Empirical Crop Water Stress Index Research Articles

Comparison of CWSI and Ts-Ta-VIs in moisture monitoring of dryland crops (sorghum and maize) based on UAV remote sensing

Monitoring agricultural drought using remote sensing data is crucial for precision irrigation in modern agriculture. Utilizing unmanned aerial vehicle (UAV) remote sensing, we explored the applicability of an empirical crop water stress index (CWSI) based on canopy temperature and three-dimensional drought indices (TDDI) constructed from surface temperature (Ts), air temperature (Ta) and five vegetation indices (VIs) for monitoring the moisture status of dryland crops. Three machine learning algorithms (random forest regression (RFR), support vector regression, and partial least squares regression) were used to compare the performance of the drought indices for vegetation moisture content (VMC) estimation in sorghum and maize. The main results of the study were as follows: (1) Comparative analysis of the drought indices revealed that Ts-Ta-normalized difference vegetation index (TDDIn) and Ts-Ta-enhanced vegetation index (TDDIe) were more strongly correlated with VMC compared with the other indices. The indices exhibited varying sensitivities to VMC under different irrigation regimes; the strongest correlation observed was for the TDDIe index with maize under the fully irrigated treatment (r=−0.93). (2) Regarding spatial and temporal characteristics, the TDDIn, TDDIe and CWSI indices showed minimal differences Over the experimental period, with coefficients of variation were 0.25, 0.18 and 0.24, respectively. All three indices were capable of effectively characterizing the moisture distribution in dryland maize and sorghum crops, but the TDDI indices more accurately monitored the spatial distribution of crop moisture after a rainfall or irrigation event. (3) For prediction of the moisture content of single crops, RFR models based on TDDIn and TDDIe estimated VMC most accurately (R2>0.7), and the TDDIn-based model predicted VMC with the highest accuracy when considering multiple-crop samples, with R2 and RMSE of 0.62 and 14.26%, respectively. Thus, TDDI proved more effective than the CWSI in estimating crop water content.

Diurnal Soybean Water Stress Computed Using Statistical-Based Thermal Indices With High-Frequency Unmanned Aircraft Flights

Highlights High-frequency UAS thermal data can identify the temporal nature of the spatial canopy stress patterns for soybean. Thermal indices were calculated using the statistical approach from the lower and upper bounds of confidence interval. The CWSI Histogram Approach (UAS) was compared to the CWSI Empirical Approach (IRT). The distribution of canopy temperature (using the inter-quartile range) may be useful for irrigation management. Abstract. The use of unmanned aerial systems (UAS) in the field of irrigation management has been increasing rapidly. Due to their ability to capture multi-temporal data over the field, new techniques for the calculation of the crop water stress index (CWSI) and degrees above non-stressed (DANS) using UAS have been evolving. In this study, a statistical CWSI approach (canopy temperature histogram method) was used to identify the diurnal crop water stress patterns in soybean crop at three different study sites in Nebraska. Two study sites were located in the Eastern Nebraska Research and Extension Center (ENREC) at Mead, Nebraska, having multiple irrigation treatments; the third site was located in the South Central Agricultural Laboratory (SCAL), Clay Center, Nebraska, having one uniform irrigation treatment. Based on the results obtained, the CWSI and DANS maps exhibited a clear diurnal pattern of crop water stress response from morning to afternoon, and recovery from late afternoon to evening, with variations between the treatments at ENREC and a similar trend on SCAL. ENREC had a stronger correlation between CWSI and DANS due to the wider range in canopy temperatures from having both irrigated and rainfed plots. When compared between deficit plots at ENREC and the irrigated treatment at SCAL, the study showed that the statistical approach was more reliable when there were differences in crop water stress among different treatments. The main advantage of using the statistical CWSI histogram approach compared to the conventional empirical CWSI approach is the reduced requirement of additional meteorological parameters and faster automation time. CWSI histogram distribution graphs were created for each flight to understand the temporal changes and reveal the mean CWSI values (approximately 0.49, 0.51, and 0.49, for ENREC1, ENREC2, and SCAL, respectively) and interquartile (IQR) range for the soybean crop. For a given field site, temporal changes in IQR were greater than temporal changes in mean CWSI. Besides the mean canopy temperature, the distribution of canopy temperature (using the IQR) may be useful for irrigation management. Keywords: Irrigation Management, Precision Agriculture, Python, Remote Sensing, Thermal Imagery, Unmanned Aircraft Systems.

Real-Time Assessment of Mandarin Crop Water Stress Index.

The use of plant-based indicators and other conventional means to detect the level of water stress in crops may be challenging, due to their difficulties in automation, their arduousness, and their time-consuming nature. Non-contact and non-destructive sensing methods can be used to detect the level of water stress in plants continuously and to provide automatic sensing and controls. This research aimed at determining the viability, efficiency, and swiftness in employing the commercial Workswell WIRIS Agro R infrared camera (WWARIC) in monitoring water stress and scheduling appropriate irrigation regimes in mandarin plants. The experiment used a four-by-three randomized complete block design with 80–100% FC water treatment as full field capacity and three deficit irrigation treatments at 70–75% FC, 60–65% FC, and 50–55% FC. Air temperature, canopy temperature, and vapor pressure deficits were measured and employed to deduce the empirical crop water stress index, using the Idso approach (CWSI(Idso)) as well as baseline equations to calculate non-water stress and water stressed conditions. The relative leaf water content (RLWC) of mandarin plants was also determined for the growing season. From the experiment, CWSI(Idso) and CWSI were estimated using the Workswell Wiris Agro R infrared camera (CWSIW) and showed a high correlation (R2 = 0.75 at p < 0.05) in assessing the extent of water stress in mandarin plants. The results also showed that at an altitude of 12 m above the mandarin canopy, the WWARIC was able to identify water stress using three modes (empirical, differential, and theoretical). The WWARIC’s color map feature, presented in real time, makes the camera a suitable device, as there is no need for complex computations or expert advice before determining the extent of the stress the crops are subjected to. The results prove that this novel use of the WWARIC demonstrated sufficient precision, swiftness, and intelligibility in the real-time detection of the mandarin water stress index and, accordingly, assisted in scheduling irrigation.

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.

Cotton phenotyping and physiology monitoring with a proximal remote sensing system

AbstractSubstantial progress has been made in developing sensor‐based proximal phenotyping systems for cotton (Gossypium hirsutum L.), but research is needed to improve in‐season prediction of lint yield and to improve accuracy in monitoring crop water stress using such a system. Here, we report on results of a 2‐yr field study in which a proximal remote sensing system (measuring canopy height, spectral indices [normalized difference vegetation index, NDVI], and canopy temperature) was deployed every 2 wk over plots of eight cotton varieties at three rates of ET replacement (0, 45, and 90%). As expected, NDVI was an excellent predictor of canopy and biomass traits, including canopy height and leaf area index (LAI). The strength of correlations between in‐season sensor measurements (NDVI and the canopy‐to‐air temperature difference [Tc – Ta]) and lint yield ranged from poor to fair when analyzed by irrigation rate and excellent when analyzed across all rates. Correlations were weaker in the drier of the two years tested and NDVI was a better and more consistent predictor of yield than Tc − Ta, though multiple linear regression integrating both variables improved results by up to 9%. Combining the Tc − Ta data with onsite atmospheric weather station data allowed calculation of empirical crop water stress index (CWSI) values. Using the CWSI metric, relative differences in crop water stress were clear among ET replacement levels once the canopy width reached the threshold for focusing the infrared temperature sensor on the canopy. This indicated that cotton water stress can be successfully monitored using a proximal phenotyping system, which can be quickly and easily deployed across many plots in research or production settings.

Neural computing modelling of the crop water stress index

In this study, two artificial neural network models viz. supervised Feed-Forward Back Propagation (FF-BP) and unsupervised Kohonen Self-Organizing Map (K-SOM) have been developed to predict the Crop Water Stress Index (CWSI) using air temperature, relative humidity, and canopy temperature. Field experiments were conducted on Indian mustard to observe the crop canopy temperature under different levels of irrigation treatment during the 2017 and 2018 cropping seasons. The empirical CWSI was computed using well-watered and non-transpiring baseline canopy temperatures. The K-SOM and FF-BP CWSI predictions were compared with the empirical CWSI estimates and both performed satisfactorily. Of the two, however, the K-SOM was better with R2 (coefficient of determination) of 0.97 and 0.96 for model development and validation, respectively; corresponding values for FF-BP were 0.86 and 0.75. The results of the study suggest that neural network modelling offers significant potential for reliable prediction of the CWSI, which can be utilized in irrigation scheduling and crop stress management.

Simplified Evaluation of Cotton Water Stress Using High Resolution Unmanned Aerial Vehicle Thermal Imagery

Irrigation water management and real-time monitoring of crop water stress status can enhance agricultural water use efficiency, crop yield, and crop quality. The aim of this study was to simplify the calculation of the crop water stress index (CWSI) and improve its diagnostic accuracy. Simplified CWSI (CWSIsi) was used to diagnose water stress for cotton that has received four different irrigation treatments (no stress, mild stress, moderate stress, and severe stress) at the flowering and boll stage. High resolution thermal infrared and multispectral images were taken using an Unmanned Aerial Vehicle remote sensing platform at midday (local time 13:00), and stomatal conductance (gs), transpiration rate (tr), and cotton root zone soil volumetric water content (θ) were concurrently measured. The soil background pixels of thermal images were eliminated using the Canny edge detection to obtain a unimodal histogram of pure canopy temperatures. Then the wet reference temperature (Twet), dry reference temperature (Tdry), and mean canopy temperature (Tl) were obtained from the canopy temperature histogram to calculate CWSIsi. The other two methods of CWSI evaluation were empirical CWSI (CWSIe), in which the temperature parameters were determined by measuring natural reference cotton leaves, and statistical CWSI (CWSIs), in which Twet was the mean of the lowest 5% of canopy temperatures and Tdry was the air temperature (Tair) + 5 °C. Compared with CWSIe, CWSIs and spectral indices (NDVI, TCARI, OSAVI, TCARI/OSAVI), CWSIsi has higher correlation with gs (R2 = 0.660) and tr (R2 = 0.592). The correlation coefficient (R) for θ (0–45 cm) and CWSIsi is also high (0.812). The plotted high-resolution map of CWSIsi shows the different distribution of cotton water stress in different irrigation treatments. These findings demonstrate that CWSIsi, which only requires parameters from a canopy temperature histogram, may potentially be applied to precision irrigation management.

Dynamic modelling of the baseline temperatures for computation of the crop water stress index (CWSI) of a greenhouse cultivated lettuce crop

The crop water stress index (CWSI) has been shown to be a tool that could be used for non-contact and real-time monitoring of plant water status, which is a key requirement for the precision irrigation management of crops. However, its adoption for irrigation scheduling is limited because of the need to know the baseline temperatures which are required for its calculation. In this study, the canopy temperature of greenhouse cultivated lettuce plants which were maintained as either well-watered or non-transpiring was continuously monitored along with prevailing environmental conditions during a five week period. This data was applied in developing a dynamic model that can be used for predicting the baseline temperatures. Input variables for the dynamic model included air temperature, shortwave irradiance, and air vapour pressure deficit measured at a 10 s interval. During a follow up study, the dynamic model successfully predicted the baseline temperatures producing mean absolute errors (MAE) that varied between 0.17 °C and 0.29 °C, and root mean squared errors (RMSE) that varied between 0.21 °C and 0.35 °C when comparing model predictions with measured values. The model predicted baseline temperatures were applied in calculating an empirical CWSI for lettuce plants receiving one of two irrigation treatments. The empirical CWSI consistently differentiated between the irrigation treatments and was significantly correlated with the theoretical CWSI with correlation coefficient (r) values greater than 0.9. The dynamic model presented in this study requires easily measured input parameters for the prediction of the baseline temperatures. This eliminates the need to maintain artificial reference surfaces required in other empirical approaches for the CWSI calculation and also eliminates the need for computing the complex theoretical CWSI.

Daylight crop water stress index for continuous monitoring of water status in apple trees

We studied the suitability of empirical crop water stress index (CWSI) averaged over daylight hours (CWSId) for continuous monitoring of water status in apple trees. The relationships between a midday CWSI (CWSIm) and the CWSId and stem water potential (ψ stem), and soil water deficit (SWD) were investigated. The treatments were: (1) non-stressed where the soil water was close to field capacity and (2) mildly stressed where SWD fluctuated between 0 and a maximum allowable depletion (MAD of 50 %). The linear relationship between canopy and air temperature difference (ΔT) and air vapor pressure deficit (VPD) averaged over daylight hours resulted in a non-water-stressed baseline (NWSBL) with higher correlation (∆T = −0.97 VPD – 0.46, R 2 = 0.78, p < 0.001) compared with the conventional midday approach (∆T = −0.59 VPD – 0.67, R 2 = 0.51, p < 0.001). Wind speed and solar radiation showed no significant effect on the daylight NWSBL. There was no statistically meaningful relationship between midday ψ stem and CWSIm. The CWSId agreed well with SWD (R 2 = 0.70, p < 0.001), while the correlation between SWD and CWSIm was substantially weaker (R 2 = 0.38, p = 0.033). The CWSId exhibited high sensitivity to mild variations in the soil water content, suggesting it as a promising indicator of water availability in the root zone. The CWSId is stable under transitional weather conditions as it reflects the daily activity of an apple crop.

An insight to the performance of crop water stress index for olive trees

Optimization of olive oil quantity and quality requires finely tuned water management, as increased irrigation, up to a certain level, results in increasing yield, but a certain degree of stress improves oil quality. Monitoring tools that provide accurate information regarding orchard water status would therefore be beneficial. Amongst the various existing methods, those having high resolution, either temporally (i.e., continuous) or spatially, have the maximum adoption potential. One of the commonly used spatial methods is the Crop Water Stress Index (CWSI). The objective of this research was to test the ability of the CWSI to characterize water status dynamics of olive trees as they enter into and recover from stress, and on a diurnal scale. CWSI was tested in an empirical form and in two analytical configurations. In an experiment conducted in a lysimeter facility in the northwestern Negev, Israel, irrigation was withheld for 6 days for 5 of 15 trees, while daily irrigation continued for the rest of the trees. After resuming irrigation, the trees were monitored for 5 additional days. Water status measurements and thermal imaging were conducted daily between 12:00 and 14:00. Diurnal monitoring (predawn to after dusk) of the same indicators was conducted on the day of maximum stress. Continuous meteorological data were acquired throughout the experimental period. Empirical and analytical CWSI were calculated based on canopy temperature extracted from thermal images. The empirical CWSI differentiated between well watered and stressed trees, and depicted the water status dynamics during the drought and recovery periods as well as on a diurnal scale. Analytical approaches did not perform as well at either time scale. In conclusion, the empirical CWSI seems to be promising even given its limitations, while analytical forms of CWSI still require improvement before they can be used as a water status monitoring tool for olive orchards. Practically, it is proposed to compute the wet temperature analytically and set the dry temperature to 5°C higher than air temperature.

Using radiation thermography and thermometry to evaluate crop water stress in soybean and cotton

Abstract The use of digital infrared thermography and thermometry to investigate early crop water stress offers a producer improved management tools to avoid yield declines or to deal with variability in crop water status. This study used canopy temperature data to investigate whether an empirical crop water stress index could be used to monitor spatial and temporal crop water stress. Different irrigation treatment amounts (100%, 67%, 33%, and 0% of full replenishment of soil water to field capacity to a depth of 1.5 m) were applied by a center pivot system to soybean ( Glycine max L.) in 2004 and 2005, and to cotton ( Gossypium hirsutum L.) in 2007 and 2008. Canopy temperature data from infrared thermography were used to benchmark the relationship between an empirical crop water stress index (CWSI e ) and leaf water potential ( Ψ L ) across a block of eight treatment plots (of two replications). There was a significant negative linear correlation between midday Ψ L measurements and the CWSI e after soil water differences due to irrigation treatments were well established and during the absence of heavy rainfall. Average seasonal CWSI e values calculated for each plot from temperature measurements made by infrared thermometer thermocouples mounted on a center pivot lateral were inversely related to crop water use with r 2 values >0.89 and 0.55 for soybean and cotton, respectively. There was also a significant inverse relationship between the CWSI e and soybean yields in 2004 ( r 2 = 0.88) and 2005 ( r 2 = 0.83), and cotton in 2007 ( r 2 = 0.78). The correlations were not significant in 2008 for cotton. Contour plots of the CWSI e may be used as maps to indicate the spatial variability of within-field crop water stress. These maps may be useful for irrigation scheduling or identifying areas within a field where water stress may impact crop water use and yield.

Towards using a thermal infrared index combined with water balance modelling to monitor sugarcane irrigation in a tropical environment

Abstract In humid regions, the timing and quantity of a complementary irrigation regime is challenging because of the irregularity of rainfalls events. In this study, we tested the use of a thermal infrared derived empirical crop water stress index (CWSIe) as an in situ measurement of the water status of sugarcane, to better monitor the irrigation scheduling. To do this, we set up a 2-year experiment in Reunion Island, on a trial with plots under different water conditions (rainfed and irrigated). Crop surface temperature was measured daily with infrared radiometers (Apogee Instruments) installed above the canopy, and soil moisture and drainage measurements were used to derive the ratio between actual and maximum evapotranspiration (AET/MET) values that were then averaged on “hydrically homogeneous” time periods (between 7 and 25 days). Only the thermal data acquired on clear days and 1 h after noon in 2007 were used to define the empirical lower and upper baselines required for the calculation of empirical CWSI. The data set acquired in 2008 was used to test the robustness of the method as we used the upper and lower baselines defined in 2007 to calculate CWSIe. The linear regression between AET/MET and (1 − CWSIe) averaged on the same periods (values ranging between 0.4 and 1) showed a significant correlation for both experimental years (global R2 = 0.75 and RMSE = 0.12). This result indicates the effectiveness of the CWSIe to measure the water status of the sugarcane crop, even in humid conditions with a vapor pressure deficit (VPD) between 0.5 and 2.1. We conclude the study by discussing the complementarity of this remote water stress index (CWSIe) with OSIRI water balance modelling tool currently used in Reunion Island for monitoring sugarcane crop irrigation.

Evaluating water stress in irrigated olives: correlation of soil water status, tree water status, and thermal imagery

Irrigation of olive orchards is challenged to optimize both yields and oil quality. Best management practices for olive irrigation will likely depend on the ability to maintain mild to moderate levels of water stress during at least some parts of the growing season. We examined a number of soil, plant and remote sensing parameters for evaluating water stress in bearing olive (var. Barnea) trees in Israel. The trees were irrigated with five water application treatments (30, 50, 75, 100 and 125% of potential evapotranspiration) and the measurements of soil water content and potential, mid-day stem water potential, and stomatal resistance were taken. Remote thermal images of individual trees were used to alternatively measure average canopy temperature and to calculate the tree’s crop water stress index (CWSI), testing empirical and analytical approaches. A strong non-linear response showing similar trends and behavior was evident in soil and plant water status measurements as well as in the CWSI, with decreasing rates of change at the higher irrigation application levels. No statistically significant difference was found between the analytical and the empirical CWSI, suggesting that the relative simplicity of the analytical method would make it preferable in practical applications.

NON-WATER-STRESSED BASELINES FOR CALCULATING CROP WATER STRESS INDEX (CWSI) FOR ALFALFA AND TALL FESCUE GRASS

The lack of transferability of the Crop Water Stress Index (CWSI) baselines, together with the restriction of having to make required measurements close to noon and under clear-sky conditions, are major drawbacks that restrict the use of the empirical CWSI method for irrigation scheduling. The objectives of this study were to: (1) evaluate the effect of solar radiation (Rs) on the non-water-stressed baselines (NWSBs) of alfalfa (Medicago sativa L.) and tall fescue grass (Festuca arundinacea), and (2) develop empirical equations to estimate their NWSBs, which could be applied at any time during the daytime cycle and under conditions of full canopy cover. A Bowen ratio system was used to measure 20 min averages of radiometric surface temperature, air temperature (Ta), wind speed (u2), dew point, and Rs over the two crop canopies during the 1991 growing season at Kimberly, Idaho. Using this dataset, empirical NWSBs for different Rs ranges were derived, which tended to diverge from each other as vapor pressure deficit (VPD) increased, indicating that Rs considerably affected the baselines and that its effect was more pronounced as the air got drier. Multiple regression analysis was also used to develop equations to estimate the NWSBs for the entire daytime cycle and specifically for the near-noon period. For alfalfa, the equation derived for the entire daytime cycle estimated the NWSBs as a function of Rs, VPD, Ta, u2, and plant canopy height (h) (r2 = 0.89). For grass, the equation only included Rs, VPD, Ta, and u2 (r2 = 0.89). For alfalfa, the near-noon equation included Rs, VPD, Ta, u2, and h (r2 = 0.92). For grass, on the other hand, Ta and h were not statistically significant, and the near-noon equation only included VPD, Rs, and u2 (r2 = 0.94). Since all variables that significantly affected the NWSBs for these crops were included in the equations, we expect them to be transferable to other locations; however, additional testing at other locations is needed to confirm this hypothesis.

Evaluation of a crop water stress index for detecting water stress in winter wheat in the North China Plain

Canopy temperature measured with infrared thermometers or other remote infrared sensors is an important tool for detecting crop water stress. The crop water stress index (CWSI) is the most often used index which is based on canopy temperature to detect crop water stress. This study evaluates the application of three different forms of CWSI for winter wheat water stress monitoring in the North China Plain (NCP): the Idso empirical model, the Jackson theoretical model, and the new Alves model, which replaces the radiometric surface temperature with a surface “wet bulb” temperature, thereby avoiding the evaluation of the surface resistance of the crop. The results show that the CWSI based on Jackson’s definition and Alves’ definition are better than the empirical CWSI for monitoring winter wheat water stress in NCP. Both definitions are useful tools to evaluate winter wheat water stress in NCP, but the CWSI based on Alves’ definition is more practical for monitoring winter wheat water stress in NCP, mainly due to not having to estimate the crop surface resistance, while the CWSI based on Jackson’s definition is more reasonable for quantifying winter wheat water stress in NCP.

Evaluation of Resistances for Bermudagrass Turf Crop Water Stress Index Models

AbstractEvaluation of the Penman‐Monteith‐based crop water stress index (CWSI), as a potential indicator of turf irrigation timing, requires reliable estimates of potential (minimum) canopy resistance, rcp, and aerodynamic resistance, ra. This paper compares various methods of estimating rcp and ra for evaluation of water stress of bermudagrass turfgrass (Cynodon dactylon cv. Midiron) in the desert Southwest of the USA. First, two empirical CWSI models were developed using field data collected from well‐watered and severely water‐stressed turf plots during the 1986 growing season in Tucson, AZ. Second, the regression constants in the empirical lower limits of CWSI (canopy temperature minus air temperature, Tc − Ta, of nonwater‐stressed turf) and the parameters associated with similar terms (net radiation and vapor pressure deficit) in the Penman‐Monteith equation were equated, thus solving for rcp and ra. This yielded mean rcp and ra values of 79 and 13 s m−1, respectively. Using these estimates, the predicted lower limits of Tc − Ta from the Penman‐Monteith equation were in good agreement with values measured over well‐watered turf. However, the predicted Tc − Ta for severely water‐stressed turf agreed very poorly with the measured values. The use of the log‐law equation yielded independent estimates of ra generally greater than 45 s m−1 and resulted in erroneous estimates of Tc − Ta by the Penman‐Monteith equation. Using a crop‐based method and data from the turf plots, estimates of the resistances were made in which constant rcp and ra values of 62.5 and 20 s m−1, respectively, were found. The crop‐based method was found to be not only simple to use, but also performed satisfactorily in predicting Tc − Ta. A well‐defined curvilinear relationship between estimates of midday canopy resistance (rc) and CWSI was determined for a wide range of turf water stress, implying that these two estimates nearly equally reflect the state of turf water stress. Our findings also suggest threshold midday CWSI and rc values for short bermudagrass turf of 0.16 and 125 s m−1, respectively, to indicate the need for irrigation.

How the nitrogen problem has been solved

The crop water stress index (CWSI) has been shown to be a tool that could be used for non-contact and real-time monitoring of plant water status, which is a key requirement for the precision irrigation management of crops. However, its adoption for irrigation scheduling is limited because of the need to know the baseline temperatures which are required for its calculation. In this study, the canopy temperature of greenhouse cultivated lettuce plants which were maintained as either well-watered or non-transpiring was continuously monitored along with prevailing environmental conditions during a five week period. This data was applied in developing a dynamic model that can be used for predicting the baseline temperatures. Input variables for the dynamic model included air temperature, shortwave irradiance, and air vapour pressure deficit measured at a 10 s interval. During a follow up study, the dynamic model successfully predicted the baseline temperatures producing mean absolute errors (MAE) that varied between 0.17 °C and 0.29 °C, and root mean squared errors (RMSE) that varied between 0.21 °C and 0.35 °C when comparing model predictions with measured values. The model predicted baseline temperatures were applied in calculating an empirical CWSI for lettuce plants receiving one of two irrigation treatments. The empirical CWSI consistently differentiated between the irrigation treatments and was significantly correlated with the theoretical CWSI with correlation coefficient (r) values greater than 0.9. The dynamic model presented in this study requires easily measured input parameters for the prediction of the baseline temperatures. This eliminates the need to maintain artificial reference surfaces required in other empirical approaches for the CWSI calculation and also eliminates the need for computing the complex theoretical CWSI.