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As irrigation water decreases, crop transpiration also decreases, resulting in an increase in leaf temperature and an increase of the crop water stress index (CWSI). Therefore, in the study, the response of Capsicum to different irrigation regimes was evaluated with periodic measurements of leaf temperature, stomatal conductance and estimation of the CWSI. The treatments consisted of a complete drip irrigation (RO) and deficit irrigation (RD-78 and RD-58) that received 78% and 58% of the complete irrigation, applying a total volume of water of 3363, 2618 and 1956 m3 ha-1 for RO, RD-78 and RD-58, respectively; and obtaining maximum yields of 30.2, 13.9 and 12.9 t ha-1 with CWSI of 0.50, 0.62 and 0.54 in C. bacatum, C. annumm and C. chinense, respectively. The highest stomatal conductance values ​​ranged from 724 to 887 mmol s-1 m-2, with CWSI from 0.1 to 0.3. The CWSI value of 0.3 (soil moisture from 25 to 33%) results in a good indicator of the time of irrigation, and it might be a criterion to be incorporated to save water and improve the management of Capsicum irrigation programs. A medida que disminuye el agua de riego, la transpiración del cultivo disminuye, resultando en un incremento de la temperatura foliar y un aumento del índice de estrés hídrico del cultivo (IEHC). Por ello, en el estudio, se evaluó la respuesta del Capsicum a diferentes regímenes de riego, con mediciones periódicas de la temperatura de la hoja, la conductancia estomática y la estimación del IEHC. Los tratamientos consistieron en un riego por goteo completo (RO) y riego con déficit (RD-78 y RD-58) que recibieron el 78% y 58% del riego completo, aplicándose un volumen total de agua de 3363, 2618 y 1956 m3 ha-1 para RO, RD-78 y RD-58, respectivamente; and obteniéndose rendimientos máximos de 30,2, 13,9 y 12,9 t ha-1 con IEHC de 0,50, 0,62 y 0,54 en C. bacatum, C. annumm y C. chinense, respectivamente. Los mayores valores de conductancia estomática variaron de 724 a 887 mmol s-1 m-2, con IEHC de 0,1 a 0,3. El valor IEHC de 0,3 (humedad del suelo del 25% al 33%), resulta en un buen indicador del momento del riego, criterio a incorporar con la finalidad de ahorrar agua y mejorar la gestión de programas de riego del Capsicum.

Due to the limited availability of Root-Zone Soil Moisture (RZSM) information at the regional scale, this paper explores the use of thermal infrared remote sensing to estimate RZSM in agricultural ...

Structural–physiological factors affect the accurate estimation of vegetation gross primary production (GPP) under various types of environmental stress. Solar-induced chlorophyll fluorescence (SIF), which is directly linked to photosynthesis, has been effectively used to estimate and monitor GPP. However, understanding of the physiological mechanism linking SIF to GPP under stress remains limited. In this article, the link between SIF and GPP at diurnal and seasonal timescales was explored for a maize field and its response to drought stress (as defined by the crop water stress index, CWSI) using three-years of continuous tower-based measurements was investigated. The results show that the ratio of GPP to SIF decreased with increasing drought stress levels, and the canopy stomata conductance (Gs) declined synchronously. Compared to two canopy structural factors (NDVI and NIRv), both the Pearson and partial correlation coefficients for the relationship between Gs and the ratio of GPP to the total SIF was higher (0.38, p < 0.01 and 0.32, p < 0.01 with photosynthetically active radiation as the control variable, respectively). We also found that ${{\bf \Phi }_F}$ tracked the changes in LUE well under drought conditions (CWSI > 0.6), which demonstrated that SIF can be a powerful parameter for estimating GPP under drought stress. However, there was a smaller drop in ${{\bf \Phi }_F}$ under drought stress (slope = –0.002) compared to the slope for the relationship between LUE and CWSI (–0.08). The response of light reactions to drought stress may be muted compared to the stomatal response. These findings confirm that the Gs is sensitive to drought and is important for the SIF-based GPP estimation model. It also provides reliable evidence that SIF data include a large amount of physiological information and can serve as a potential indicator for detecting drought and estimating GPP.

With the advent of optical sensors, thermal-based indicators can be retrieved at multiscale levels from handheld devices to satellite platforms, providing a low-cost method to mirror plant water st...

Xylella fastidiosa (Xf) is a harmful plant pathogenic bacterium, able to infect over 500 plant species worldwide. Successful eradication and containment strategies for harmful pathogens require large-scale monitoring techniques for the detection of infected hosts, even when they do not display visual symptoms. Although a previous study using airborne hyperspectral and thermal imagery has shown promising results for the early detection of Xf-infected olive (Olea europaea) trees, further work is needed when adopting these techniques for large scale monitoring using multispectral cameras on board airborne platforms and satellites. We used hyperspectral and thermal imagery collected during a two-year airborne campaign in a Xf-infected area in southern Italy to assess the performance of spectrally constrained machine-learning algorithms for this task. The algorithms were used to assess multispectral bandsets, selected from the original hyperspectral imagery, that were compatible with large-scale monitoring from unmanned platforms and manned aircraft. In addition, the contribution of solar–induced chlorophyll fluorescence (SIF) and the temperature-based Crop Water Stress Index (CWSI) retrieved from hyperspectral and thermal imaging, respectively, were evaluated to quantify their relative importance in the algorithms used to detect Xf infection. The detection performance using support vector machine algorithms decreased from ∼80% (kappa, κ = 0.42) when using the original full hyperspectral dataset including SIF and CWSI to ∼74% (κ = 0.36) when the optimal set of six spectral bands most sensitive to Xf infection were used in addition to the CWSI thermal indicator. When neither SIF nor CWSI were used, the detection yielded less than 70% accuracy (decreasing κ to very low performance, 0.29), revealing that tree temperature was more important than chlorophyll fluorescence for the Xf detection. This work demonstrates that large-scale Xf monitoring can be supported using airborne platforms carrying multispectral and thermal cameras with a limited number of spectral bands (e.g., six to 12 bands with 10 nm bandwidths) as long as they are carefully selected by their sensitivity to the Xf symptoms. More precisely, the blue (bands between 400 and 450 nm to derive the NPQI index) and thermal (to derive CWSI from tree temperature) were the most critical spectral regions for their sensitivity to Xf symptoms in olive., Data collection was partially supported by the European Union’s Horizon 2020 research and innovation program through grants to the POnTE (Pest Organisms threatening Europe; grant 635646 from European Union’s Horizon 2020 Framework Research Programme) and XF-ACTORS (Xylella fastidiosa Active Containment Through a Multidisciplinary-Oriented Research Strategy; grant 727987 from European Union’s Horizon 2020 Framework Research Programme) projects. A. Hornero was supported by a research fellowship DTC GEO 29 “Detection of global photosynthesis and forest health from space” from the Science Doctoral Training Centre (Swansea University, UK).

The aim of this study was to find out the effects of crop water stress index (CWSI) and soil water content (SWC) under subsurface drip irrigation system on yield and water use efficiency. The CWSI was estimated and SWC was measured under seven irrigation strategies: sustainable deficit irrigation (DI), regulated deficit irrigation (RDI), partial root zone drying (PRD) at two levels i.e. 80% and 60% and full irrigation water (FI) (control). The results showed that, the highest SWC and CWSI during the growth season compared with FI were regulated deficit irrigation (RDI) at 80% followed by PRD at 80% whereas the lowest ones were DI at 60% and PRD at 60%. Also, the highest values of yield and water use efficiency were achieved under RDI at 80% (19.54 t ha-1 in summer and 21.55 t ha-1 in winter) followed by PRD at 80 % whereas the lowest values were achieved under DI and PRD at 60%. The winter season gave values of yield higher than the corresponding ones in obtained summer season by 17.02%.

Under the current water crisis in agriculture, irrigation methods for saving and conserving water are necessary. However, these methods must guarantee an appropriate yield with a concomitant economic benefit and a reduced environmental impact. In this study, two irrigation thresholds for irrigation timing (IT) based on thermal imagery were analyzed with the UNICA potato variety in three trials under drip (DI) and furrow (FI) irrigation during 2017–2018 in Lima, Peru. The control (T1) remained at >70% of soil field capacity. For other treatments, thresholds were defined based on stomatal conductance at light saturation (T2: 0.15 and T3: 0.05 mol H2O m−2s−1) and crop water stress index (T2: 0.4 and T3: 0.6) based on canopy temperature. An integrated index (IIN) was established for the valuation of treatments using the criteria of high fresh tuber yield (FTY) and a low total amount of irrigated water, production cost (PC), and total C emissions (TE) and using criteria of a score. FI-T2 (0.69–0.72) and DI-T3 (0.19–0.29) showed the highest and lowest IIN value, respectively. FTY in T2 was not significantly reduced under FI, resulting in a lower PC regarding DI–T2 and emphasizing the usefulness of thermal imagery in determining watering schedules in potatoes under furrow irrigation systems.

Relationship between canopy temperature and soil moisture is important for using the potential of canopy temperature as an indicator of crop water stress. A two-year field experiment was carried ou...

This paper evaluates the usefulness of the Crop Water Stress Index (CWSI) for monitoring transpiration and water status in almond trees, and proposes a methodology for assessing crop yield derived from the relation between canopy temperature and transpiration. For this purpose, a Non-Water Stress Baseline (NWSB) was developed from canopy temperature measurements taken with Infrared Thermometers (IRT) installed permanently over well-watered trees for three years. Tree transpiration was measured continuously with sap flow probes installed in the same trees than the IRT sensors. The calculated CWSI was closely related to water potential and stomatal conductance measured during kernel filling, as well as with transpiration and the ratio kT/GC (the transpiration coefficient over the ground cover). Taking into consideration this relation and the water production function recently published, the seasonal CWSI was compared to final yield and the regression yielded good results (R2 = 0.80). An empirical relationship between the CWSI acquired remotely from two flights performed during the kernel filling stage and crop yield was determined for this orchard. The estimated yield from the proposed methodology was compared to ground-truth measurements of crop yield measured in 80 trees during 2014 and 2015. The result obtained a RMSE that yielded 1.54 kg/tree. This study thus demonstrates that CWSI is closely related to the transpiration and the ratio kT/GC. This relation settles the basis for the development of methodologies for estimating water-limited crop yield from thermal derived information., Financial support for this work was provided by Junta de Andalucia (P12-AGR-2521), by the Spanish Ministry of Science and Innovation (AGL2015-66141-R)), by IRIDA project financed under the ERA-NET Cofund WaterWorks 2014 and by project INNOVA-NUTS (AVA.AVA201601.18) funded by the European Regional Development Fund (FEDER).

Smartphones have several advantages over specialist monitoring systems including ubiquity, price, and ease of implementing updates. Thermal imaging can be used to assess plant water status and allow more informed irrigation decisions; unfortunately, this technique has not been widely adopted due to the high cost of equipment and the lack of a system to provide analysis and results in real-time. Several inexpensive thermal cameras that connect to smartphones have recently been released and one of these (FLIR One) was evaluated as part of a system to assess grapevine water status. Irrigation treatments were established on Cabernet Sauvignon and Chardonnay vines in an arid region. Thermal images were taken from the shaded side of the grapevine canopy and software was developed to automatically determine the temperature of the canopy and artificial reference leaves. The temperature readings and metrological inputs were used to calculate five indices of water status including the Crop Water Stress Index (CWSI) and the stomatal conductance index. The best performing was the CWSI, which does not require input from a weather station. Over 30 days of assessment, and a range of irrigation levels, measurements collected with the thermal camera were correlated with stem water potential (R2 = 0.61) and stomatal conductance (R2 = 0.74). Windy conditions appeared to be the major cause of variation between CWSI and stomatal conductance. Inexpensive thermal cameras have the potential to be an easy and accessible tool for the assessment of plant water status and to make better irrigation decisions.

An efficient management of water relies on the correct estimation of tree water requirements and the accurate monitoring of tree water status and canopy growth. This study aims to test the suitability of visible and thermal images acquired by an unmanned aerial vehicle (UAV) for monitoring tree water status and canopy growth in an irrigation experiment. We used mature olive trees of two cultivars subjected to full irrigation, deficit irrigation (41–44% of full irrigation), or rainfed conditions. Deficit irrigation had limited or no effect on fruit and oil yield. There was a significant relationship between the remotely sensed crop water stress index derived from thermal images and the stem water potential (R2 = 0.83). The RGB images by UAV allowed to estimate tree canopy volume and were able to detect differences in canopy growth across irrigation regimes. A significant relationship between canopy volume and LAI was found for both cultivars (R2 of 0.84 and 0.88 for Frantoio and Leccino, respectively). Our results confirm the positive effects of deficit irrigation strategies to save relevant volumes of water and show that aerial images from UAV can be used to monitor both tree water stress and its effects on canopy growth and yield.

The crop water stress index (CWSI) is a reliable indicator of water status in plants and has been utilized for stress monitoring, yield prediction, and irrigation scheduling. Despite this, ...

Globally, olive (Olea europaea L.) productivity is threatened by plant pathogens, particularly the fungus Verticillium dahliae (Vd) and the bacterium Xylella fastidiosa (Xf). Infections by these pathogens restrict water and nutrient flow through xylem, producing a similar set of symptoms that can also be confounded with water stress. Conventional in situ monitoring techniques are time consuming and expensive, necessitating the development of large-scale detection methods. Airborne hyperspectral and thermal imagery have been successfully used to detect both Xf and Vd infection symptoms independently, i.e., when only one of the two diseases is present. Nevertheless, the discrimination of Vd from Xf infections in contexts where both pathogens are present has not been addressed to date. This study proposes a three-stage machine learning algorithm to distinguish Vd infections from Xf infections, using a series of datasets from 27 olive orchards affected by Xf and Vd outbreaks in Italy and Spain between 2011 and 2017. Plant traits were derived from airborne hyperspectral and thermal imagery, including physiological indices from radiative transfer model inversion, Solar-induced Fluorescence emission (SIF@760), the Crop Water Stress Index (CWSI), and a selection of narrow–band hyperspectral indices. Several distinct spectral traits successfully discriminated Xf from Vd infections. The three-stage method generated a false-positive rate of 9%, an overall accuracy (OA) of 98%, and a kappa coefficient (κ) of 0.7 when identifying Vd infections using a mixed Vd + Xf dataset. When identifying Xf infections, the false-positive rate was 4%, the OA was 92%, and κ was 0.8. These results indicate that hyperspectral and thermal traits can be used to discriminate Xf from Vd infection caused by the two xylem–limited pathogens that trigger similar visual symptoms., The study was partially funded by the European Union’s Horizon 2020 Research and Innovation Programme through grant agreements POnTE (635646) and XF-ACTORS (727987), as well as by projects AGL2009-13105 from the Spanish Ministry of Education and Science, P08-AGR-03528 and P18-RT-4184 from the Regional Government of Andalusia and the European Social Fund, project E-RTA2017-00004-02 from “Programa Estatal de I + D + I Orientada a los Retos de la Sociedad” of Spain and FEDER, Intramural Project 201840E111 from CSIC.

In 1980s, Crop Water Stress Index (CWSI) is suggested to indicate the water stress of crops. CWSI is based on the leaf energy balance, which is closely related to leaf temperature. To calculate CWSI, meteorological factors such as air temperature and vapor pressure deficit should be measured besides leaf temperature. As recent technology has been developed, leaf temperature can be easily observed by thermal camera or infrared thermometer. Stomatal conductance (gs, mmol m-2 s-1) is one of the critical factors to understand crop photosynthesis and water demand. In addition, the behaviors of gs can represent the biotic and abiotic plant stresses. In abnormal condition, such as drought, insects or disease, gs getting lower. The observation of gs will make better to evaluate and predict crop growth and conditions. Therefore, the time series data of gs is useful for the monitoring of crop growth and the quick detection of abnormal crop condition in smart-farming system but there are some limitations to measure gs continuously and easily. We assume that there is some relationship between CWSI and gs because both has strong relation to leaf temperature. Thus, the aim of this study is to investigate possibility of estimation of gs using CWSI which is derived from thermal image. Through the data collected from literatures, negative correlations between CWSI and gs were revealed. The slope of correlation was changed according to crop types. In addition, as a result of simulation, there is almost linear negative relationship between CWSI and gs, and the slope was determined by maximum stomatal conductance (gs_max). Field measurement in this study was also demonstrated to identify such correlation. Further, various methods to measure CWSI were tested. This relationship will contribute to not only monitoring of crop stress for irrigation scheduling in smart farm system but also estimating evapotranspiration, photosynthesis, and crop yield.

Crop water stress indices (CWSIs) quantify plant water status based on measurement of plant temperature. The goal of CWSI formulation is to normalize measured leaf temperatures based on reference temperatures to remove sensitivity to ambient environmental conditions (e.g., air temperature, humidity, radiation), while retaining sensitivity to plant water status as reflected by stomatal conductance. This study sought to better understand the sensitivity of these temperatures to ambient environmental conditions, and ultimately how they influence various CWSIs. The surface energy balance was modeled to simulate the impacts of input parameter variation on leaf temperature and reference surface temperatures used to calculate four different CWSIs. The performance of the CWSIs were assessed based on their ability to maximize sensitivity to stomatal conductance while minimizing the relative sensitivity to ambient environmental conditions. The sensitivity analyses indicated that all four CWSIs performed poorly in shaded conditions, as they had relatively low sensitivity to stomatal conductance and were sensitive to all environmental parameters. Two CWSIs had high sensitivity to stomatal conductance, and low sensitivity to all environmental parameters except wind speed. None of CWSIs could remove sensitivity to all environmental parameters while retaining sensitivity to stomatal conductance.

To optimize crop water consumption and adopt water-saving measures such as precision irrigation, early identification of plant water status is critical. This study explores the effectiveness of estimating water stress in choy sum (Brassica chinensis var. parachinensis) grown in pots in greenhouse conditions using Crop Water Stress Index (CWSI) and crop vegetation indicators to improve irrigation water management. Data on CWSI and Spectral reflectance were collected from choy sum plants growing in sandy loam soil with four different soil field capacities (FC): 90–100% FC as no water stress (NWS); 80–90% FC for light water stress (LWS); 70–80% FC for moderate water stress (MWS); and 60–70% FC for severe water stress (SWS). With four treatments and three replications, the experiment was set up as a completely randomized design (CRD). Throughout the growing season, plant water stress tracers such as leaf area index (LAI), canopy temperature (Tc), leaf relative water content (LRWC), leaf chlorophyll content, and yield were measured. Furthermore, CWSI estimated from the Workswell Wiris Agro R Infrared Camera (CWSIW) and spectral data acquisition from the Analytical Spectral Device on choy sum plants were studied at each growth stage. NDVI, Photochemical Reflectance Index positioned at 570 nm (PRI570), normalized PRI (PRInorm), Water Index (WI), and NDWI were the Vegetation indices (VIs) used in this study. At each growth stage, the connections between these CWSIW, VIs, and water stress indicators were statistically analyzed with R2 greater than 0.5. The results revealed that all VIs were valuable guides for diagnosing water stress in choy sum. CWSIW obtained from this study showed that Workswell Wiris Agro R Infrared Camera mounted on proximal remote sensing platform for assessing water stress in choy sum plant was rapid, non-destructive, and user friendly. Therefore, integrating CWSIW and VIs approach gives a more rapid and accurate approach for detecting water stress in choy sum grown under greenhouse conditions to optimize yield by reducing water loss and enhancing food security and sustainability.

The estimation of evapotranspiration in a canopy rather than at the plant level is of interest to crop scientists. Information about the plant-water relationship at the canopy level permits better irrigation-related estimates. In addition, there is very limited proper instrumentation to measure evapotranspiration in water-stressed crops in trials under field conditions. The aim of this study was to estimate the evapotranspiration rates, canopy temperatures, Tc, crop water stress index (CWSI), and production of two soybean varieties using three water supply levels (unlimited water, 50% of water requirement in evapotranspirometers, and rainfed conditions). To date, there are no studies that have attempted to estimate the amount of water loss in stressed field-grown crops while operating converted evapotranspirometers. For each 0.1 increase in CWSI above 0.2, seed yield decreased by 434.1 ± 28.8 g m−2. Mean CWSI and Tc data were inversely related to crop water use efficiency (WUE). WUE and CWSI showed a quadratic relationship, and the economic limit of CWSI was 0.62. Tc and CWSI could serve as indicators for selecting soybean varieties with different levels of stress tolerance. However, due to the short observation period in this study, results might be site-specific and should not be extrapolated to locations with completely different climatic conditions.

Water deficit stress is a major factor that inhibits the overall growth and development in cassava (Manihot esculenta), leading to decreased storage root yield. We conducted a study to investigate whether thermal sensing could be used to indicate water deficit stress and the health and yield of cassava crops in field. The objective of the study was to use thermal imaging to determine relationship between crop water stress index (CWSI) and physiological changes, and to identify the critical CWSI point in fields of cassava cv. Rayong 9 under well-irrigated and water-deficit conditions. At the time of storage root initiation (85 DAP [day after planting]), thermal imagery was collected and the physiological changes and growth characters were measured prior to storage root harvesting (162 DAP). Thermal infrared imager was used to measure the canopy temperature and CWSI of cassava plants. Net photosynthetic rate (Pn), stomatal conductance (gs) and transpiration rates (Tr) of cassava plants under water deficit conditions for 29 d (114 DAP) were significantly decreased, leading to delayed plant growth as compared to those under well-irrigated conditions. In contrast, air vapor pressure deficit (VPDair) and CWSI in drought-stressed plants were higher than well irrigated plants. High correlations between Tr/gs/Pn and CWSI were observed. The study concludes that CWSI is a sensitive indicator of water deficit stress caused due to stomatal function. Abbreviations: CWSI: crop water stress index; DAP: day after planting; Pn: net photosynthetic rate; gs: stomatal conductance; Tr: transpiration rate; VPDair: air vapor pressure; RMSE: root mean square error

The present research was carried out to determine water-stress tolerance of linoleic sunflower genotypes (P64LE119, PR63F73, P64LL62) grown under different water stress conditions [no water-stress (I100); mild water-stress (I70); strong water-stress (I35)] in the years 2015 and 2016. Variance analyses revealed significant differences between the genotypes (P

Canopy temperature has been proposed as a relevant variable for crop water stress monitoring. Since crop temperature is highly influenced by the prevailing climatic conditions, it is usually normalized with indices such as the crop water stress index (CWSI). The index requires the use of two baselines that relate canopy temperature under maximum stress and non-water stress conditions with vapor pressure deficit (VPD). These reference baselines are specific to each crop and climatic region. In maize, they have been extensively studied for certain climatic regions but very little is known on their suitability to be used under Mediterranean-type conditions nor their temporal stability, both diurnally and between seasons. Thus, the objective of this work was to determine the reference baselines for maize grown under Mediterranean conditions, as well as its diurnal and long-term stability. An experiment was conducted for 3 years in a maize breeding field, under well-watered and water-stressed irrigation treatments. The determined reference baselines for computing CWSI in maize have shown to be stable in the long term but markedly influenced by the meteorological variations between 10–17 h UTC (Coordinated Universal Time). These results indicate that several reference baselines should be used for CWSI computing throughout the abovementioned time interval. The CWSI values calculated for well-watered and water-stressed maize breeding plots using the reference baselines derived in this study were successfully correlated with other physiological indicators of plant water stress.

The determination of crop water status has positive effects on the Chinese Brassica industry and irrigation decisions. Drought can decrease the production of Chinese Brassica, whereas over-irrigation can waste water. It is desirable to schedule irrigation when the crop suffers from water stress. In this study, a random forest model was developed using sample data derived from meteorological measurements including air temperature (Ta), relative humidity (RH), wind speed (WS), and photosynthetic active radiation (Par) to predict the lower baseline (Twet) and upper baseline (Tdry) canopy temperatures for Chinese Brassica from 27 November to 31 December 2020 (E1) and from 25 May to 20 June 2021 (E2). Crop water stress index (CWSI) values were determined based on the predicted canopy temperature and used to assess the crop water status. The study demonstrated the viability of using a random forest model to forecast Twet and Tdry. The coefficients of determination (R2) in E1 were 0.90 and 0.88 for development and 0.80 and 0.77 for validation, respectively. The R2 values in E2 were 0.91 and 0.89 for development and 0.83 and 0.80 for validation, respectively. Our results reveal that the measured and predicted CWSI values had similar R2 values related to stomatal conductance (~0.5 in E1, ~0.6 in E2), whereas the CWSI showed a poor correlation with transpiration rate (~0.25 in E1, ~0.2 in E2). Finally, the methodology used to calculate the daily CWSI for Chinese Brassica in this study showed that both Twet and Tdry, which require frequent measuring and design experiment due to the trial site and condition changes, have the potential to simulate environmental parameters and can therefore be applied to conveniently calculate the CWSI.

The crop water stress index (CWSI) was developed as a normalized index to quantify stress and overcome the effects of other environmental parameters affecting the relationship between stress and plant temperature. This index has been widely used for crop water status monitoring. Nowadays CWSI can be determined by different methodologies being the most used: i) baseline empirical approach and ii) natural and artificial wet and dry reference surfaces. In this study both methodologies were used in the calculation of CWSI. Then, canopy thermal images were used to assess the relationship between grapevine water status and CWSI. Natural references Tdry and Twet surfaces were obtained by covering the leaves with petroleum jelly and spraying soapy water on leaves, respectively. Non-water stressed and non-transpiring baseline equations were developed using canopy temperature measured from full irrigated and stress plants using infrared thermometers placed on top of grapevines. Stem water potential (Ψstem) was determined with a pressure chamber on 32 grapevines in a 'Carmenere' commercial vineyard. Thermal image acquisitions and Ψstem measures were obtained simultaneously at midday. Comparison between natural references and baseline approaches for estimating stem water potential showed that both methods presented high and significant correlations with determination confidents (r2) over 0.78. According to these results, both CWSI approaches, can be accepted as a useful tool to schedule irrigations in grapevines. However, for practical purposes, the use of natural references is more suitable since it does not need field experiments to determine the empirical baseline.

Land Surface Temperature (LST) is an important indicator of environment changes, especially related drought monitoring. It is necessary to accurately detect drought events using advanced technology proved information regarding the drought areas. Remote sensing images have proven to be efficient in detecting drought events. MODIS Terra and Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat 8 OLI/TIRS (The Operational Land Imager and the Thermal Infrared Scanner) represent remote imaging images with different spatial resolutions that enable us proved drought information. However, proper methods are needed to optimize these images for monitoring drought events. The purpose of this study is to find out the ability of multi-scale images proved information about drought monitoring using LST methods. The method used in LST is Temperature Condition Index (TCI), Crop Water Stress Index (CWSI), and Principal Component Analysis (PCA). All three equations are selected because they represent a modification of the method for LST input. The results suggest that the three equations used in multi-level imagery have a critical alignment of information regarding drought. The results show that drought pattern identified by MODIS Terra image was similar to the one detected by Landsat ETM+ and OLI/TIRS images. However, we found a temperature difference in dry season (especially in October) between Landsat ETM+ and OLI/TIRS. The degree of LST estimation accuracy between MODIS Terra and Landsat (ETM+ and OLI/TIRS) is indicated by the average difference between the results of those images, which was 1 degree Celsius (1°C). The use of these three equations for drought monitoring with multi-level imagery suggests that there is a positive relationship. This relationship manifests the same pattern, shape, and association that are produced, thus using a common equation for drought monitoring is more focused.

In this study, the crop water stress index (CWSI) was monitored at a commercial apple orchard in Brandenburg (Germany). In this regard, two methods were applied; an established method based on canopy leaf temperature using infrared thermometers (IRT), followed by calculation of CWSI according to Idso $(CWSI_{IDSO})$. The established method was compared to satellite-based approach $(CWSI_{sat-based})$ using Landsat 8 satellite images. During the IRT measurements, the sampling for estimating vegetation water content (VWC) was carried out. The results showed that the average differences between CWSI estimation methods were less than 3%, indicating good agreement between both methods. There is a negative correlation between VWC and two CWSI, indicating CWSI sat has the potential to replace direct VWC measurements and to provide the tree water status that includes spatial distribution, through mapping of extended areas of the apple orchards, which may more effectively monitor water stress and irrigation scheduling

Automated irrigation is effective in optimizing water use in irrigation. The study presents the application of environmental canopy sensing based Crop Water Stress Index (CWSI) to develop a highly effective sprinkler based irrigation system. Field experiments were utilized for observing plant canopy temperature and soil moisture status. CWSI of 0.35 resulted in maximum water use efficiency and optimum yield of wheat crop. CWSI approach resulted in 10-15% water savings in comparison to soil moisture based irrigation. Based on results of field experiments, an automated approach is proposed, in which the CWSI will be used to trigger the sprinkler irrigation system at critical levels of stresses.

We compared the theoretical crop water stress index (CWSI) averaged over late morning hours (10:00-11:00 AM), (CWSImo) and over solar noon hours, midday (1:00-3:00 PM), (CWSImd). We assessed their sensitivity to changes in soil water content including soil water deficit, (SWD, %) and soil matric potential, (SMP, kPa) at different root zone depths, and to stem water potential (Ψstem). At the effective root zone depth, the linear relationship between SWD and CWSI averaged over morning hours resulted in a reasonably good correlation (R2 = 0.52, p

This study was conducted in 2015 and 2016 to investigate potential use of crop water stress index (CWSI) in irrigation scheduling for drip-irrigated pumpkin seed plants. Six different irrigation levels (I0, I20, I40, I60, I80 and I100) were used. Amount of irrigation water applied to plants varied between 24.0–258.4 mm in 2015 and between 13.0–470.2 mm in 2016. Plant water consumptions (ET) varied between 256–479 mm in 2015 and between 227–628 mm in 2016. The greatest seed yields (1417 kg/ha in 2015 and 1306 kg/ha in 2016) were obtained from I100 treatments and the lowest seed yields (470 kg/ha in 2015 and 427 kg/ha in 2016) were obtained from I0 treatments. In 2015, water use efficiency (WUE) values varied between 0.18 - 0.30 kg/m3 and irrigation water use efficiency (IWUE) values varied between 0.55 – 0.89 kg/m3. In 2016, WUE values varied between 0.19 - 0.24 kg/m3 and IWUE values varied between 0.28 – 0.71 kg/m3. The average lower limit baseline equation for CWSI calculations was identified as Tc-Ta = - 2.3728 × VPD + 4.4254. CWSI values varied between 0.24–1.0 in 2015 and between 0.08 - 0.93 in 2016. CWSI significantly correlated with seed yield, leaf area index (LAI), leaf water potential (LWP), oil and protein content. It was concluded based on present findings that CWSI could reliably be used in irrigation scheduling and yield estimation of pumpkin seed plants.

Aerial thermal remote sensing can provide a means for collecting spatial plant water status data. Many studies have shown their potential in irrigation management but the adaptation of this technology is not straight forward. In this paper, knowledge accumulated in recent years on thermal imagery analysis methodology for water status mapping is summarized aiming at indicating alternatives to calculate the Crop Water Stress Index (CWSI) for commercial scale water status mapping. Based on literature overview, four forms of wet-baselines to calculate CWSI were selected, namely: artificial wet reference surface, two theoretical calculations and a statistical bio-indicator. These baselines were used to calculate CWSI based on multi-temporal aerial thermal images of cotton fields. CWSI based on a statistical bio-indicator and one of the theoretical wet-baselines provided the best correlations. It is argued though that the statistical one is preferable since it includes the plant characteristics and it is farmer-friendly. Based on bio-indicators, leaf water potential maps of three commercial fields were produced on several dates through the season. Water status spatial patterns were not static and the effect of static factors like sandy soil patches also changed through the season. The maps show the importance of in-season variability mapping for rational irrigation management. To improve current variable-rate irrigation, the concept of in-season irrigation management zones (IMZ) based on thermal-images should be considered and integrated with the delineation of static irrigation IMZ.

The definition of irrigation schedules depends on the understanding of the response of key plant traits to different water restriction characteristics with the aim to avoid physiological impairment. In this study, different timings (at tuber initiation and bulking) and intensities (four soil moisture levels) of water restriction were tested in the potato crop. The temporal patterns of mid-morning or maximum, light saturated stomatal conductance ( g s_max ), recovery of net photosynthesis ( A recovery ), stem water potential (Ψ stem ), carbon isotope discrimination in tubers (Δ tuber ), plant water concentration ( PWC ), photochemical reflectance index ( PRI ) and crop water stress index ( CWSI ) were analyzed. Early-severe water restriction caused a drastic yield reduction, with low recovery of physiological responses ( g s_max , Δ tuber , Ψ stem , CWSI , A recovery ) after 15 days of post-restriction irrigation and even a continued reduction of some of them ( PWC , PRI ). It also caused a prolonged g s_max reduction below 0.05 mol H 2 O m −2 s −1 (≈ 5 μmol CO 2 m −2 s −1 of net photosynthesis) suggesting that this value defines a physiological severity threshold in potato, under which a metabolic impairment occurs. CWSI and PRI showed a close linear (R 2 = 0.76) and no linear (natural logarithm function, R 2 = 0.67) relationship with g s_max respectively. In cloudless dry environments, irrigation schedules in potato should aim to avoiding CWSI values higher than 0.4, especially until before of maximum canopy cover establishment. A close relationship between A recovery at maximum stress moment and yield reduction was found. The strong relationship between the measured traits (except PWC and Ψ stem ) and final yield at maximum stress moment found in the present study warrants further research on drought phenotyping immediately before post-restriction irrigation or when the defined severity threshold in potato is reached.

Sun-injury (=sunburn) on apples is an important physiological disorder affecting all cultivars grown in semi-arid climates. Both, high light and elevated temperatures during the fruit-growing season are responsible for the symptoms. Therefore, a crop water stress index (CWSI), calculated from infrared images, and chlorophyll fluorescence were assessed as early predictors, when no symptoms of sun-damage were yet visible. Fifty fruit of each cv. (Royal Gala, Granny Smith, Fuji) were tagged at 50 days after full bloom (DAFB) in different orchards. Infrared images were taken weekly and used for CWSI calculation. A similar number of fruit (Royal Gala, Fuji) were tagged after 80 DAFB for chlorophyll fluorescence measurements carried out every 2 weeks. The proposed CWSI was able to predict the occurrence of severe sunburn symptoms in Granny Smith apples as early as 86 DAFB, 42 days before symptoms were visible. In Fuji, this occurred as early as 122 DAFB, 25 days prior visible symptoms. The threshold value of CWSI required for sunburn development on the fruit was 0.5. The maximum quantum efficiency of photosystem II (Fv/Fm) was also able to detect early sun damage, at 100 DAFB, before symptoms appearance, with a threshold value of 0.6. The results would imply that early events in photooxidative stress leading to sun injury have a profound effect on tissue physiology and biochemistry, which can be assessed using chlorophyll fluorescence and a thermography-based index, such as the CWSI. Both, the CWSI and Fv/Fm ratio could be used as non-destructive, fast and reliably field tools to monitor sun-injury when no symptoms have yet developed.

The crop water stress index (CWSI) is usually used to normalize crop temperature for efficient irrigation management. For CWSI computation, the temperature of a non-transpiring crop (Tdry) and that of a crop transpiring at its potential rate (Twet) are required. Alternatively, the use of artificial reference surfaces (ARS) that mimic Tdry and Twet has been proposed. However, to date, little effort has been made on the development of such surfaces, and there is still much uncertainty concerning what is measured. The aim of this research was to design, develop and evaluate the feasibility of new artificial reference surfaces for real time CWSI computation in maize. Hemispherical cellulose paper-based surfaces were constructed using three shades of green. The paper-based hemispheric surfaces were placed in a 3D-printed plastic structure that allows water storage and the placement of an upward-looking infrared thermometer at the bottom of the ARS. An experimental array comprising 18 randomly located ARS was designed, of which 9 were wet ARS (3 replicates per colour) and 9 were dry ARS (3 replicates per colour). The prototypic ARS were able to accurately determine Tdry and Twet in maize canopies. The CWSI values determined using the ARS developed in this research were validated in a commercial maize breeding trial against leaf gas exchange variables. The shade of green that provided the best results in both cases was the RGB colour R32, G66, B34. While Tdry could be obtained directly from a dry ARS temperature, a solar radiation-based transformation is required to determine Twet from the wet ARS temperature.

Droughts occurring more and more frequently in recent years are usually associated with progressive climate changes. Modern monitoring methods is mainly associated with agricultural use due to the high market demand for services protecting against agricultural losses. In natural wetland ecosystem this problem is neglected which is probably due to fact that this are ecosystems rich in water by definition. Nevertheless this droughts might become a problem of wetlands, whose good condition depends on adequate hydration. Therefore this study focused on determination Crop Water Stress Index (CWSI) for natural heterogeneous wetland habitats. CWSI based on remotely sensed land surface temperature and associated basic meteorological (air temperature and humidity) measurements. CWSI was calculated based on high resolution LST measurements from UAV platform and compare with on ground measurements of soil moisture, CCI, fAPAR and meteorological conditions. Results shows that CWSI properly shows water condition of plants in measured habitats and revels high importance of metrological conditions during data acquisition.

This work focuses on understanding the effects caused by the within-tree structural heterogeneity on the Crop Water Stress Index (CWSI). We present an assessment of the CWSI variability and its relationship with stomatal conductance (Gs) using different automatic object-based tree-crown detection algorithms based on temperature quartile thresholds. The study was carried out in an almond orchard cultivated under three irrigated regimes. High-resolution (25 cm) thermal imagery was acquired by an aircraft on summer 2015. The tree crowns were segmented into 4 classes using the 25th, 50th and 75th percentiles via automatic object-based methods. Results showed that CWSI was linearly and inversely correlated with Gs in all thermal classes. However, the relationship with Gs was heavily affected by the crown segmentation levels applied, and improved remarkably when CWSI values where those corresponding to the coldest and purest vegetation pixels (r2 =0.78 from pure vegetation pixels vs. r2=0.52 when warmer pixels were used).

This chapter outlines the potential of thermal sensing as a tool for plant ecophysiological studies and provides a summary of the key biophysical equations involved in the use of thermal sensing for the study of plant water relations. Particular emphasis is placed on the precautions that need to be adopted for high precision applications. The use of reference ‘mimic’ surfaces for improved estimation of stomatal conductance and evapotranspiration is outlined, and some of the precautions necessary for accurate work are described. Not only are recent applications reviewed, but some additional opportunities for use of the technique are described.

Deficit irrigation is a common practice to cope with limited water availability. A two-year field experiment was conducted in order to determine the water status of cotton crop (Gossypium hirsutum L.) under full and deficit irrigation conditions. The experiment was set up as a split plot design with four replicates, two main plots (irrigation treatments) and two sub plots (cotton cultivars). The lower (non-stressed) and upper (fully stressed) base lines for the determination of Crop Water Stress Index (CWSI) of cotton crop were estimated. Plant water status, expressed in terms of the water potential index (WPI) and CWSI, was measured throughout the growing seasons. The adopted irrigation schemes produced a clear differentiation among cultivars concerning their plant water status. Water shortage resulted in more negative leaf water potential and WPI and greater CWSI values. Leaf water potential, WPI and CWSI values, variation throughout both growing seasons, indicated greater adaptability for “Julia” in water stress conditions, while “Zoi” showed a significant water stress behavior during the phases of yield formation and ripening, even for the full irrigated treatment. In general, use of limited drip irrigation regime with 50% of water requirement had significant benefits in terms of saved irrigation water.

Remotely sensing plant canopy water status remains a long term goal of remote sensing research. Established approaches to estimating canopy water status — the Crop Water Stress Index, the Water Deficit Index and the Equivalent Water Thickness — involve measurements in the thermal or reflective infrared. Here we report plant water status estimates based upon analysis of polarized visible imagery of a cotton canopy measured by ground Multi-Spectral Polarization Imager (MSPI). Such estimators potentially provide access to the plant hydrological photochemistry that manifests scattering and absorption effects in the visible spectral region.

A study was conducted in a large pistachio farm in Madera County, California, to assess the spatial variability in water status and irrigation needs by using high-resolution thermal imagery acquired by an unmanned aerial system. We determined the Crop Water Stress Index (CWSI) of two fields, 130 ha each, based on canopy temperature measurements of individual tree crowns, thus assessing the spatial variations in tree water status within each field. The CWSI of each potential management unit (sectors encompassing about 175 trees) was then calculated and related to the days since last irrigation (DSLI) in F1 and F2. The relationship between CWSI and DSLI was established to calculate the average CWSI corresponding to the whole area that was irrigated on the same day. This value was afterward compared with the actual CWSI value of each management unit as a proxy of the spatial variability in CWSI. This information was used to calculate the deviation of each irrigation unit from the fixed irrigation schedule for the whole fields. Our results show that it is feasible to use high-resolution thermal imagery for integrating the crop response in irrigation performance assessment and for providing recommendations at the farm scale., Chris Wylie and Richard Paslay, from Agri-World Cooperative, are also acknowledged. This work was funded by the Spanish Ministry of Science and Innovation (CONSOLIDER CSD2006-0067 and AGL2009-13105).

The thermal infrared bands of Landsat 7 ETM+ and Landsat 8 OLI/TIRS have ability to provide Land Surface Temperature (LST) information. This information is important to map and monitor Earth surface’s temperature-related events such as drought. However, these two Landsat sensors have different conversion methods of deriving LST information from their image pixels. Landsat 7 ETM+ applies a simple algorithm using single thermal band to produce an index of surface temperature; while Landsat 8 TIRS applies two thermal bands and a more sophisticated Split Windows Algorithm (SWA). This research aims to compare and contrast the ability of Landsat 7 ETM+ and Landsat 8 OLI/TIRS to map LST for drought monitoring in part of East Java Province, Indonesia. Drought detection was conducted using two different algorithms, namely Temperature Condition Index (TCI) with a range of values from 0-100 and Crop Water Stress Index (CWSI) with a range of 0-1. These algorithms have an opposite index value explanation; zero in TCI indicates drought condition, but wet condition in CWSI. The differences in the LST results from Landsat images showed that SWA method provides more similar results to the corresponding land cover conditions as compared to the result of Landsat 7 ETM+. Drought detection results demonstrate that TCI and CWSI would be optimally useful for area have a potential drought. Both methods have similar results in terms of the condition of Land Surface Temperatures.