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【Objective】 Canopy temperature varies with leaf water content and can be used as a proxy of crop water deficit. In this paper, we compare different interpolation methods for calculating spatial distribution of crop water deficit based on canopy temperature. 【Method】 The calculation was based on the 10 interpolation modules in ArcGIS. We studied two experimental sites cultivated with maize - winter wheat rotation. For each site, we analyzed the accuracy and zoning effect of canopy temperature, as well as the normalized relative canopy temperature (NRCT). The accuracy and robustness of each interpolation method was evaluated based on its characteristic value, normalized root mean square error (nRMSE) and Pearson correlation coefficient between predicted and ground-truth values, as well as spatial distributions of the predicted canopy temperature and NRCT. 【Result】 The spatial distribution of canopy temperature of the winter wheat and summer maize both has a strong autocorrelation on the two sites. The canopy temperature estimated using local polynomial and universal Kriging interpolation is spatially abnormal. The nRMSE and NRCT between the measured canopy temperature and that predicted using global polynomial interpolation are the highest, being 5.9% and 28.6% respectively; their associated Pearson correlation coefficient is 0.33. The ordinary Kriging method is most accurate in that the difference between the predicted and measured canopy temperatures is less than 0.5 ℃; its associated nRMSE (3.6%) and NRCT (17.5%) are the least with a Pearson correlation coefficient 0.8. The spatial distribution of canopy temperature calculated by the simple Kriging method, disjunctive Kriging method and empirical Bayesian Kriging method is similar to that by the ordinary Kriging method; their overlapping percentage is greater than 90%. 【Conclusion】 Considering accuracy and spatial distribution of canopy temperature and NRCT, the interpolation methods is ranked in the following order based on their accuracy: ordinary Kriging method > simple Kriging method > disjunctive Kriging method > empirical Bayesian Kriging method > radial basis function method (tension spline function) > radial basis function method(regular spline function) > inverse distance weight method. Overall, the ordinary Kriging method is most accurate for estimating crop water deficit from canopy temperature.

The crucial obstacle in the research of plant science is to distinguish between superior genotypes and its selection. Machine vision can recognize the better genotype precisely, effortlessly and in asymptomatic manner. Genotype differentiation based on machine vision by using thermal imaging parameters is evaluated and elaborated in this article. The main objective of this study is to assess better performance and select superior genotypes based on the common thermal indicators. Mungbean and chickpea crops were studied and measured in greenhouse conditions with well-watered and water stress treatments. An algorithm is developed for extracting parameters from thermal images and implemented in a Python tool using OpenCV (cv2), Pandas packages. The genotypes of the crops were contrasted for drought tolerance to be able to differentiate drought responses with thermal imaging. The result of the experiments express that crops are differentiated between treatments and discriminated among genotypes within a treatment. These results were validated with the soil moisture data, which was collected on simultaneous day of the image captured.

Canopy temperature T can is a key driver of plant function that emerges as a result of interacting biotic and abiotic processes and properties. However, understanding controls on T can and forecasting canopy responses to weather extremes and climate change are difficult due to sparse measurements of T can at appropriate spatial and temporal scales. Burgeoning observations of T can from thermal cameras enable evaluation of energy budget theory and better understanding of how environmental controls, leaf traits and canopy structure influence temperature patterns. The canopy scale is relevant for connecting to remote sensing and testing biosphere model predictions. We anticipate that future breakthroughs in understanding of ecosystem responses to climate change will result from multiscale observations of T can across a range of ecosystems.

Understanding and predicting the relationship between leaf temperature (Tleaf ) and air temperature (Tair ) is essential for projecting responses to a warming climate, as studies suggest that many forests are near thermal thresholds for carbon uptake. Based on leaf measurements, the limited leaf homeothermy hypothesis argues that daytime Tleaf is maintained near photosynthetic temperature optima and below damaging temperature thresholds. Specifically, leaves should cool below Tair at higher temperatures (i.e., > ∼25–30°C) leading to slopes <1 in Tleaf/Tair relationships and substantial carbon uptake when leaves are cooler than air. This hypothesis implies that climate warming will be mitigated by a compensatory leaf cooling response. A key uncertainty is understanding whether such thermoregulatory behavior occurs in natural forest canopies. We present an unprecedented set of growing season canopy-level leaf temperature (Tcan ) data measured with thermal imaging at multiple well-instrumented forest sites in North and Central America. Our data do not support the limited homeothermy hypothesis: canopy leaves are warmer than air during most of the day and only cool below air in mid to late afternoon, leading to Tcan/Tair slopes >1 and hysteretic behavior. We find that the majority of ecosystem photosynthesis occurs when canopy leaves are warmer than air. Using energy balance and physiological modeling, we show that key leaf traits influence leaf-air coupling and ultimately the Tcan/Tair relationship. Canopy structure also plays an important role in Tcan dynamics. Future climate warming is likely to lead to even greater Tcan , with attendant impacts on forest carbon cycling and mortality risk.

As evaporation of water is an energy-demanding process, increasing evapotranspiration rates decrease the surface temperature (Ts) of leaves and plants. Based on this principle, ground-based thermal remote sensing has become one of the most important methods for estimating evapotranspiration and drought stress and for irrigation. This paper reviews its application in agriculture. The review consists of four parts. First, the basics of thermal remote sensing are briefly reviewed. Second, the theoretical relation between Ts and the sensible and latent heat flux is elaborated. A modelling approach was used to evaluate the effect of weather conditions and leaf or vegetation properties on leaf and canopy temperature. Ts increases with increasing air temperature and incoming radiation and with decreasing wind speed and relative humidity. At the leaf level, the leaf angle and leaf dimension have a large influence on Ts; at the vegetation level, Ts is strongly impacted by the roughness length; hence, by canopy height and structure. In the third part, an overview of the different ground-based thermal remote sensing techniques and approaches used to estimate drought stress or evapotranspiration in agriculture is provided. Among other methods, stress time, stress degree day, crop water stress index (CWSI), and stomatal conductance index are discussed. The theoretical models are used to evaluate the performance and sensitivity of the most important methods, corroborating the literature data. In the fourth and final part, a critical view on the future and remaining challenges of ground-based thermal remote sensing is presented.

Plant phenotyping forms the core of crop breeding, allowing breeders to build on physiological traits and mechanistic science to inform their selection of material for crossing and genetic gain. Recent rapid progress in high throughput techniques based on machine vision, robotics and computing (Plant Phenomics) enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits. By combining these techniques with affordable genomic sequencing and genotyping, machine learning and genome selection approaches, breeders have an opportunity to make rapid genetic progress. This review focusses on how field based plant phenomics can enable next generation physiological breeding in cereal crops for traits related to radiation use efficiency, photosynthesis and crop biomass. These traits have previously been regarded as difficult and laborious to measure but have recently become a focus as cereal breeders find genetic progress from “Green Revolution” traits such as harvest index become exhausted. Application of LiDAR, thermal imaging, leaf and canopy spectral reflectance, chlorophyll fluorescence and machine learning are discussed using wheat and sorghum phenotyping as case studies. A vision of how crop genomics and high-throughput phenotyping could enable the next generation of crop research and breeding is presented. This article is protected by copyright. All rights reserved.

Understanding tree transpiration variability is vital for assessing ecosystem water‐use efficiency and forest health amid climate change, yet most landscape‐level measurements do not differentiate individual trees. Using canopy temperature data from thermal cameras, we estimated the transpiration rates of individual trees at Harvard Forest and Niwot Ridge. PT‐JPL model was used to derive latent heat flux from thermal images at the canopy‐level, showing strong agreement with tower measurements (R2 = 0.70–0.96 at Niwot, 0.59–0.78 at Harvard at half‐hourly to monthly scales) and daily RMSE of 33.5 W/m2 (Niwot) and 52.8 W/m2 (Harvard). Tree‐level analysis revealed species‐specific responses to drought, with lodgepole pine exhibiting greater tolerance than Engelmann spruce at Niwot and red oak showing heightened resistance than red maple at Harvard. These findings show how ecophysiological differences between species result in varying responses to drought and demonstrate that these responses can be characterized by deriving transpiration from crown temperature measurements. Plain Language Summary Understanding how forests use water, especially during droughts, is crucial for a changing climate. We developed a method using thermal cameras to estimate individual tree water loss (transpiration), something traditional methods lack. These cameras capture temperature data from the tree crown, which is then used to estimate transpiration rates. Tests in two forests showed this method aligns well with existing water use measurements. The study also revealed fascinating differences in how species handle drought. For instance, lodgepole pine outperformed Engelmann spruce in one forest, while red oak proved more resistant than red maple in the other. This shows that the thermal cameras can help assess how different trees resist drought conditions. This thermal camera technique has the potential to become a valuable tool for monitoring forest health as our climate evolves. Key Points Canopy‐level evapotranspiration derived from thermal cameras agreed well with eddy covariance measurements Crown temperature measurements facilitated the estimation of individual‐tree transpiration of co‐occurring species using the same approach The results show interspecific differences in water use and response to drought that align with species traits

Simple and repeatable methods are needed to select for deep roots under field conditions. A large-scale field experiment was conducted to assess the association between canopy temperature (CT) measured by airborne thermography and rooting depth determined by the core-break method. Three wheat populations, C306×Westonia (CW), Hartog×Drysdale (HD), and Sundor×Songlen (SS), were grown on stored soil water in NSW Australia in 2017 (n=196–252). Cool and warm CT extremes (‘tails’) were cored after harvest (13–32% of each population). Rooting depth was significantly correlated with CT at late flowering (r= –0.25, –0.52, and –0.23 for CW, HD, and SS, respectively, P<0.05 hereafter), with normalized difference vegetation index (NDVI) at early grain filling (r=0.30–0.39), and with canopy height (r=0.23–0.48). The cool tails showed significantly deeper roots than the respective warm tails by 8.1 cm and 6.2 cm in CW and HD, and correspondingly, greater yields by an average 19% and 7%, respectively. This study highlighted that CT measured rapidly by airborne thermography or NDVI at early grain filling could be used to guide selection of lines with deeper roots to increase wheat yields. The remote measurement methods in this study were repeatable and high throughput, making them well suited to use in breeding programmes.

Plant roots play a significant role in plant growth by exploiting soil resources via the uptake of water and nutrients. Root traits such as fine root diameter, specific root length, specific root area, root angle, and root length density are considered useful traits for improving plant productivity under drought conditions. Therefore, understanding interactions between roots and their surrounding soil environment is important, which can be improved through root phenotyping. With the advancement in technologies, many tools have been developed for root phenotyping. Canopy temperature depression (CTD) has been considered a good technique for field phenotyping of crops under drought and is used to estimate crop yield as well as root traits in relation to drought tolerance. Both laboratory and field-based methods for phenotyping root traits have been developed including soil sampling, mini-rhizotron, rhizotrons, thermography and non-soil techniques. Recently, a non-invasive approach of X-ray computed tomography (CT) has provided a break-through to study the root architecture in three dimensions (3-D). This review summarizes methods for root phenotyping. On the basis of this review, it can be concluded that root traits are useful characters to be included in future breeding programs and for selecting better cultivars to increase crop yield under water-limited environments.

【Objective】 Change in soil water content is not only an indicator of water stresses used for irrigation management but also controls biogeochemical processes in soil. In this paper, we study the feasibility of using thermal infrared imaging to estimate soil moisture dynamics. 【Method】 The experiment was conducted in July-August 2023 in a walnut orchard in Xinjiang. Thermal infrared images of the walnut canopy were measured continuously using a thermal infrared camera. Based on the HSV color space of the images, an improved K-means segmentation algorithm was proposed to analyze the change in canopy temperature. We also measured air temperature and humidity, illuminance, wind speed, atmospheric CO2, and soil water content in the 0-80 cm soil layer, from which we proposed an inversion model to estimate soil water dynamics. 【Result】 The improved K-means algorithm increased the accuracy from 82.34% to 94.55%, and the errors between the canopy temperature acquired from the images and the measured canopy temperature were in the range of 0 to 1.0. The infrared imaging method was most accurate between14:00 pm to 16:00 pm. Our results showed that the walnut roots were most active in taking up water from the 40-60 cm soil layer 50-60 cm away horizons from the tree truck. Canopy temperature, air temperature and relative humidity, and atmospheric CO2 concentration were correlated with soil water content at significant levels; they can thus be used to estimate soil water dynamics, with a coefficient of determination of R2=0.86 and p<0.01. 【Conclusion】 The temperature acquired from the infrared images of the walnut canopy can be used with other metrological data and atmospheric CO2 concentration to estimate soil water dynamics in the root zone of the walnut. It provides a new method for improving soil water management in walnut orchards in Xinjiang.