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Guayule (Parthenium argentatum Gray, cv. N565 II) was grown at Phoenix, Arizona, in well-watered and water-stressed conditions. The relationships between the crop water stress index (CWSI) and other physiological indicators of plant water status were examined. The drought treatment lasted for 70 days (29 May through 5 August 1986) during which time the available soil moisture content declined from 100 to 0%, while the well-watered plants were maintained at greater than 70% available soil moisture. The CWSI results were normalized to reduce environmental effects by subtracting the values obtained for the well-watered plants from those of the water-stressed plants. Significant (P

A plant water stress index has recently been developed which employs a radiometric measurement of foliage temperature and a psychometric measurement of the vapor pressure deficit of the air. To utilize the index, it is necessary to know the relationship that exists between foliage—air temperature differential and air vapor pressure deficit for the plant in question when it is well watered and transpiring at the potential rate. This information is provided for 26 different species for clear sky conditions in the format of non-water-stressed baselines. For six of these plants, including one aquatic species, such information is also included for cloudy or shaded conditions; and two grain crops have results presented for both pre-heading and post-heading growth stages.

A crop water stress index (CWSI) was derived from air temperatures, air vapor pressure deficits and the midday radiant leaf temperatures of cotton plants that were exposed to different early-season irrigation treatments at Phoenix, AZ, U.S.A. To calculate the CWSI, an infrared thermometer was used to measure leaf temperatures which were then scaled relative to minimum and maximum temperatures expected for no-stress (CWSI=0) and extreme drought-stress conditions (CWSI=1). Results showed the CWSI behaved as expected, dropping to low levels following an irrigation and increasing gradually as the cotton plants depleted soil moisture reserves. The final yield of seed cotton was significantly inversely correlated with the average CWSI observed over the interval from the appearance of the first square until two weeks following the final irrigation.

Guayule plant temperature measurement with infrared thermometers was investigated as a means for estimating water stress in guayule. Crop water stress behavior of the drought tolerant guayule plant was found to be similar to that developed for other economic crops using similar techniques. Thus, stress measurement and management will be useful tools for scheduling irrigations. The method was found to be ideally suited for determining the onset, duration and relief of moisture stress.

Canopy temperature in differentially irrigated and fertilized wheat plots were collected by hand held infrared thermometer from seedling emergence to maturity for two growing seasons (1981–82 and 1982–83). Canopy temperature indices like stress degree day (SDD) and crop water stress index (CWSI) based four-parameter (crop growth stage partitioned) and two-parameter (Non-partitioned) yield models suitable for remote sensing application were developed and tested with observed yield data. From statistical analysis of the models it was concluded that crop growth stage partitioned CWSI or SDD yield model was better than non-partitioned SDD models for predicting wheat grain as well as biological yields.

Degagement des limites superieures de Tf-Ta (temperature du feuillage, temperature de l'air ambiant) derivees selon Idso etal. et mesurees au champ. Effets de divers facteurs de milieu, principalement la vitesse du vent sur les limites inferieures et superieures de Tf-Ta et de l'indice de contrainte hydrique de la culture (CSWI). Comparaison du potentiel hydrique foliaire, de Tf-Ta et du CSWI dans des cultures (sorgho, mais, haricots, cotonniers)

Many parts of the world are totally dependent on irrigation to produce plentiful crops. Some areas use irrigation as a supplement to rainfall and others use irrigation only to assure that crop yields won’t be affected by a lack of rainfall for a few weeks.

Canopy surface temperature measured with infrared thermometers or other remote IR sensor is an important tool for crop water stress monitoring. Crop Water Stress Index (CWSI) is the most often used index to detect crop water stress based on canopy surface temperature. The calculation of CWSI relies on two baselines: the non-water-stressed baseline, and the maximum stressed baseline. This paper evaluates the application of the most often used non-water-stressed baselines in North China Plain (NCP): the Idso's empirical baseline, and the Jackson's theoretical baseline. The results show that the Jackson's model makes better than the Idso's in monitoring winter wheat water stress in NCP.

Canopy temperature has been recognized as a crop water stress indicator, since it reflects the interaction of crops with soil and the atmosphere. Even though there are several methods to estimate canopy temperature, most of them are time consuming, expensive, inaccurate, or require considerable human input. This work mobilizes Convolutional Neural Networks (CNNs) to identify sunlit leaves and in conjuction with thermal imagery, find the underlying leaf temperatures and calculate the Crop Water Stress Index (CWSI). The results of two different CNN architectures (FRRN, DeepLabV3) have been compared with two minimum input, state of the art methods, namely temperature Histogram Gradient Thresholding and Gaussian Mixture Models. We evaluate our approaches against this baseline using our own dataset of 1432 image-label pairs of pistachio trees. Results indicate that CNNs outperform existing methods. Our dataset is released for the scientific community to use. Finally, a web application was developed, so that researchers/growers can calculate the CWSI in real time, using pictures captured with a thermal camera., Η θερμοκρασία του φυτικού θόλου έχει αναγνωριστεί ως ένας δείκτης καταπόνησης νερού στις καλλιέργειες, καθώς ανακλά την αλληλεπίδραση των καλλιεργειών με το χώμα και την ατμόσφαιρα. Παρά το γεγονός ότι υπάρχουν διάφορες μέθοδοι για τον υπολογισμό της θερμοκρασίας του θόλου, οι περισσότερες από αυτές είναι χρονοβόρες, ακριβές, ανακριβείς, ή απαιτούν σημαντική ανθρώπινη παρέμβαση. Η παρούσα έρευνα χρησιμοποιεί συνελικτικά νευρωνικά δίκτυα (CNNs) για την αναγνώριση των ηλιοφώτιστων φύλλων, και σε συνεργασία με τη χρήση θερμικών εικόνων, βρίσκει τις υποκείμενες θερμοκρασίες των φύλλων και υπολογίζει το δείκτη καταπόνησης νερού της καλλιέργειας (CWSI). Τα αποτελέσματα δύο διαφορετικών αρχιτεκτονικών συνελικτικών νευρωνικών δικτύων (FRRN, DeepLabV3) έχουν συγκριθεί με δύο σύγχρονες μεθόδους ελάχιστης ανθρώπινης παρέμβασης, temperature Histogram Gradient Thresholding και Gaussian Mixture Models. Οι προσεγγίσεις μας εξετάζονται χρησιμοποιώντας ως γνώμονα αυτές τις δύο μεθόδους. Η αξιολόγηση γίνεται πάνω στο δικό μας dataset, το οποίο αποτελέιται απο 1432 ζευγάρια image-label φυστικιών. Τα αποτελέσματα δείχνουν πως τα συνελικτικά νευρωνικά δίκτυα λειτουργούν καλύτερα από τις υπάρχουσες μεθόδους. Το dataset δημοσιεύεται για χρήση από την επιστημονική κοινότητα. Τέλος, αναπτύχθηκε μία διαδικτυακή εφαρμογή, ώστε οι ερευνητές/καλλιεργητές να μπορούν να υπολογίζουν το CWSI σε πραγματικό χρόνο, χρησιμοποιώντας εικόνες από μια θερμική κάμερα.

In viticulture, detailed spatial information about actual evapotranspiration (ETa) and vine water status within a vineyard may be of particular utility when applying site-specific, precision irrigation management. Over recent decades, extensive research has been carried out in the use of remote sensing energy balance models to estimate and monitor ETa at the field level. However, one of the major limitations remains the coarse spatial resolution in the thermal infrared (TIR) domain. In this context, the recent advent of the Sentinel missions of the European Space Agency (ESA) has greatly improved the possibility of monitoring crop parameters and estimating ETa at higher temporal and spatial resolutions. In order to bridge the gap between the coarse-resolution Sentinel-3 thermal and the fine-resolution Sentinel-2 shortwave data, sharpening techniques have been used to downscale the Sentinel-3 land surface temperature (LST) from 1 km to 20 m. However, the accurate estimates of high-resolution LST through sharpening techniques are still unclear, particularly when intended to be used for detecting crop water stress. The goal of this study was to assess the feasibility of the two-source energy balance model (TSEB) using sharpened LST images from Sentinel-2 and Sentinel-3 (TSEB-PTS2+3) to estimate the spatio-temporal variability of actual transpiration (T) and water stress in a vineyard. T and crop water stress index (CWSI) estimates were evaluated against a vine water consumption model and regressed with in situ stem water potential (Ystem). Two different TSEB approaches, using very high-resolution airborne thermal imagery, were also included in the analysis as benchmarks for TSEB-PTS2+3. One of them uses aggregated TIR data at the vine+inter-row level (TSEB-PTairb), while the other is based on a contextual method that directly, although separately, retrieves soil and canopy temperatures (TSEB-2T). The results obtained demonstrated that when comparing airborne Trad and sharpened S2+3 LST, the latter tend to be underestimated. This complicates the use of TSEB-PTS2+3 to detect crop water stress. TSEB-2T appeared to outperform all the other methods. This was shown by a higher R2 and slightly lower RMSD when compared with modelled T. In addition, regressions between T and CWSI-2T with Ystem also produced the highest R2.