Canopy Temperature Data Research Articles

Scaling Individual Tree Transpiration With Thermal Cameras Reveals Interspecies Differences to Drought Vulnerability

AbstractUnderstanding 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.

Integrating NDVI, SPAD, and Canopy Temperature for Strategic Nitrogen and Seeding Rate Management to Enhance Yield, Quality, and Sustainability in Wheat Cultivation.

This study explores the interplay between nitrogen doses and seeding rates on wheat yield, biomass, and protein content. Utilizing tools such as the Normalized Difference Vegetation Index (NDVI), Soil Plant Analysis Development (SPAD) measurements, and canopy temperature (CT), we conducted experiments over five growing seasons. The treatments included three nitrogen levels (0, 60, 120 kg/ha) and three seeding rates (300, 400, 500 seeds/m2) in a split-plot design with 90 plots and two replications. Our results show that an intermediate nitrogen dose (60 kg/ha) combined with a moderate seed rate (400 seeds/m2) enhances wheat yield by 22.95%. Reduced nitrogen levels increased protein content, demonstrating wheat's adaptive mechanisms under nitrogen constraints. NDVI analysis highlighted significant growth during the tillering phase with high nitrogen, emphasizing early-stage nutrient management. SPAD measurements showed that early nitrogen applications boost chlorophyll content, essential for vigorous early growth, while CT data indicate that optimal nitrogen and seed rates can effectively modulate plant stress responses. As crops mature, the predictive capacity of NDVI declines, indicating the need for adjusted nitrogen strategies. Collectively, these findings advocate for refined management of nitrogen and seeding rates, integrating NDVI, SPAD, and CT assessments to enhance yields and promote sustainable agricultural practices while minimizing environmental impacts.

Thermal imaging can reveal variation in stay-green functionality of wheat canopies under temperate conditions.

Canopy temperature (CT) is often interpreted as representing leaf activity traits such as photosynthetic rates, gas exchange rates, or stomatal conductance. This interpretation is based on the observation that leaf activity traits correlate with transpiration which affects leaf temperature. Accordingly, CT measurements may provide a basis for high throughput assessments of the productivity of wheat canopies during early grain filling, which would allow distinguishing functional from dysfunctional stay-green. However, whereas the usefulness of CT as a fast surrogate measure of sustained vigor under soil drying is well established, its potential to quantify leaf activity traits under high-yielding conditions is less clear. To better understand sensitivity limits of CT measurements under high yielding conditions, we generated within-genotype variability in stay-green functionality by means of differential short-term pre-anthesis canopy shading that modified the sink:source balance. We quantified the effects of these modifications on stay-green properties through a combination of gold standard physiological measurements of leaf activity and newly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation. In parallel, we monitored CT by means of a pole-mounted thermal camera that delivered continuous, ultra-high temporal resolution CT data. Our results show that differences in stay-green functionality translate into measurable differences in CT in the absence of major confounding factors. Differences amounted to approximately 0.8°C and 1.5°C for a very high-yielding source-limited genotype, and a medium-yielding sink-limited genotype, respectively. The gradual nature of the effects of shading on CT during the stay-green phase underscore the importance of a high measurement frequency and a time-integrated analysis of CT, whilst modest effect sizes confirm the importance of restricting screenings to a limited range of morphological and phenological diversity.

Using a soil moisture sensor-based smart controller for autonomous irrigation management of hybrid bermudagrass with recycled water in coastal Southern California

There is a lack of information on the reliability of smart soil moisture sensor-based irrigation controllers for autonomous urban landscape irrigation management using recycled water where water conservation is required, which is the main objective of this study. A three-year turfgrass irrigation research trial (2019–2021) using tertiary-treated recycled water (mean EC = 1.18 dS/m, pH = 7.5) was conducted in Irvine, California, USA. A total of 12 irrigation treatments were designed, including six soil moisture-based thresholds (for triggering and terminating irrigation events) × two irrigation frequency treatments (restricted versus on-demand). Weekly NDVI and canopy temperature data were collected during the summer irrigation season to assess the impact of irrigation treatments on the overall health and quality of 'Tifgreen 328' hybrid bermudagrass. Soil salinity and sodium adsorption ratio (SAR) were measured before and after the summer irrigation seasons. Findings revealed statistically significant effects of irrigation levels and irrigation frequency restrictions on NDVI over the three years. The temperature difference (dT) between turfgrass and air (Tc - Ta) was significantly affected by irrigation levels throughout the three years, while irrigation frequency restrictions showed a significant effect in the years 2021 and 2022. On-demand irrigation resulted in a 10% reduction in temperature difference values during the study period compared to restricted irrigation. Variations in soil salinity levels were observed during the experimental periods; however, both salinity and SAR increased as the study progressed. Results indicate that fully autonomous hybrid bermudagrass irrigation with recycled water, based on recommended soil moisture thresholds of 75% of field capacity over an extended period in a semiarid climate, may lead to unacceptable turfgrass quality.

Estimation of Millet Aboveground Biomass Utilizing Multi-Source UAV Image Feature Fusion

Aboveground biomass (AGB) is a key parameter reflecting crop growth which plays a vital role in agricultural management and ecosystem assessment. Real-time and non-destructive biomass monitoring is essential for accurate field management and crop yield prediction. This study utilizes a multi-sensor-equipped unmanned aerial vehicle (UAV) to collect remote sensing data during critical growth stages of millet, including spectral, textural, thermal, and point cloud information. The use of RGB point cloud data facilitated plant height extraction, enabling subsequent analysis to discern correlations between spectral parameters, textural indices, canopy temperatures, plant height, and biomass. Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) models were constructed to evaluate the capability of different features and integrated multi-source features in estimating the AGB. Findings demonstrated a strong correlation between the plant height derived from point cloud data and the directly measured plant height, with the most accurate estimation of millet plant height achieving an R2 of 0.873 and RMSE of 7.511 cm. Spectral parameters, canopy temperature, and plant height showed a high correlation with the AGB, and the correlation with the AGB was significantly improved after texture features were linearly transformed. Among single-factor features, the RF model based on textural indices showcased the highest accuracy in estimating the AGB (R2 = 0.698, RMSE = 0.323 kg m−2, and RPD = 1.821). When integrating two features, the RF model incorporating textural indices and canopy temperature data demonstrated optimal performance (R2 = 0.801, RMSE = 0.253 kg m−2, and RPD = 2.244). When the three features were fused, the RF model constructed by fusing spectral parameters, texture indices, and canopy temperature data was the best (R2 = 0.869, RMSE = 0.217 kg m−2, and RPD = 2.766). The RF model based on spectral parameters, texture indices, canopy temperature, and plant height had the highest accuracy (R2 = 0.877, RMSE = 0.207 kg m−2, and RPD = 2.847). In this study, the complementary and synergistic effects of multi-source remote sensing data were leveraged to enhance the accuracy and stability of the biomass estimation model.

Assessing the Impact of Water Conservation on Cooling Potential of Two Turfgrass Species

Highlights Empirical CWSI was developed for 'Westcoaster' tall fescue and ‘Tifgreen’ hybrid bermudagrass. The canopy temperature of both species was monitored under multiple irrigation scenarios. Deficit irrigation might reduce the cooling benefits of irrigated urban lawns. Abstract. Canopy temperature provides valuable information for efficient irrigation management and for detecting drought injury in a fast and non-destructive way. It also helps quantify the trade-offs between water conservation and the cooling benefit of the irrigated urban landscape, a critical issue in semiarid inland southern California with limited available water resources. Two adjacent hybrid bermudagrass and tall fescue irrigation trials were conducted to determine canopy temperature changes under a wide range of irrigation treatments from 2017 to 2019 in Riverside, California, USA. The canopy temperature data were also used to develop an empirical crop water stress index (CWSI) for each species. The CWSI values ranged from -0.15 to 0.71 for tall fescue and from -0.28 to 0.54 for hybrid bermudagrass. We observed a moderate correlation between visual rating scores (VR) and CWSI values for tall fescue (r = -0.68) and hybrid bermudagrass (r = -0.61). The fitted linear regression between VR and CWSI data suggested CWSI thresholds close to zero for both species to maintain their acceptable visual quality. The irrigation level consistently showed a significant effect on canopy temperature for both species. On average, a 10% decrease in irrigation application increased the canopy temperature of tall fescue and hybrid bermudagrass by 1.3°C and 0.9°C, respectively. Therefore, deficit irrigation might reduce the cooling benefits of irrigated urban lawns in the semiarid climate of inland southern California. Keywords: Canopy temperature, Evapotranspiration, Smart irrigation controller, Urban heat island, Urban irrigation, Water conservation.

Crop response to thermal stress without yield loss in irrigated maize and soybean in Nebraska

Thermal sensing provides rapid and accurate estimation of crop water stress through canopy temperature data. Canopy temperature is highly dependent on the transpiration rate of the leaves. It is usually assumed that any reduction in crop evapotranspiration (ET) leads to crop yield loss. As a result, an increase in canopy temperature due to a decrease in crop ET would indicate crop yield loss. This research evaluated the hypothesis that crop water stress could be detected using canopy temperature measurements (increased leaf temperature) from infrared thermometers (IRTs) before incurring crop yield loss. This would be possible in a narrow range when the photosynthesis rate (and carbon assimilation) is limited by solar radiation (energy-limiting water stress) while the leaf has abundant carbon dioxide for photosynthesis. Once photosynthesis becomes limited by carbon dioxide (carbon-dioxide-limiting water stress), then yield reduction would occur. In this field experiment, measured response variables included the integrated crop water stress index (iCWSI), ET, and crop yield for maize and soybean during the 2020 and 2021 growing seasons. The irrigation was applied at four different refill levels: rainfed (0%), deficit (50%), full (100%), and over (150%). The irrigation depth was prescribed using four different irrigation methods. The field was irrigated with a center pivot irrigation system, which was also used as a platform to mount IRT sensors. The iCWSI thresholds required for irrigation management were determined using the iCWSI dataset collected in 2020. The low, medium, and high iCWSI thresholds were 120, 150, and 180, respectively for maize and 110, 130, and 150, respectively for soybean. These thresholds should be updated with iCWSI data from future studies in this region to increase the credibility of the thresholds for irrigation management. The mean iCWSI values for consecutive days after a wetting event substantially increased with time for each irrigation level and a larger range in iCWSI values was observed among the irrigation levels after three days from a wetting event. The seasonal iCWSI for different levels were found to be negatively correlated with seasonal evapotranspiration for both years. The correlations between seasonal ET and crop yield were significant with the rainfed and deficit levels for maize (p-value < 0.001) and soybean (p-value = 0.04) in 2020. The iCWSI and yield data for the fully watered plots indicated that thermal stress was detected using the sensing system without incurring yield loss (i.e., energy-limiting water stress). The ET and yield data for 2021 indicated that reduction in seasonal crop ET did not result in yield loss which also supported the hypothesis. Future studies should investigate whether this phenomenon of detecting crop water stress in an early stage without yield loss is observed in other climates and locations.

Machine Learning approach to Predict net radiation over crop surfaces from global solar radiation and canopy temperature data.

As the ground-based instruments for measuring net radiation are costly and need to be handled skillfully, the net radiation data at spatial and temporal scales over Indian subcontinent are scanty. Sometimes, it is necessary to use other meteorological parameters to estimate the value of net radiation, although the prediction may vary based on season, ground cover and estimation method. In this context, artificial intelligence can be used as a powerful tool for predicting the data considering past observed data. This paper proposes a novel method to predict the net radiation for five crop surfaces using global solar radiation and canopy temperature. This contribution includes the generation of real-time data for five crops grown in West Bengal state of India. After manual analysis and data preprocessing, data normalization has been done before applying machine learning approaches for training a robust model. We have presented the comparison in various machine learning algorithm such as ridge and spline regression, random forest, ensemble and deep neural networks. The result shows that the gradient boosting regression and ridge regression are outperforming other ML approaches. The estimated predictors enable to reduce the number of resources in terms of time, cost and manpower for proper net radiation estimation. Thus, the problem of predicting net radiation over various crop surfaces can be sorted out through ML algorithm.

Response of a Diverse European Soybean Collection to "Short Duration" and "Long Duration" Drought Stress.

Drought causes significant damage to a high value crop of soybean. Europe has an increasing demand for soybean and its own production is insufficient. Selection and breeding of cultivars adapted to European growth conditions is therefore urgently needed. These new cultivars must have a shorter growing cycle (specifically for adaptation to North-West Europe), high yield potential under European growing conditions, and sufficient drought resistance. We have evaluated the performance of a diverse collection of 359 soybean accessions under drought stress using rain-out shelters for 2 years. The contrasting weather conditions between years and correspondingly the varying plant responses demonstrated that the consequences of drought for an individual accession can vary strongly depending on the characteristics (e.g., duration and intensity) of the drought period. Short duration drought stress, for a period of four to 7 weeks, caused an average reduction of 11% in maximum canopy height (CH), a reduction of 17% in seed number per plant (SN) and a reduction of 16% in seed weight per plant (SW). Long duration drought stress caused an average reduction of 29% in CH, a reduction of 38% in SN and a reduction of 43% in SW. Drought accelerated plant development and caused an earlier cessation of flowering and pod formation. This seemed to help some accessions to better protect the seed yield, under short duration drought stress. Drought resistance for yield-related traits was associated with the maintenance of growth under long duration drought stress. The collection displayed a broad range of variation for canopy wilting and leaf senescence but a very narrow range of variation for crop water stress index (CWSI; derived from canopy temperature data). To the best of our knowledge this is the first study reporting a detailed investigation of the response to drought within a diverse soybean collection relevant for breeding in Europe.

Comparison of Stationary and Mobile Canopy Sensing Systems for Maize and Soybean in Nebraska, USA

HighlightsMultispectral sensors mounted on the center pivot lateral were able to capture differences between rainfed and irrigated crop.Canopy temperature was strongly associated among stationary and pivot-mounted sensors with coefficient of determination ranging between 0.88 and 0.99.A cooling effect of about 2°C was observed in canopy temperature data collected from pivot mounted sensors for irrigated soybean crop.Abstract. Accurate knowledge of plant and field characteristics is crucial for irrigation management. Irrigation can potentially be better managed by utilizing data collected from various sensors installed on different platforms. The accuracy and repeatability of each data source are important considerations when selecting a sensing system suitable for irrigation management. The objective of this study was to compare data from multispectral (red and near-infrared bands) and thermal (long wave thermal infrared band) sensors mounted on different platforms to investigate their comparative usability and accuracy. The different sensor platforms included stationary posts fixed on the ground, the lateral of a center pivot irrigation system, unmanned aircraft systems (UAS), and Planet (PlanetScope multispectral imager, Planet Labs, Inc., San Francisco, Calif.) satellites. The surface reflectance data from multispectral (MS) sensors were used to compute the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). The experimental plots were managed with rainfed and irrigated treatments. Irrigation was applied according to a spatial evapotranspiration model informed with Planet satellite imagery. The NDVI and SAVI curves computed from the different sensing systems exhibited similar patterns and were able to capture differences between the rainfed and irrigated treatments when the crops were approaching senescence. Strong correlations were observed for canopy temperature measurements between the stationary and pivot-mounted infrared thermometer (IRT) sensors (p-value of less than 0.01 for the correlations) when canopy were scanned with no irrigation application (dry scans). The best correlation was obtained for the irrigated maize, which yielded r2 of 0.99, RMSE of 0.4°C, and MAE of 0.3°C. The correlation for the canopy temperature data collected during dry scan between UAS and pivot-mounted thermal sensors was weak with r2 = 0.26 to 0.28, larger RMSE values of 3.7°C and MAE values of 3.4°C. Secondary analysis between thermal data from stationary and pivot-mounted IRTs collected during wet scans (during an irrigation event) demonstrated reduced canopy temperature from pivot-mounted IRTs by approximately 2°C for irrigated soybean due to wetting of the canopy by the irrigation. Understanding the performance of these sensor systems is valuable in configuring practical design and operational considerations when using sensor feedback for irrigation management. Keywords: Center pivots, Irrigation, Multispectral, Remote sensing, Thermal, Unmanned aircraft systems.

CROP WATER STRESS INDEX FOR PREDICTING YIELD LOSS IN COMMON BEAN

The crop water stress index (CWSI), an index derived from canopy temperature, has been widely studied as a physiological indicator of plant water status to optimize irrigation in common beans. However, it is not clear how this index could contribute to yield prediction as a decision support tool in irrigation management. This paper aimed to use the CWSI for predicting yield loss in common bean (Phaseolus vulgaris L.) subjected to water stress under drip irrigation. A rain shelter experiment was conducted using a completely randomized design with five replications. The indeterminate growth cultivar TAA Dama was subjected to three irrigation treatments: 100% of the field capacity (FC), 75 and 50% FC from 20 days after sowing (DAS) until the end of the crop cycle. Grain yield was reduced by 42% under 50% FC treatment. Furthermore, stomatal conductance was reduced under this treatment, whereas the CWSI and canopy temperature increased as irrigation levels decreased. The relationship between grain yield and CWSI (R2=0.76, RSME=2.35g) suggests that canopy temperature data could be used to forecast grain yield losses. In conclusion, farmers can have a low-cost, effective technique for making water management decisions in common bean.

Hybrid Bermudagrass and Tall Fescue Turfgrass Irrigation in Central California: II. Assessment of NDVI, CWSI, and Canopy Temperature Dynamics

As the drought conditions persist in California and water continues to become less available, the development of methods to reduce water inputs is extremely important. Therefore, improving irrigation water use efficiency and developing water conservation strategies is crucial for maintaining urban green infrastructure. This two-year field irrigation project (2018–2019) focused on the application of optical and thermal remote sensing for turfgrass irrigation management in central California. We monitored the response of hybrid bermudagrass and tall fescue to varying irrigation treatments, including irrigation levels (percentages of reference evapotranspiration, ETo) and irrigation frequency. The ground-based remote sensing data included NDVI and canopy temperature, which was subsequently used to calculate the crop water stress index (CWSI). The measurements were done within two hours of solar noon under cloud-free conditions. The NDVI and canopy temperature data were collected 21 times in 2018 and 10 times in 2019. For the tall fescue, a strong relationship was observed between NDVI and visual rating (VR) values in both 2018 (r = 0.92) and 2019 (r = 0.83). For the hybrid bermudagrass, there was no correlation in 2018 and a moderate correlation (r = 0.72) in 2019. There was a moderate correlation of 0.64 and 0.88 in 2018 and 2019 between tall fescue canopy minus air temperature difference (dt) and vapor pressure deficit (VPD) for the lower CWSI baseline. The correlation between hybrid bermudagrass dt and VPD for the lower baseline was 0.69 in 2018 and 0.64 in 2019. Irrigation levels significantly impacted tall fescue canopy temperature but showed no significant effect on hybrid bermudagrass canopy temperature. For the same irrigation levels, increasing irrigation frequency slightly but consistently decreased canopy temperature without compromising the turfgrass quality. The empirical CWSI values violated the minimum expected value (of 0) 38% of the time. Our results suggest NDVI thresholds of 0.6–0.65 for tall fescue and 0.5 for hybrid bermudagrass to maintain acceptable quality in the central California region. Further investigation is needed to verify the thresholds obtained in this study, particularly for hybrid bermudagrass, as the recommendation is only based on 2019 data. No CWSI threshold was determined to maintain turf quality in the acceptable range because of the high variability of CWSI values over time and their low correlation with VR values.

Image based thermal sensing for glyphosate resistant weed identification in greenhouse conditions

In mid-western states, studies have estimated that herbicide-resistant weed management has increased weed control costs by $20–$40 per acre. From 2019 to 2020, greenhouse research was conducted to investigate the canopy temperature response of waterhemp (Amaranthusrudis), kochia (Kochiascoparia), common ragweed (Ambrosiaartemisiifolia), horseweed (Conyzacanadensis),and Palmer amaranth (Amaranthuspalmeri),after glyphosate application to identify glyphosate resistance. In theseexperiments,thermal images were captured of randomized glyphosate-resistant and -susceptible collections of each weed species. The weed canopies' temperature values were extracted from the thermal images andsubmitted to statistical testing. Various classifiers were established to discriminatebetween resistant and susceptible biotypes.Results showed glyphosate-resistant horseweed was the only biotype under greenhouse conditions reliably classified with the resistance trait, 89% accuracy, using cooler temperature signatures than its susceptible counterpart as the discrimination characteristic. The unstable performance from thermal imagery further suggested canopy temperature data was not a reliable predictor of glyphosate resistance. Investigation of additional multispectral sensing technologies or analysis models should be evaluated for more reliable identification of glyphosate-resistant weed species.

Evaluation of the MOD11A2 product for canopy temperature monitoring in the Brazilian Atlantic Forest

Forest canopies have an important influence on the global climate balance. Through the analysis of the temperature of the canopy, it is possible to infer about the physiological aspects of the plants, helping to understand the behavior of the vegetation and, consequently, in the environmental monitoring and management of green areas. This study aims to validate the MOD11A2 V006 product from canopy surface temperature data obtained by an infrared radiation sensor. For the validation of the MOD11A2 product, a comparative analysis was performed between the land surface temperature (LST) data, obtained by the MODIS sensor, and the canopy temperature data, obtained by the SI-111 infrared radiation sensor coupled to the Itatiaia National Park (PNI) micrometeorological tower. Meteorological variables and land surface temperature collected from January to December 2018 in the PNI were also analyzed. The results reveal that the MOD11A2 product overestimates the canopy temperature in the daytime (MB ranging from 1.56 to 3.57 °C) and underestimates in the night time (MB ranging from - 0.18 to - 4.22 °C). During daytime, the months corresponding to the dry season presented a very high correlation (r = 0.74 and 0.86) and the highest values for the Willmott index (d = 0.70 and 0.64). At nighttime, the MOD11A2 product did not present a good performance for the LST estimation, especially in the rainy season. Therefore, we observed that the MOD11A2 product has limitations to estimate the land surface temperature and that possible changes in the algorithm of this product can be performed for high atmospheric humidity conditions.

Evaluation of Infrared Canopy Temperature Data in Relation to Soil Water-Based Irrigation Scheduling in a Humid Subtropical Climate

HighlightsCorn response to irrigation was influenced by the precipitation distribution in 2018 and 2019, and that impacted the response of CWSI as an irrigation scheduling signaling method.CWSI was sensitive to changes in soil water storage, increasing due to crop evapotranspiration and decreasing after a precipitation or irrigation event.In 2018, both seasonal CWSI and yield were not different among the irrigation treatments, while in 2019, seasonal CWSI and yield were all statistically different among the treatments evaluated.Post analysis of canopy and air temperature indicated that the temperature-time threshold (TTT) method might not appropriately signal crop water stress in a humid environment.Abstract. Irrigation scheduling based on the crop water stress index (CWSI) and temperature-time threshold (TTT) methods is promising for semi-arid and arid climates. The objective of this study was to investigate if CWSI and TTT methods could be used as irrigation signaling tools for a humid environment in the southeastern U.S. Corn canopy temperature data were collected in Alabama in 2018 and 2019 using infrared leaf temperature sensors on a fully irrigated treatment and on two limited irrigation treatments. A set of three soil water sensors installed at 0.15, 0.3, and 0.6 m soil depth were used to prescribe irrigation time and amount. CWSI was sensitive to precipitation, irrigation, and plant water uptake. No statistical differences in CWSI or yield among the three irrigation levels were found in 2018 when precipitation was well distributed during the season. In contrast, during 2019 both CWSI and yield differed significantly among the three irrigation treatments. Precipitation events in 2019 were sparse compared to 2018; therefore, irrigation promoted greater differences in water availability between treatments. Inconsistencies observed in potential irrigation signaling using the TTT method with or without the inclusion of a limiting relative humidity algorithm indicate that the TTT method may not be a reliable irrigation signaling tool for humid environments. Keywords: Corn yield, Crop water stress index, Irrigation scheduling, Limiting relative humidity, Soil water depletion, Temperature-time threshold.

High‐Throughput Field Phenotyping to Assess Irrigation Treatment Effects in Quinoa

Core Ideas Water stress in multiple quinoa varieties were introduced. Remote sensing technologies were evaluated to assess water stress response. Handheld and unmanned aerial system based sensors provided reliable data. Thermal imaging data could capture stress in quinoa varieties. Quinoa (Chenopodium quinoa Willd.) is a crop known for its tolerance to abiotic stress such as drought and salinity. Quinoa is also a versatile superfood, which is gluten‐free and high in protein, vitamins, nutrients, and beneficial antioxidants. Washington State University's quinoa breeding program efforts focus on identification and development of varieties that are resilient to local environmental conditions. In this study, high‐throughput phenotyping techniques were applied to evaluate the performance of quinoa varieties under different irrigation regimes. Handheld multispectral radiometer (Crop Scan), proximal sensing system using ground platform, and remote sensing with unmanned aerial system (UAS) were used to assess the performance of eight quinoa varieties under two irrigation treatments (non‐irrigated and irrigated). Crop Scan data, along with multispectral and thermal infrared images, were acquired at multiple time points at different stages of crop development during the season. In general, the normalized difference vegetation index, water band index, and green normalized difference vegetation index (GNDVI) data extracted from Crop Scan, and GNDVI and canopy temperature data extracted from UAS were able to detect irrigation treatment effects. Comparing the spectral data acquired at multiple scales indicated that Crop Scan data were highly and significantly correlated with remote sensing data (|r| = 0.57–0.85). Rapid data acquisition and the ability to detect differences among varieties under water stress highlight the application of remote sensing techniques as a high‐throughput phenotyping tool to evaluate quinoa.

Methodology for High-Throughput Field Phenotyping of Canopy Temperature Using Airborne Thermography.

Lower canopy temperature (CT), resulting from increased stomatal conductance, has been associated with increased yield in wheat. Historically, CT has been measured with hand-held infrared thermometers. Using the hand-held CT method on large field trials is problematic, mostly because measurements are confounded by temporal weather changes during the time required to measure all plots. The hand-held CT method is laborious and yet the resulting heritability low, thereby reducing confidence in selection in large scale breeding endeavors. We have developed a reliable and scalable crop phenotyping method for assessing CT in large field experiments. The method involves airborne thermography from a manned helicopter using a radiometrically-calibrated thermal camera. Thermal image data is acquired from large experiments in the order of seconds, thereby enabling simultaneous measurement of CT on potentially 1000s of plots. Effects of temporal weather variation when phenotyping large experiments using hand-held infrared thermometers are therefore reduced. The method is designed for cost-effective and large-scale use by the non-technical user and includes custom-developed software for data processing to obtain CT data on a single-plot basis for analysis. Broad-sense heritability was routinely >0.50, and as high as 0.79, for airborne thermography CT measured near anthesis on a wheat experiment comprising 768 plots of size 2 × 6 m. Image analysis based on the frequency distribution of temperature pixels to remove the possible influence of background soil did not improve broad-sense heritability. Total image acquisition and processing time was ca. 25 min and required only one person (excluding the helicopter pilot). The results indicate the potential to phenotype CT on large populations in genetics studies or for selection within a plant breeding program.

A cost-effective canopy temperature measurement system for precision agriculture: a case study on sugar beet

Increasing agricultural efficiency in a sustainable manner will contribute to feed a growing population under limited land, nutrient and water resources. Water scarcity and the increasing social concern for this resource are already requiring more sophisticated irrigation and decision-support systems. To address the heterogeneity in crop water status in a commercial field, precision irrigation requires accurate information about crops (e.g., crop water status), soil (e.g., moisture content) and weather (e.g., wind speed and vapor pressure deficit). Numerous studies have shown that plant canopy temperature can be used to derive reliable plant water stress indicators, thus making it a promising tool for irrigation water management. However, efficient and cost-effective measurement techniques are still lacking. This paper assesses the potential of infrared thermometry and thermal imaging for monitoring plant water stress in a commercial sugar beet field by comparing canopy temperature data acquired from a conventional thermal camera with an inexpensive infrared sensor, both mounted on a rotary-wing unmanned aerial vehicle (UAV). Measurements were taken at various phenological stages of the sugar beet growing season. Laboratory tests were performed to determine the key features for accurate temperature measurements and flight altitude. Experiments were conducted in 2014 and 2015 in experimental and commercial sugar beet fields in Southwestern Spain to (i) develop an affordable infrared temperature system suitable for mounting on a UAV to obtain thermal information, (ii) compare sugar beet canopy temperature measurements collected with the low-cost platform with those obtained from a conventional thermal camera, both mounted on a rotary-wing UAV, (iii) identify the factors that will limit the use of the low-cost system to derive temperature-based water stress indices. To accomplish these objectives, well-watered and deficit irrigated plots were established. Results indicated that the lightweight canopy temperature system was robust and reliable, although there were some constraints related to weather conditions and delimitation of the area covered by the infrared sensor.

Estimating Potential Transpiration of Apple Trees Using Theoretical Non-Water-Stressed Baselines

AbstractTo maximize irrigation efficiency, applied water has to be precisely adjusted to the crop water use. This study develops a method based on the energy balance of a single apple leaf to calculate potential transpiration (Ep) for the whole apple tree. The Ep model was based on two main submodels predicting canopy temperature (Tc) and total canopy conductance (gT). The gT model was derived by simplifying the energy budget to rely on only climatic data and an empirical coefficient. These submodels were evaluated using the canopy temperature data collected in a Fuji apple orchard during the 2007, 2008, and 2013 growing seasons. The applicability of the Ep model was examined on (1) well-watered, young Fuji apple trees, and (2) well-irrigated, older apple trees bearing little fruit. Predicted potential transpiration rates at both scenarios were compared with those predicted by the ASCE standardized Penman–Monteith values for alfalfa (ETr). Daily average weather data collected during the three growing seas...

Soil Moisture and Plant Canopy Temperature Sensing for Irrigation Application in Cotton

There is a need to provide technical tools to cotton producers for appropriate management of irrigation in the Mid-South region of the U.S.A wireless sensor network was deployed in a cotton field to monitor soil water status for irrigation. The network included two systems, a Decagon system and a microcontroller-based system. The Decagon system consisted of soil volumetric water-content sensors, wireless data loggers, and a central data station. Sensor data collected by each data logger were wirelessly transferred to and stored in the data station. The microcontroller-based system was designed to be a low-cost data logger for monitoring Watermark water-potential sensors. An infrared thermometer was used in the field to measure plant canopy temperature for evaluating its usefulness in detecting water stress in cotton under humid conditions. Soil water and plant canopy temperature data were collected during the 2011 cotton growing season. Results indicated that the soil water sensors were able to measure the soil water status, and the measurements recorded by the systems reflected general trends of soil water change during the growing season. Soil water content decreased at a higher rate in 15-cm and 30-cm depths than in 60-cm depth before 60 days after planting. A sharp decrease of soil water content in 60-cm depth was observed from 60 to 80 days after planting. Sensor measurements responded to effects of soil texture on available water capacity. Canopy temperature of non-irrigated plants is 2-4 C higher than that of the irrigated plants during peak time of day. Supplemental irrigation increased yield by 14.2 % in this study. System installation and maintenance procedures developed worked well in general. Some installation and operational issues were found and need to be resolved for wider field operation and user acceptance.