlation analysis shows that throughfall has significantly negative correlation with plant area index (PAI) and shrub height (P < 0.001), and stemflow has positive correlation with stem orientation (P < 0.01). Other canopy morphology like bark roughness, leaf shape and canopy form, which lack of quantitative description, are also vital for rainfall partitioning patterns. The results might improve better understanding of shrubs' role on the local hydrological processes in oasis-desert ecotone, and provide a reasonable method for estimating interception loss by sparse vegetation in drylands.

Quantifying Stomatal Canopy Conductance in a Pine Forest During Drought from Combined Sap Flow and Canopy Surface Temperature Measurements

By 2050, the U.S. Corn Belt will likely face a 23% increase in leaf‐to‐air vapor pressure deficit (VPDL), the driving force of evapotranspiration (ET), which may restrict maize yield improvements for rainfed agroecosystems. Alternative cropping systems, such as maize and legume intercrops, have previously demonstrated yield and resource‐use advantages over monocultures. In this study, the residual energy balance approach was used to gain insights into how an additive simultaneous maize and soybean intercrop system regulates ET and water‐use efficiency (WUE) compared to standard maize and soybean monoculture systems of the U.S. Corn Belt. Experimental field plots were rain‐fed and arranged in a randomized complete block design in three blocks. Photosynthetic capacity and grain yield of maize were conserved in the intercrop. However, its competitive dominance shaded 80%–90% of incident light for intercropped soybean at canopy closure, leading to a 94% decrease in grain yield compared to soybean monoculture. The total grain yield per unit area of the additive intercrop (land‐use efficiency) increased by 11% ± 6% (1 SE). Compared to maize monoculture, the intercrop had higher latent heat fluxes (λET) at night but lower daytime λET as the intercrop canopy surface temperature was approximately .25°C warmer, partitioning more energy to sensible heat flux. However, the diel differences in λET fluxes were not sufficient to establish a statistically significant or biologically relevant decrease in seasonal water‐use (ΣET). Likewise, the increase in land‐use efficiency by the intercrop was not sufficient to establish an increase in seasonal water‐use efficiency. Intercropping high‐performing maize and soybean cultivars in a dense configuration without negative impact suggests that efforts to increase yield and WUE may lead to improved benefits.

Thermal imaging is utilized as a technique in agricultural crop water management due to its efficiency in estimating canopy surface temperature and the ability to predict crop water levels. Thermal imaging was considered as a beneficial integration in Unmanned Aerial Vehicle (UAV) for agricultural and civil engineering purposes with the reduced weight of thermal imaging systems and increased resolution. When implemented on-site, this technique was able to address a number of difficulties, including estimation of water in the plant in farms or fields, while considering officially induced variability or naturally existing water level. The proposed effort aims to determine the amount of water content in a vineyard using the high-resolution thermal imaging. This research work has developed an unmanned aerial vehicle (UAV) that is particularly intended to display high-resolution images. This approach will be able to generate crop water stress index (CWSI) by utilizing a thermal imaging system on a clear-sky day. The measured values were compared to the estimated stomatal conductance (sg) and stem water (s) potential along the Vineyard at the same time. To evaluate the performance of the proposed work, special modelling approach was used to identify the pattern of variation in water level. Based on the observation, it was concluded that both ‘sg’ and ‘s’ value have correlated well with the CWSI value by indicating a great potential to monitor instantaneous changes in water level. However, based on seasonal changes in water status, it was discovered that the recorded thermal images did not correspond to seasonal variations in water status.

The real challenge for separating leaf pixels from background pixels in thermal images is associated with various factors such as the amount of emitted and reflected thermal radiation from the targeted plant, absorption of reflected radiation by the humidity of the greenhouse, and the outside environment. We proposed TheLNet270v1 (thermal leaf network with 270 layers version 1) to recover the leaf canopy from its background in real time with higher accuracy than previous systems. The proposed network had an accuracy of 91% (mean boundary F1 score or BF score) to distinguish canopy pixels from background pixels and then segment the image into two classes: leaf and background. We evaluated the classification (segment) performance by using more than 13,766 images and obtained 95.75% training and 95.23% validation accuracies without overfitting issues. This research aimed to develop a deep learning technique for the automatic segmentation of thermal images to continuously monitor the canopy surface temperature inside a greenhouse.

Intercropping of legumes and cereals provides many ecological advantages and contributes to a sustainable agriculture. These agricultural systems face ongoing shifts in precipitation patterns and seasonal drought. Although the effect of drought stress on legumes has been frequently studied, knowledge about water deficits influencing legumes under different cropping systems is still limited. Therefore, we investigated the impact of water deficit and re-irrigation on two winter faba bean genotypes (S_004 and S_062) and winter wheat (var. Genius) in pure and intercropped stands under greenhouse conditions. Various physiological and biochemical parameters, such as canopy surface temperature, leaf relative water content and proline content, were collected at three time points (beginning of water deficit, end of water deficit, after re-irrigation). In addition, water use efficiency (WUE) was analyzed at the end of the experiment. The overall drought stress tolerance was determined as conceptual analysis of all measured parameters. Water deficit significantly affected WUE, surface temperature and proline content of both winter faba bean genotypes. Interestingly, intercropping with wheat resulted in an overall high drought tolerance of genotype S_004, while genotype S_062 had a high drought tolerance in pure stands. Under water deficit, pure stands of S_062 substantially increased WUE by 30.5%. Intercropping of genotype S_004 increased the dry matter per plant by 31.7% compared to pure stands under water deficit. Contrary, intercropping of genotype S_062 did not improve the dry matter production. Our findings indicate that genotype S_004 benefits from resource complementarity in intercropping systems with wheat, whereas S_062 is better suitable for pure stands due to competitive effects. Thus, our study highlights that the drought tolerance of winter faba bean genotypes depends on the cropping system, leading to a demand for drought-adapted cultivars specifically selected for intercropping.

Impact of steep slope management system and row orientation on canopy microclimate. Comparing terraces to downslope vineyards