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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.

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.

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).

Irrigation scheduling requires an operational means to quantify plant water stress. Remote sensing may offer quick measurements with regional coverage that cannot be achieved by current ground-based sampling techniques. This study explored the relation between variability in fine-resolution measurements of canopy temperature and crop water stress in cotton fields in Central Arizona, USA. By using both measurements and simulation models, this analysis compared the standard deviation of the canopy temperature $$ {\left( {\sigma _{{T_{{\text{c}}} }} } \right)} $$ to the more complex and data intensive crop water stress index (CWSI). For low water stress, field $$ \sigma _{{T_{{\text{c}}} }} $$ was used to quantify water deficit with some confidence. For moderately stressed crops, the $$ \sigma _{{T_{{\text{c}}} }} $$ was very sensitive to variations in plant water stress and had a linear relation with field-scale CWSI. For highly stressed crops, the estimation of water stress from $$ \sigma _{{T_{{\text{c}}} }} $$ is not recommended. For all applications of $$ \sigma _{{T_{{\text{c}}} }} , $$ one must account for variations in irrigation uniformity, field root zone water holding capacity, meteorological conditions and spatial resolution of T c data. These sensitivities limit the operational application of $$ \sigma _{{T_{{\text{c}}} }} $$ for irrigation scheduling. On the other hand, $$ \sigma _{{T_{{\text{c}}} }} $$ was most sensitive to water stress in the range in which most irrigation decisions are made, thus, with some consideration of daily meteorological conditions, $$ \sigma _{{T_{{\text{c}}} }} $$ could provide a relative measure of temporal variations in root zone water availability. For large irrigation districts, this may be an economical option for minimizing water use and maximizing crop yield.

This study was conducted to determine the crop water stress index (CWSI) for potato (Solanum tuberosum L.) grown under furrow and drip irrigation methods and subjected to three different irrigation levels (100, 50 and 0% replenishment of soil water depleted). The lower (non-stressed) and upper (stressed) baselines were determined empirically from measurements of canopy temperatures, ambient air temperatures and vapor pressure deficit values. Tuber yield decreased when mean CWSI prior to irrigation exceeded 0.68 in furrow and 0.81 in drip irrigation. The tuber yield was directly correlated with the seasonal CWSI values and the linear equations for furrow and drip irrigation methods, Y = −45.82 CWSI + 50.69 and Y = −52.65 CWSI + 58.44, respectively, can be used for yield prediction.

Surface temperature measured with infrared thermometers is an important tool for irrigation scheduling which has been in practice for some decades. Several indices have been developed to time irrigation events. The most useful is the Crop Water Stress Index (CWSI). Its use, however, relies on a non-water-stressed baseline that, although having a theoretical basis, is to be determined experimentally given the uncertainties related to the surface resistance of the crop. The drawbacks of this procedure, besides the non-transferability of the lines from place to place, are that the surface temperature measurements have always to be made under similar weather conditions. A new definition of a non-water-stressed baseline theoretically based and driven by weather variables that can easily be measured and/or estimated is proposed that allows measurements at any time of the day and whatever the weather conditions, thus simplifying the task of the irrigator.

This study was designed to evaluate the crop water stress index (CWSI) for low-energy precision application (LEPA) irrigated corn (Zea mays L.) grown on slowly-permeable Pullman clay loam soil (fine, mixed, Torrertic Paleustoll) during the 1992 growing season at Bushland, Tex. The effects of six different irrigation levels (100%, 80%, 60%, 40%, 20%, and 0% replenishment of soil water depleted from the 1.5-m soil profile depth) on corn yields and the resulting CWSI were investigated. Irrigations were applied in 25 mm increments to maintain the soil water in the 100% treatment within 60-80% of the "plant extractable soil water" using LEPA technology, which wets alternate furrows only. The 1992 growing season was slightly wetter than normal. Thus, irrigation water use was less than normal, but the corn dry matter and grain yield were still significantly increased by irrigation. The yield, water use, and water use efficiency of fully irrigated corn were 1.246 kg/m2, 786 mm, and 1.34 kg/m3, respectively. CWSI was calculated from measurements of infrared canopy temperatures, ambient air temperatures, and vapor pressure deficit values for the six irrigation levels. A "non-water-stressed baseline" equation for corn was developed using the diurnal infrared canopy temperature measurements as Tc-Ta = 1.06-2.56 VPD, where Tc was the canopy temperature (°C), Ta was the air temperature (°C) and VPD was the vapor pressure deficit (kPa). Trends in CWSI values were consistent with the soil water contents induced by the deficit irrigations. Both the dry matter and grain yields decreased with increased soil water deficit. Minimal yield reductions were observed at a threshold CWSI value of 0.33 or less for corn. The CWSI was useful for evaluating crop water stress in corn and should be a valuable tool to assist irrigation decision making together with soil water measurements and/or evapotranspiration models.

Hand-held infrared radiometers, developed during the past decade, have extended the measurement of plant canopy temperatures from individual leaves to entire plant canopies. Canopy temperatures are determined by the water status of the plants and by ambient meteorological conditions. The crop water stress index (CWSI) combines these factors and yields a measure of plant water stress. Two forms of the index have been proposed, an empirical approach as reported by Idso et al. (1981), and a theoretical approach reported by Jackson et al. (1981). Because it is simple and requires only three variables to be measured, the empirical approach has received much attention in the literature. It has, however received some criticism concerning its inability to account for temperature changes due to radiation and windspeed. The theoretical method is more complicated in that it requires these two additional variables to be measured, and the evaluation of an aerodynamic resistance, but it will account for differences in radiation and windspeed. This report reexamines the theoretical approach and proposes a method for estimating an aerodynamic resistance applicable to a plant canopy. A brief history of plant temperature measurements is given and the theoretical basis for the CWSI reviewed.

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.